Python Interface¶

acados_template is a Python package that can be used to specify optimal control problems from Python and to generate self-contained C code to solve them using acados. The pip package is based on templated code (C files, Header files and Makefiles), which are rendered from Python using the templating engine Tera. The genereated C code can be compiled into a self-contained C library that can be deployed on an embedded system.

One can interact with the generated solver using the Python wrapper. There is a ctypes based wrapper which is the default and a cython based wrapper which allows for faster interaction with the C code, to allow deployment of the acados solver in a Python framework with less overhead.

Optimal Control Problem description¶

The Python interface relies on the same problem formulation as the MATLAB interface see here. Currently, Python >= 3.8 is required.

Installation¶

Compile and install acados by following the CMake installation instructions.
Optional: Recommended. Create a Python virtual environment using virtualenv.
```
virtualenv env --python=/usr/bin/python3
# Source the environment
source env/bin/activate
```
Note: There are known path issues with more high level virtual environment managers. Such as conda, miniconda, Pycharm. It is not recommended to use them. However, if you need to do so and have issues, please have a look in the acados forum.
Install acados_template Python package:
```
pip install -e <acados_root>/interfaces/acados_template
```
Note: The option -e makes the installation editable, so you can seamlessly switch to a later acados version and make changes in the Python interface yourself.
Add the path to the compiled shared libraries libacados.so, libblasfeo.so, libhpipm.so to LD_LIBRARY_PATH (default path is <acados_root/lib>) by running:
```
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:"<acados_root>/lib"
export ACADOS_SOURCE_DIR="<acados_root>"
```
NOTE: On MacOS DYLD_LIBRARY_PATH should be used instead of LD_LIBRARY_PATH. Hint: you can add these lines to your .bashrc/.zshrc.
Run a Python example to check that everything works. We suggest to get started with the example <acados_root>/examples/acados_python/getting_started/minimal_example_ocp.py. The example has to be run from the folder in which it is located.
Optional: Can be done automatically through the interface: In order to be able to successfully render C code templates, you need to download the t_renderer binaries for your platform from https://github.com/acados/tera_renderer/releases/ and place them in <acados_root>/bin (please strip the version and platform from the binaries (e.g.t_renderer-v0.0.34 -> t_renderer). Notice that you might need to make t_renderer executable. Run export ACADOS_SOURCE_DIR=<acados_root> such that the location of acados will be known to the Python package at run time.
Optional: Set acados_lib_path, acados_include_path. If you want the generated Makefile to refer to a specific path (e.g. when cross-compiling or compiling from a location different from the one where you generate the C code), you will have to set these paths accordingly in the generating Python code.

Windows 10+ (WSL) and VSCode¶

Virtual Environment¶

Follow the installation instructions above, using a virtual environment.

Connect to VS-Code¶

Install VS-Code on Windows
Install the Remote Development Extension.
Inside the WSL Ubuntu shell run

code .

to start VS-Code in the current folder.

Select the created virtual environment as Python interpreter.

Windows¶

The Python interface of acados can run natively under Windows using the CMake process. Take a look at the CMake installation instructions for Workflow with Microsoft Visual C Compiler (MSVC). We suggest to get started with the example <acados_root>/examples/acados_python/getting_started/minimal_example_ocp_cmake.py. It uses CMake instead of Make by providing an instance of the CMakeBuilder class to the constructor of AcadosOcpSolver or AcadosSimSolver. Depending on your Visual Studio Version the CMake generator might have to be adapted (see CMakeBuilder.generator).

Overview¶

The following image shows an overview of the available classes in the acados Python interface and their dependencies.

Python API classes overview¶

acados OCP solver interface – `AcadosOcp` and `AcadosOcpSolver`¶

The class AcadosOcp can be used to formulate optimal control problems (OCP), for which an acados solver (AcadosOcpSolver) can be created.

The interface to interact with the acados OCP solver in C is based on ctypes.

Additionally, there is the option to use a Cython based wrapper around the C part. This offers faster interaction with the solver, because getter and setter calls, which include shape checking are done in compiled C code. The cython based wrapper is called AcadosOcpSolverCython and was added in PR #791. AcadosOcpSolverCython and AcadosOcpSolver offer the same functionality and equivalent behavior is ensured in CI tests.

class acados_template.acados_ocp_solver.AcadosOcpSolver(acados_ocp: acados_template.acados_ocp.AcadosOcp, json_file='acados_ocp_nlp.json', simulink_opts=None, build=True, generate=True, cmake_builder: acados_template.builders.CMakeBuilder = None, verbose=True)¶

Class to interact with the acados ocp solver C object.

param acados_ocp

type AcadosOcp - description of the OCP for acados

param json_file

name for the json file used to render the templated code - default: acados_ocp_nlp.json

param simulink_opts

Options to configure Simulink S-function blocks, mainly to activate possible Inputs and Outputs

classmethod build(code_export_dir, with_cython=False, cmake_builder: acados_template.builders.CMakeBuilder = None, verbose: bool = True)¶

Builds the code for an acados OCP solver, that has been generated in code_export_dir

param code_export_dir: directory in which acados OCP solver has been generated, see generate()
param with_cython: option indicating if the cython interface is build, default: False.
param cmake_builder: type CMakeBuilder generate a CMakeLists.txt and use the CMake pipeline instead of a Makefile (CMake seems to be the better option in conjunction with MS Visual Studio); default: None
param verbose: indicating if build command is printed

constraints_set(stage_, field_, value_, api='warn')¶

Set numerical data in the constraint module of the solver.

param stage

integer corresponding to shooting node

param field

string in [‘lbx’, ‘ubx’, ‘lbu’, ‘ubu’, ‘lg’, ‘ug’, ‘lh’, ‘uh’, ‘uphi’, ‘C’, ‘D’]

param value

of appropriate size

cost_set(stage_, field_, value_, api='warn')¶

Set numerical data in the cost module of the solver.

param stage

integer corresponding to shooting node

param field

string, e.g. ‘yref’, ‘W’, ‘ext_cost_num_hess’, ‘zl’, ‘zu’, ‘Zl’, ‘Zu’

param value

of appropriate size

classmethod create_cython_solver(json_file)¶

Returns an AcadosOcpSolverCython object.

This is an alternative Cython based Python wrapper to the acados OCP solver in C. This offers faster interaction with the solver, because getter and setter calls, which include shape checking are done in compiled C code.

The default wrapper AcadosOcpSolver is based on ctypes.

custom_update(data_: numpy.ndarray)¶: A custom function that can be implemented by a user to be called between solver calls. By default this does nothing. The idea is to have a convenient wrapper to do complex updates of parameters and numerical data efficiently in C, in a function that is compiled into the solver library and can be conveniently used in the Python environment.

dump_last_qp_to_json(filename: str = '', overwrite=False)¶

Dumps the latest QP data into a json file

param filename

if not set, use model_name + timestamp + ‘.json’

param overwrite

if false and filename exists add timestamp to filename

eval_param_sens(index: int, stage: int = 0, field='ex')¶

Calculate the sensitivity of the current solution with respect to the initial state component of index.

NOTE: Correct computation of sensitivities requires

HPIPM as QP solver,
the usage of an exact Hessian,
positive definiteness of the full-space Hessian if the square-root version of the Riccati recursion is used OR positive definiteness of the reduced Hessian if the classic Riccati recursion is used (compare: solver_options.qp_solver_ric_alg),
the solution of at least one QP in advance to evaluation of the sensitivities as the factorization is reused.

param index

integer corresponding to initial state index in range(nx)

classmethod generate(acados_ocp: acados_template.acados_ocp.AcadosOcp, json_file='acados_ocp_nlp.json', simulink_opts=None, cmake_builder: acados_template.builders.CMakeBuilder = None)¶

Generates the code for an acados OCP solver, given the description in acados_ocp.

param acados_ocp: type AcadosOcp - description of the OCP for acados
param json_file: name for the json file used to render the templated code - default: acados_ocp_nlp.json
param simulink_opts: Options to configure Simulink S-function blocks, mainly to activate possible inputs and outputs; default: None
param cmake_builder: type CMakeBuilder generate a CMakeLists.txt and use the CMake pipeline instead of a Makefile (CMake seems to be the better option in conjunction with MS Visual Studio); default: None

get(stage_, field_)¶

Get the last solution of the solver:

param stage

integer corresponding to shooting node

param field

string in [‘x’, ‘u’, ‘z’, ‘pi’, ‘lam’, ‘t’, ‘sl’, ‘su’,]

Note

regarding lam, t:

the inequalities are internally organized in the following order:

[ lbu lbx lg lh lphi ubu ubx ug uh uphi;

lsbu lsbx lsg lsh lsphi usbu usbx usg ush usphi]

Note

pi: multipliers for dynamics equality constraints

lam: multipliers for inequalities

t: slack variables corresponding to evaluation of all inequalities (at the solution)

sl: slack variables of soft lower inequality constraints

su: slack variables of soft upper inequality constraints

get_cost()¶: Returns the cost value of the current solution.

get_from_qp_in(stage_: int, field_: str)¶

Get numerical data from the current QP.

param stage

integer corresponding to shooting node

param field

string in [‘A’, ‘B’, ‘b’, ‘Q’, ‘R’, ‘S’, ‘q’, ‘r’, ‘C’, ‘D’, ‘lg’, ‘ug’, ‘lbx’, ‘ubx’, ‘lbu’, ‘ubu’]

Note: additional supported fields are [‘P’, ‘K’, ‘Lr’], which can be extracted form QP solver PARTIAL_CONDENSING_HPIPM.

get_hessian_block(stage: int) → numpy.ndarray¶: Get Hessian block from last QP at stage i In HPIPM form [[R, S^T],

[S, Q]]

get_optimal_value_gradient()¶: Returns the gradient of the optimal value function w.r.t. the current initial state. Disclaimer: This function only returns reasonable values if the solver has converged for the current problem instance.

get_residuals(recompute=False)¶

Returns an array of the form [res_stat, res_eq, res_ineq, res_comp]. This residual has to be computed for SQP_RTI solver, since it is not available by default.

res_stat: stationarity residual
res_eq: residual wrt equality constraints (dynamics)
res_ineq: residual wrt inequality constraints (constraints)
res_comp: residual wrt complementarity conditions

get_stats(field_: str) → Union[int, float, numpy.ndarray]¶

Get the information of the last solver call.

param field

string in [‘statistics’, ‘time_tot’, ‘time_lin’, ‘time_sim’, ‘time_sim_ad’, ‘time_sim_la’, ‘time_qp’, ‘time_qp_solver_call’, ‘time_reg’, ‘sqp_iter’, ‘residuals’, ‘qp_iter’, ‘alpha’]

Available fileds:

time_tot: total CPU time previous call
time_lin: CPU time for linearization
time_sim: CPU time for integrator
time_sim_ad: CPU time for integrator contribution of external function calls
time_sim_la: CPU time for integrator contribution of linear algebra
time_qp: CPU time qp solution
time_qp_solver_call: CPU time inside qp solver (without converting the QP)
time_qp_xcond: time_glob: CPU time globalization
time_solution_sensitivities: CPU time for previous call to eval_param_sens
time_reg: CPU time regularization
sqp_iter: number of SQP iterations
qp_stat: status of QP solver
qp_iter: vector of QP iterations for last SQP call
statistics: table with info about last iteration
stat_m: number of rows in statistics matrix
stat_n: number of columns in statistics matrix
residuals: residuals of last iterate
alpha: step sizes of SQP iterations

load_iterate(filename: str, verbose: bool = True)¶: Loads the iterate stored in json file with filename into the ocp solver. Note: This does not contain the iterate of the integrators, and the parameters.

options_set(field_, value_)¶

Set options of the solver.

param field

string, e.g. ‘print_level’, ‘rti_phase’, ‘initialize_t_slacks’, ‘step_length’, ‘alpha_min’, ‘alpha_reduction’, ‘qp_warm_start’, ‘line_search_use_sufficient_descent’, ‘full_step_dual’, ‘globalization_use_SOC’, ‘qp_tol_stat’, ‘qp_tol_eq’, ‘qp_tol_ineq’, ‘qp_tol_comp’, ‘qp_tau_min’, ‘qp_mu0’

param value

of type int, float, string

qp_tol_stat: QP solver tolerance stationarity

qp_tol_eq: QP solver tolerance equalities

qp_tol_ineq: QP solver tolerance inequalities

qp_tol_comp: QP solver tolerance complementarity

qp_tau_min: for HPIPM QP solvers: minimum value of barrier parameter in HPIPM

qp_mu0: for HPIPM QP solvers: initial value for complementarity slackness

warm_start_first_qp: indicates if first QP in SQP is warm_started

print_statistics()¶

prints statistics of previous solver run as a table:

iter: iteration number
res_stat: stationarity residual
res_eq: residual wrt equality constraints (dynamics)
res_ineq: residual wrt inequality constraints (constraints)
res_comp: residual wrt complementarity conditions
qp_stat: status of QP solver
qp_iter: number of QP iterations
alpha: SQP step size
qp_res_stat: stationarity residual of the last QP solution
qp_res_eq: residual wrt equality constraints (dynamics) of the last QP solution
qp_res_ineq: residual wrt inequality constraints (constraints) of the last QP solution
qp_res_comp: residual wrt complementarity conditions of the last QP solution

reset(reset_qp_solver_mem=1)¶: Sets current iterate to all zeros.

set(stage_, field_, value_)¶

Set numerical data inside the solver.

param stage

integer corresponding to shooting node

param field

string in [‘x’, ‘u’, ‘pi’, ‘lam’, ‘t’, ‘p’, ‘xdot_guess’, ‘z_guess’]

Note

regarding lam, t:

the inequalities are internally organized in the following order:

[ lbu lbx lg lh lphi ubu ubx ug uh uphi;

lsbu lsbx lsg lsh lsphi usbu usbx usg ush usphi]

Note

pi: multipliers for dynamics equality constraints

lam: multipliers for inequalities

t: slack variables corresponding to evaluation of all inequalities (at the solution)

sl: slack variables of soft lower inequality constraints

su: slack variables of soft upper inequality constraints

set_new_time_steps(new_time_steps)¶

Set new time steps. Recreates the solver if N changes.

param new_time_steps

1 dimensional np array of new time steps for the solver

Note

This allows for different use-cases: either set a new size of time_steps or a new distribution of the shooting nodes without changing the number, e.g., to reach a different final time. Both cases do not require a new code export and compilation.

set_params_sparse(stage_, idx_values_, param_values_)¶

set parameters of the solvers external function partially: Pseudo: solver.param[idx_values_] = param_values_; Parameters:

param stage_

integer corresponding to shooting node

param idx_values_

0 based np array (or iterable) of integers: indices of parameter to be set

param param_values_

new parameter values as numpy array

solve()¶: Solve the ocp with current input.

solve_for_x0(x0_bar, fail_on_nonzero_status=True, print_stats_on_failure=True)¶: Wrapper around solve() which sets initial state constraint, solves the OCP, and returns u0.

store_iterate(filename: str = '', overwrite=False, verbose=True)¶

Stores the current iterate of the ocp solver in a json file. Note: This does not contain the iterate of the integrators, and the parameters.

param filename

if not set, use f’{self.model_name}_iterate.json’

param overwrite

if false and filename exists add timestamp to filename

update_qp_solver_cond_N(qp_solver_cond_N: int)¶

Recreate solver with new value qp_solver_cond_N with a partial condensing QP solver. This function is relevant for code reuse, i.e., if either set_new_time_steps(…) is used or the influence of a different qp_solver_cond_N is studied without code export and compilation.

param qp_solver_cond_N

new number of condensing stages for the solver

Note

This function can only be used in combination with a partial condensing QP solver.

Note

After set_new_time_steps(…) is used and depending on the new number of time steps it might be necessary to change qp_solver_cond_N as well (using this function), i.e., typically qp_solver_cond_N < N.

acados_template.acados_ocp_solver.__ocp_get_template_list(acados_ocp: acados_template.acados_ocp.AcadosOcp, cmake_builder=None, simulink_opts=None) → list¶: returns a list of tuples in the form: (input_filename, output_filname) or (input_filename, output_filname, output_directory)

acados_template.acados_ocp_solver.ocp_get_default_cmake_builder() → acados_template.builders.CMakeBuilder ¶: If AcadosOcpSolver is used with CMake this function returns a good first setting. :return: default CMakeBuilder

class acados_template.acados_ocp.AcadosOcp(acados_path='')¶

Class containing the full description of the optimal control problem. This object can be used to create an acados_template.acados_ocp_solver.AcadosOcpSolver.

The class has the following properties that can be modified to formulate a specific OCP, see below:

dims of type acados_template.acados_ocp.AcadosOcpDims

model of type acados_template.acados_model.AcadosModel

cost of type acados_template.acados_ocp.AcadosOcpCost

constraints of type acados_template.acados_ocp.AcadosOcpConstraints

solver_options of type acados_template.acados_ocp.AcadosOcpOptions

acados_include_path (set automatically)

shared_lib_ext (set automatically)

acados_lib_path (set automatically)

parameter_values - used to initialize the parameters (can be changed)

acados_include_path¶: Path to acados include directory (set automatically), type: string

acados_lib_path¶: Path to where acados library is located, type: string

code_export_directory¶: Path to where code will be exported. Default: c_generated_code.

constraints¶: Constraints definitions, type acados_template.acados_ocp.AcadosOcpConstraints

cost¶: Cost definitions, type acados_template.acados_ocp.AcadosOcpCost

dims¶: Dimension definitions, type acados_template.acados_ocp.AcadosOcpDims

formulate_constraint_as_Huber_penalty(constr_expr: casadi.casadi.SX, weight: float, upper_bound: Optional[float, None], lower_bound: Optional[float, None], residual_name: str = 'new_residual', huber_delta: float = 1.0, use_xgn=True, min_hess=0) → None¶

Formulate a constraint as Huber penalty and add it to the current cost.

use_xgn: if true an XGN Hessian is used, if false a GGN Hessian (= exact Hessian, in this case) is used. min_hess: provide a minimum value for the hessian

formulate_constraint_as_L2_penalty(constr_expr: casadi.casadi.SX, weight: float, upper_bound: Optional[float, None], lower_bound: Optional[float, None], residual_name: str = 'new_residual') → None¶: Formulate a constraint as an L2 penalty and add it to the current cost.

model¶: Model definitions, type acados_template.acados_model.AcadosModel

property parameter_values¶: $p$ - initial values for parameter - can be updated stagewise

solver_options¶: Solver Options, type acados_template.acados_ocp.AcadosOcpOptions

translate_nls_cost_to_conl()¶: Translates a NONLINEAR_LS cost to a CONVEX_OVER_NONLINEAR cost.

zoro_description¶: zoRO - zero order robust optimization - description: for advanced users.

class acados_template.acados_ocp.AcadosOcpConstraints¶

class containing the description of the constraints

property C¶: $C$ - C matrix in $\underline{g} \leq D \, u + C \, x \leq \bar{g}$ at shooting nodes (0 to N-1). Type: np.ndarray; default: np.array((0,0)).

property C_e¶: $C^e$ - C matrix at terminal shooting node N. Type: np.ndarray; default: np.array((0,0)).

property D¶: $D$ - D matrix in $\underline{g} \leq D \, u + C \, x \leq \bar{g}$ at shooting nodes (0 to N-1). Type: np.ndarray; default: np.array((0,0))

property Jbu¶: $J_{bu}$ - matrix coefficient for bounds on u at shooting nodes (0 to N-1). Translated internally to idxbu.

property Jbx¶: $J_{bx}$ - matrix coefficient for bounds on x at intermediate shooting nodes (1 to N-1). Translated internally into idxbx.

property Jbx_0¶: $J_{bx,0}$ - matrix coefficient for bounds on x at initial stage 0. Translated internally to idxbx_0

property Jbx_e¶: $J_{bx}^e$ matrix coefficient for bounds on x at terminal shooting node N. Translated internally into idxbx_e.

property Jsbu¶: $J_{sbu}$ - matrix coefficient for soft bounds on u at stages (0 to N-1); internally translated into idxsbu

property Jsbx¶: $J_{sbx}$ - matrix coefficient for soft bounds on x at stages (1 to N-1); Translated internally into idxsbx.

property Jsbx_e¶: $J_{sbx}^e$ - matrix coefficient for soft bounds on x at terminal shooting node N. Translated internally to idxsbx_e

property Jsg¶: $J_{sg}$ - matrix coefficient for soft bounds on general linear constraints. Translated internally to idxsg

property Jsg_e¶: $J_{s,h}^e$ - matrix coefficient for soft bounds on general linear constraints at terminal shooting node N. Translated internally to idxsg_e

property Jsh¶: $J_{sh}$ - matrix coefficient for soft bounds on nonlinear constraints. Translated internally to idxsh

property Jsh_0¶: $J_{s,h}^0$ - matrix coefficient for soft bounds on nonlinear constraints at initial shooting node 0; fills idxsh_0

property Jsh_e¶: $J_{s,h}^e$ - matrix coefficient for soft bounds on nonlinear constraints at terminal shooting node N; fills idxsh_e

property Jsphi¶: $J_{s, \phi}$ - matrix coefficient for soft bounds on convex-over-nonlinear constraints. Translated internally into idxsphi.

property Jsphi_0¶: $J_{sh}^0$ - matrix coefficient for soft bounds on convex-over-nonlinear constraints at shooting node N. Translated internally to idxsphi_0

property Jsphi_e¶: $J_{sh}^e$ - matrix coefficient for soft bounds on convex-over-nonlinear constraints at shooting node N. Translated internally to idxsphi_e

property constr_type¶: Constraints type for shooting nodes (0 to N-1). string in {BGH, BGP}. Default: BGH; BGP is for convex over nonlinear.

property constr_type_0¶: Constraints type for initial shooting node. string in {BGH, BGP}. Default: BGH; BGP is for convex over nonlinear.

property constr_type_e¶: Constraints type for terminal shooting node N. string in {BGH, BGP}. Default: BGH; BGP is for convex over nonlinear.

property idxbu¶: Indices of bounds on u (defines $J_{bu}$) at shooting nodes (0 to N-1). Can be set by using Jbu. Type: np.ndarray; default: np.array([])

property idxbx¶: indices of bounds on x (defines $J_{bx}$) at intermediate shooting nodes (1 to N-1). Can be set by using Jbx. Type: np.ndarray; default: np.array([])

property idxbx_0¶: Indices of bounds on x at initial stage 0 – can be set automatically via x0. Can be set by using Jbx_0. Type: np.ndarray; default: np.array([])

property idxbx_e¶: Indices for bounds on x at terminal shooting node N (defines $J_{bx}^e$). Can be set by using Jbx_e. Type: np.ndarray; default: np.array([])

property idxbxe_0¶: Indices of bounds on x0 that are equalities – can be set automatically via x0. Type: np.ndarray; default: np.array([])

property idxsbu¶: Indices of soft bounds on u within the indices of bounds on u at stages (0 to N-1). Can be set by using Jsbu. Type: np.ndarray; default: np.array([])

property idxsbx¶: Indices of soft bounds on x within the indices of bounds on x at stages (1 to N-1). Can be set by using Jsbx. Type: np.ndarray; default: np.array([])

property idxsbx_e¶: Indices of soft bounds on x at shooting node N, within the indices of bounds on x at terminal shooting node N. Can be set by using Jsbx_e. Type: np.ndarray; default: np.array([])

property idxsg¶: Indices of soft general linear constraints within the indices of general linear constraints. Can be set by using Jsg. Type: np.ndarray; default: np.array([])

property idxsg_e¶: Indices of soft general linear constraints at shooting node N within the indices of general linear constraints at shooting node N. Can be set by using Jsg_e.

property idxsh¶: Indices of soft nonlinear constraints within the indices of nonlinear constraints. Can be set by using Jbx. Type: np.ndarray; default: np.array([])

property idxsh_0¶: Indices of soft nonlinear constraints at shooting node N within the indices of nonlinear constraints at initial shooting node 0. Can be set by using Jsh_0.

property idxsh_e¶: Indices of soft nonlinear constraints at shooting node N within the indices of nonlinear constraints at terminal shooting node N. Can be set by using Jsh_e.

property idxsphi¶: Indices of soft convex-over-nonlinear constraints within the indices of nonlinear constraints. Can be set by using Jsphi. Type: np.ndarray; default: np.array([])

property idxsphi_0¶: Indices of soft nonlinear constraints at shooting node N within the indices of nonlinear constraints at initial shooting node 0. Can be set by using Jsphi_0. Type: np.ndarray; default: np.array([])

property idxsphi_e¶: Indices of soft nonlinear constraints at shooting node N within the indices of nonlinear constraints at terminal shooting node N. Can be set by using Jsphi_e. Type: np.ndarray; default: np.array([])

property lbu¶: $\underline{u}$ - lower bounds on u at shooting nodes (0 to N-1). Type: np.ndarray; default: np.array([])

property lbx¶: $\underline{x}$ - lower bounds on x at intermediate shooting nodes (1 to N-1). Type: np.ndarray; default: np.array([])

property lbx_0¶: $\underline{x_0}$ - lower bounds on x at initial stage 0. Type: np.ndarray; default: np.array([]).

property lbx_e¶: $\underline{x}^e$ - lower bounds on x at terminal shooting node N. Type: np.ndarray; default: np.array([])

property lg¶: $\underline{g}$ - lower bound for general polytopic inequalities at shooting nodes (0 to N-1). Type: np.ndarray; default: np.array([])

property lg_e¶: $\underline{g}^e$ - lower bound on general polytopic inequalities at terminal shooting node N. Type: np.ndarray; default: np.array([]).

property lh¶: $\underline{h}$ - lower bound for nonlinear inequalities at shooting nodes (0 to N-1). Type: np.ndarray; default: np.array([]).

property lh_0¶: $\underline{h}^e$ - lower bound on nonlinear inequalities at initial shooting node. Type: np.ndarray; default: np.array([]).

property lh_e¶: $\underline{h}^e$ - lower bound on nonlinear inequalities at terminal shooting node N. Type: np.ndarray; default: np.array([]).

property lphi¶: $\underline{\phi}$ - lower bound for convex-over-nonlinear inequalities at shooting nodes (0 to N-1). Type: np.ndarray; default: np.array([]).

property lphi_0¶: $\underline{\phi}^0$ - lower bound on convex-over-nonlinear inequalities at shooting node 0. Type: np.ndarray; default: np.array([]).

property lphi_e¶: $\underline{\phi}^e$ - lower bound on convex-over-nonlinear inequalities at terminal shooting node N. Type: np.ndarray; default: np.array([]).

property lsbu¶: Lower bounds on slacks corresponding to soft lower bounds on u at stages (0 to N-1). Not required - zeros by default.

property lsbx¶: Lower bounds on slacks corresponding to soft lower bounds on x at stages (1 to N-1); not required - zeros by default

property lsbx_e¶: Lower bounds on slacks corresponding to soft lower bounds on x at shooting node N. Not required - zeros by default

property lsg¶: Lower bounds on slacks corresponding to soft lower bounds for general linear constraints at stages (0 to N-1). Type: np.ndarray; default: np.array([])

property lsg_e¶: Lower bounds on slacks corresponding to soft lower bounds for general linear constraints at shooting node N. Not required - zeros by default

property lsh¶: Lower bounds on slacks corresponding to soft lower bounds for nonlinear constraints. Not required - zeros by default

property lsh_0¶: Lower bounds on slacks corresponding to soft lower bounds for nonlinear constraints at initial shooting node 0. Not required - zeros by default

property lsh_e¶: Lower bounds on slacks corresponding to soft lower bounds for nonlinear constraints at terminal shooting node N. Not required - zeros by default

property lsphi¶: Lower bounds on slacks corresponding to soft lower bounds for convex-over-nonlinear constraints. Not required - zeros by default

property lsphi_0¶: Lower bounds on slacks corresponding to soft lower bounds for convex-over-nonlinear constraints at initial shooting node 0. Not required - zeros by default

property lsphi_e¶: Lower bounds on slacks corresponding to soft lower bounds for convex-over-nonlinear constraints at terminal shooting node N. Not required - zeros by default

property ubu¶: $\bar{u}$ - upper bounds on u at shooting nodes (0 to N-1). Type: np.ndarray; default: np.array([])

property ubx¶: $\bar{x}$ - upper bounds on x at intermediate shooting nodes (1 to N-1). Type: np.ndarray; default: np.array([])

property ubx_0¶: $\bar{x_0}$ - upper bounds on x at initial stage 0. Type: np.ndarray; default: np.array([])

property ubx_e¶: $\bar{x}^e$ - upper bounds on x at terminal shooting node N. Type: np.ndarray; default: np.array([])

property ug¶: $\bar{g}$ - upper bound for general polytopic inequalities at shooting nodes (0 to N-1). Type: np.ndarray; default: np.array([]).

property ug_e¶: $\bar{g}^e$ - upper bound on general polytopic inequalities at terminal shooting node N. Type: np.ndarray; default: np.array([]).

property uh¶: $\bar{h}$ - upper bound for nonlinear inequalities at shooting nodes (0 to N-1). Type: np.ndarray; default: np.array([]).

property uh_0¶: $\bar{h}^e$ - upper bound on nonlinear inequalities at initial shooting node. Type: np.ndarray; default: np.array([]).

property uh_e¶: $\bar{h}^e$ - upper bound on nonlinear inequalities at terminal shooting node N. Type: np.ndarray; default: np.array([]).

property uphi¶: $\bar{\phi}$ - upper bound for convex-over-nonlinear inequalities at shooting nodes (0 to N-1). Type: np.ndarray; default: np.array([]).

property uphi_0¶: $\bar{\phi}^0$ - upper bound on convex-over-nonlinear inequalities at shooting node 0. Type: np.ndarray; default: np.array([]).

property uphi_e¶: $\bar{\phi}^e$ - upper bound on convex-over-nonlinear inequalities at terminal shooting node N. Type: np.ndarray; default: np.array([]).

property usbu¶: Lower bounds on slacks corresponding to soft upper bounds on u at stages (0 to N-1); not required - zeros by default

property usbx¶: Lower bounds on slacks corresponding to soft upper bounds on x at stages (1 to N-1); not required - zeros by default

property usbx_e¶: Lower bounds on slacks corresponding to soft upper bounds on x at shooting node N. Not required - zeros by default

property usg¶: Lower bounds on slacks corresponding to soft upper bounds for general linear constraints. Not required - zeros by default

property usg_e¶: Lower bounds on slacks corresponding to soft upper bounds for general linear constraints at shooting node N. Not required - zeros by default

property ush¶: Lower bounds on slacks corresponding to soft upper bounds for nonlinear constraints. Not required - zeros by default

property ush_0¶: Lower bounds on slacks corresponding to soft upper bounds for nonlinear constraints at initial shooting node 0. Not required - zeros by default

property ush_e¶: Lower bounds on slacks corresponding to soft upper bounds for nonlinear constraints at terminal shooting node N. Not required - zeros by default

property usphi¶: Lower bounds on slacks corresponding to soft upper bounds for convex-over-nonlinear constraints. Not required - zeros by default

property usphi_0¶: Lower bounds on slacks corresponding to soft upper bounds for convex-over-nonlinear constraints at initial shooting node 0. Not required - zeros by default

property usphi_e¶: Lower bounds on slacks corresponding to soft upper bounds for convex-over-nonlinear constraints at terminal shooting node N. Not required - zeros by default

property x0¶: $x_0 \in \mathbb{R}^{n_x}$ - initial state – Translated internally to idxbx_0, lbx_0, ubx_0, idxbxe_0

class acados_template.acados_ocp.AcadosOcpCost¶

Class containing the numerical data of the cost:

NOTE: all cost terms, except for the terminal one are weighted with the corresponding time step. This means given the time steps are $\Delta t_0,..., \Delta t_N$, the total cost is given by: $c_\text{total} = \Delta t_0 \cdot c_0(x_0, u_0, p_0, z_0) + ... + \Delta t_{N-1} \cdot c_{N-1}(x_0, u_0, p_0, z_0) + c_N(x_N, p_N)$.

This means the Lagrange cost term is given in continuous time, this makes up for a seeminglessly OCP discretization with a nonuniform time grid.

In case of LINEAR_LS: stage cost is $l(x,u,z) = || V_x \, x + V_u \, u + V_z \, z - y_\text{ref}||^2_W$, terminal cost is $m(x) = || V^e_x \, x - y_\text{ref}^e||^2_{W^e}$

In case of NONLINEAR_LS: stage cost is $l(x,u,z,p) = || y(x,u,z,p) - y_\text{ref}||^2_W$, terminal cost is $m(x,p) = || y^e(x,p) - y_\text{ref}^e||^2_{W^e}$

In case of CONVEX_OVER_NONLINEAR: stage cost is $l(x,u,p) = \psi(y(x,u,p) - y_\text{ref}, p)$, terminal cost is $m(x, p) = \psi^e (y^e(x,p) - y_\text{ref}^e, p)$

property Vu¶: $V_u$ - u matrix coefficient at intermediate shooting nodes (1 to N-1). Default: np.zeros((0,0)).

property Vu_0¶: $V_u^0$ - u matrix coefficient at initial shooting node (0). Default: None.

property Vx¶: $V_x$ - x matrix coefficient at intermediate shooting nodes (1 to N-1). Default: np.zeros((0,0)).

property Vx_0¶: $V_x^0$ - x matrix coefficient at initial shooting node (0). Default: None.

property Vx_e¶: $V_x^e$ - x matrix coefficient for cost at terminal shooting node (N). Default: np.zeros((0,0)).

property Vz¶: $V_z$ - z matrix coefficient at intermediate shooting nodes (1 to N-1). Default: np.zeros((0,0)).

property Vz_0¶: $V_z^0$ - z matrix coefficient at initial shooting node (0). Default: None.

property W¶: $W$ - weight matrix at intermediate shooting nodes (1 to N-1). Default: np.zeros((0,0)).

property W_0¶: $W_0$ - weight matrix at initial shooting node (0). Default: None.

property W_e¶: $W_e$ - weight matrix at terminal shooting node (N). Default: np.zeros((0,0)).

property Zl¶: $Z_l$ - diagonal of Hessian wrt lower slack at intermediate shooting nodes (0 to N-1). Default: np.array([]).

property Zl_0¶: $Z_l^0$ - diagonal of Hessian wrt lower slack at initial node 0. Default: np.array([]).

property Zl_e¶: $Z_l^e$ - diagonal of Hessian wrt lower slack at terminal shooting node (N). Default: np.array([]).

property Zu¶: $Z_u$ - diagonal of Hessian wrt upper slack at intermediate shooting nodes (0 to N-1). Default: np.array([]).

property Zu_0¶: $Z_u^0$ - diagonal of Hessian wrt upper slack at initial node 0. Default: np.array([]).

property Zu_e¶: $Z_u^e$ - diagonal of Hessian wrt upper slack at terminal shooting node (N). Default: np.array([]).

property cost_ext_fun_type¶: Type of external function for cost at intermediate shooting nodes (1 to N-1). – string in {casadi, generic} Default: :code:’casadi’.

property cost_ext_fun_type_0¶: Type of external function for cost at initial shooting node (0) – string in {casadi, generic} or None Default: :code:’casadi’.

Note

Cost at initial stage is the same as for intermediate shooting nodes if not set differently explicitly.

property cost_ext_fun_type_e¶: Type of external function for cost at terminal shooting node (N). – string in {casadi, generic} Default: :code:’casadi’.

property cost_type¶: Cost type at intermediate shooting nodes (1 to N-1) – string in {EXTERNAL, LINEAR_LS, NONLINEAR_LS, CONVEX_OVER_NONLINEAR}. Default: ‘LINEAR_LS’.

property cost_type_0¶: Cost type at initial shooting node (0) – string in {EXTERNAL, LINEAR_LS, NONLINEAR_LS, CONVEX_OVER_NONLINEAR} or None. Default: None.

Note

Cost at initial stage is the same as for intermediate shooting nodes if not set differently explicitly.

Note

If cost_type_0 is set to None values in W_0, Vx_0, Vu_0, Vz_0 and yref_0 are ignored (set to None).

property cost_type_e¶: Cost type at terminal shooting node (N) – string in {EXTERNAL, LINEAR_LS, NONLINEAR_LS, CONVEX_OVER_NONLINEAR}. Default: ‘LINEAR_LS’.

property yref¶: $y_\text{ref}$ - reference at intermediate shooting nodes (1 to N-1). Default: np.array([]).

property yref_0¶: $y_\text{ref}^0$ - reference at initial shooting node (0). Default: None.

property yref_e¶: $y_\text{ref}^e$ - cost reference at terminal shooting node (N). Default: np.array([]).

property zl¶: $z_l$ - gradient wrt lower slack at intermediate shooting nodes (0 to N-1). Default: np.array([]).

property zl_0¶: $z_l^0$ - gradient wrt lower slack at initial node 0. Default: np.array([]).

property zl_e¶: $z_l^e$ - gradient wrt lower slack at terminal shooting node (N). Default: np.array([]).

property zu¶: $z_u$ - gradient wrt upper slack at intermediate shooting nodes (0 to N-1). Default: np.array([]).

property zu_0¶: $z_u^0$ - gradient wrt upper slack at initial node 0. Default: np.array([]).

property zu_e¶: $z_u^e$ - gradient wrt upper slack at terminal shooting node (N). Default: np.array([]).

class acados_template.acados_ocp.AcadosOcpDims¶

Class containing the dimensions of the optimal control problem.

property N¶: $N$ - prediction horizon. Type: int; default: None

property nbu¶: $n_{b_u}$ - number of input bounds. Type: int; default: 0

property nbx¶: $n_{b_x}$ - number of state bounds. Type: int; default: 0

property nbx_0¶: $n_{b_{x0}}$ - number of state bounds for initial state. Type: int; default: 0

property nbx_e¶: $n_{b_x}$ - number of state bounds at terminal shooting node N. Type: int; default: 0

property nbxe_0¶: $n_{be_{x0}}$ - number of state bounds at initial shooting node that are equalities. Type: int; default: None

property ng¶: $n_{g}$ - number of general polytopic constraints. Type: int; default: 0

property ng_e¶: $n_{g}^e$ - number of general polytopic constraints at terminal shooting node N. Type: int; default: 0

property nh¶: $n_h$ - number of nonlinear constraints. Type: int; default: 0

property nh_0¶: $n_{h}^e$ - number of nonlinear constraints at initial shooting node. Type: int; default: 0

property nh_e¶: $n_{h}^e$ - number of nonlinear constraints at terminal shooting node N. Type: int; default: 0

property np¶: $n_p$ - number of parameters. Type: int; default: 0

property nphi¶: $n_{\phi}$ - number of convex-over-nonlinear constraints. Type: int; default: 0

property nphi_0¶: $n_{\phi}^0$ - number of convex-over-nonlinear constraints at initial shooting node 0. Type: int; default: 0

property nphi_e¶: $n_{\phi}^e$ - number of convex-over-nonlinear constraints at terminal shooting node N. Type: int; default: 0

property nr¶: $n_{\pi}$ - dimension of the image of the inner nonlinear function in positive definite constraints. Type: int; default: 0

property nr_0¶: $n_{\pi}^0$ - dimension of the image of the inner nonlinear function in positive definite constraints. Type: int; default: 0

property nr_e¶: $n_{\pi}^e$ - dimension of the image of the inner nonlinear function in positive definite constraints. Type: int; default: 0

property ns¶: $n_{s}$ - total number of slacks at stages (1, N-1). Type: int; default: 0

property ns_0¶: $n_{s}^0$ - total number of slacks at shooting node 0. Type: int; default: 0

property ns_e¶: $n_{s}^e$ - total number of slacks at terminal shooting node N. Type: int; default: 0

property nsbu¶: $n_{{sb}_u}$ - number of soft input bounds. Type: int; default: 0

property nsbx¶: $n_{{sb}_x}$ - number of soft state bounds. Type: int; default: 0

property nsbx_e¶: $n_{{sb}^e_{x}}$ - number of soft state bounds at terminal shooting node N. Type: int; default: 0

property nsg¶: $n_{{sg}}$ - number of soft general linear constraints. Type: int; default: 0

property nsg_e¶: $n_{{sg}^e}$ - number of soft general linear constraints at terminal shooting node N. Type: int; default: 0

property nsh¶: $n_{{sh}}$ - number of soft nonlinear constraints. Type: int; default: 0

property nsh_0¶: $n_{{sh}}^0$ - number of soft nonlinear constraints at shooting node 0. Type: int; default: 0

property nsh_e¶: $n_{{sh}}^e$ - number of soft nonlinear constraints at terminal shooting node N. Type: int; default: 0

property nsphi¶: $n_{{s\phi}}$ - number of soft convex-over-nonlinear constraints. Type: int; default: 0

property nsphi_0¶: $n_{{s\phi}^0}$ - number of soft convex-over-nonlinear constraints at shooting node 0. Type: int; default: 0

property nsphi_e¶: $n_{{s\phi}^e}$ - number of soft convex-over-nonlinear constraints at terminal shooting node N. Type: int; default: 0

property nu¶: $n_u$ - number of inputs. Type: int; default: None

property nx¶: $n_x$ - number of states. Type: int; default: None

property ny¶: $n_y$ - number of residuals in Lagrange term. Type: int; default: 0

property ny_0¶: $n_{y}^0$ - number of residuals in Mayer term. Type: int; default: 0

property ny_e¶: $n_{y}^e$ - number of residuals in Mayer term. Type: int; default: 0

property nz¶: $n_z$ - number of algebraic variables. Type: int; default: 0

class acados_template.acados_ocp.AcadosOcpOptions¶

class containing the description of the solver options

property Tsim¶: Time horizon for one integrator step. Automatically calculated as tf/N. Default: None

property alpha_min¶: Minimal step size for globalization MERIT_BACKTRACKING, default: 0.05.

property alpha_reduction¶: Step size reduction factor for globalization MERIT_BACKTRACKING, default: 0.7.

property collocation_type¶

Collocation type: only relevant for implicit integrators – string in {‘GAUSS_RADAU_IIA’, ‘GAUSS_LEGENDRE’, ‘EXPLICIT_RUNGE_KUTTA’}.

Default: GAUSS_LEGENDRE

property cost_discretization¶: Cost discretization

property custom_templates¶

List of tuples of the form: (input_filename, output_filename)

Custom templates are render in OCP solver generation.

Default: [].

property custom_update_copy¶: Boolean; If True, the custom update function files are copied into the code_export_directory.

property custom_update_filename¶

Filename of the custom C function to update solver data and parameters in between solver calls

This file has to implement the functions int custom_update_init_function([model.name]_solver_capsule* capsule); int custom_update_function([model.name]_solver_capsule* capsule); int custom_update_terminate_function([model.name]_solver_capsule* capsule);

Default: ‘’.

property custom_update_header_filename¶

Header filename of the custom C function to update solver data and parameters in between solver calls

This file has to declare the custom_update functions and look as follows:

// Called at the end of solver creation.

// This is allowed to allocate memory and store the pointer to it into capsule->custom_update_memory.

int custom_update_init_function([model.name]_solver_capsule* capsule);

// Custom update function that can be called between solver calls

int custom_update_function([model.name]_solver_capsule* capsule, double* data, int data_len);

// Called just before destroying the solver.

// Responsible to free allocated memory, stored at capsule->custom_update_memory.

int custom_update_terminate_function([model.name]_solver_capsule* capsule);

Default: ‘’.

property eps_sufficient_descent¶: Factor for sufficient descent (Armijo) conditon, see line_search_use_sufficient_descent. Type: float, default: 1e-4.

property exact_hess_constr¶

Used in case of hessian_approx == ‘EXACT’.

Can be used to turn off exact hessian contributions from the constraints module.

property exact_hess_cost¶

Used in case of hessian_approx == ‘EXACT’.

Can be used to turn off exact hessian contributions from the cost module.

property exact_hess_dyn¶

Used in case of hessian_approx == ‘EXACT’.

Can be used to turn off exact hessian contributions from the dynamics module.

property ext_cost_num_hess¶

Determines if custom hessian approximation for cost contribution is used (> 0).

Or if hessian contribution is evaluated exactly using CasADi external function (=0 - default).

property ext_fun_compile_flags¶: String with compiler flags for external function compilation. Default: ‘-O2’.

property full_step_dual¶: Determines if dual variables are updated with full steps (alpha=1.0) when primal variables are updated with smaller step. Type: int; 0 or 1; default: 0.

property globalization¶: Globalization type. String in (‘FIXED_STEP’, ‘MERIT_BACKTRACKING’). Default: ‘FIXED_STEP’.

Note

preliminary implementation.

property globalization_use_SOC¶: Determines if second order correction (SOC) is done when using MERIT_BACKTRACKING. SOC is done if preliminary line search does not return full step. Type: int; 0 or 1; default: 0.

property hessian_approx¶: Hessian approximation. String in (‘GAUSS_NEWTON’, ‘EXACT’). Default: ‘GAUSS_NEWTON’.

property hpipm_mode¶

Mode of HPIPM to be used,

String in (‘BALANCE’, ‘SPEED_ABS’, ‘SPEED’, ‘ROBUST’).

Default: ‘BALANCE’.

see https://cdn.syscop.de/publications/Frison2020a.pdf and the HPIPM code: https://github.com/giaf/hpipm/blob/master/ocp_qp/x_ocp_qp_ipm.c#L69

property integrator_type¶: Integrator type. String in (‘ERK’, ‘IRK’, ‘GNSF’, ‘DISCRETE’, ‘LIFTED_IRK’). Default: ‘ERK’.

property levenberg_marquardt¶: Factor for LM regularization. Type: float >= 0 Default: 0.0.

property line_search_use_sufficient_descent¶: Determines if sufficient descent (Armijo) condition is used in line search. Type: int; 0 or 1; default: 0.

property model_external_shared_lib_dir¶: Path to the .so lib

property model_external_shared_lib_name¶: Name of the .so lib

property nlp_solver_ext_qp_res¶

Determines if residuals of QP are computed externally within NLP solver (for debugging)

Type: int; 0 or 1; Default: 0.

property nlp_solver_max_iter¶: NLP solver maximum number of iterations. Type: int > 0 Default: 100

property nlp_solver_step_length¶: Fixed Newton step length. Type: float >= 0. Default: 1.0.

property nlp_solver_tol_comp¶: NLP solver complementarity tolerance

property nlp_solver_tol_eq¶: NLP solver equality tolerance

property nlp_solver_tol_ineq¶: NLP solver inequality tolerance

property nlp_solver_tol_stat¶: NLP solver stationarity tolerance. Type: float > 0 Default: 1e-6

property nlp_solver_type¶: NLP solver. String in (‘SQP’, ‘SQP_RTI’). Default: ‘SQP_RTI’.

property print_level¶: Verbosity of printing. Type: int >= 0 Default: 0

property qp_solver¶: QP solver to be used in the NLP solver. String in (‘PARTIAL_CONDENSING_HPIPM’, ‘FULL_CONDENSING_QPOASES’, ‘FULL_CONDENSING_HPIPM’, ‘PARTIAL_CONDENSING_QPDUNES’, ‘PARTIAL_CONDENSING_OSQP’, ‘FULL_CONDENSING_DAQP’). Default: ‘PARTIAL_CONDENSING_HPIPM’.

property qp_solver_cond_N¶: QP solver: New horizon after partial condensing. Set to N by default -> no condensing.

property qp_solver_cond_ric_alg¶: QP solver: Determines which algorithm is used in HPIPM condensing. 0: dont factorize hessian in the condensing; 1: factorize. Default: 1

property qp_solver_iter_max¶: QP solver: maximum number of iterations. Type: int > 0 Default: 50

property qp_solver_ric_alg¶

QP solver: Determines which algorithm is used in HPIPM OCP QP solver. 0 classical Riccati, 1 square-root Riccati.

Note: taken from [HPIPM paper]:

the classical implementation requires the reduced (projected) Hessian with respect to the dynamics equality constraints to be positive definite, but allows the full-space Hessian to be indefinite)
the square-root implementation, which in order to reduce the flop count employs the Cholesky factorization of the Riccati recursion matrix (P), and therefore requires the full-space Hessian to be positive definite

[HPIPM paper]: HPIPM: a high-performance quadratic programming framework for model predictive control, Frison and Diehl, 2020 https://cdn.syscop.de/publications/Frison2020a.pdf

Default: 1

property qp_solver_tol_comp¶: QP solver complementarity. Default: None

property qp_solver_tol_eq¶: QP solver equality tolerance. Default: None

property qp_solver_tol_ineq¶: QP solver inequality. Default: None

property qp_solver_tol_stat¶: QP solver stationarity tolerance. Default: None

property qp_solver_warm_start¶: QP solver: Warm starting. 0: no warm start; 1: warm start; 2: hot start. Default: 0

property qp_tol¶: QP solver tolerance. Sets all of the following at once or gets the max of qp_solver_tol_eq, qp_solver_tol_ineq, qp_solver_tol_comp and qp_solver_tol_stat.

property reg_epsilon¶: Epsilon for regularization, used if regularize_method in [‘PROJECT’, ‘MIRROR’, ‘CONVEXIFY’]

property regularize_method¶

Regularization method for the Hessian. String in (‘NO_REGULARIZE’, ‘MIRROR’, ‘PROJECT’, ‘PROJECT_REDUC_HESS’, ‘CONVEXIFY’) or None.

MIRROR: performs eigenvalue decomposition H = V^T D V and sets D_ii = max(eps, abs(D_ii))
PROJECT: performs eigenvalue decomposition H = V^T D V and sets D_ii = max(eps, D_ii)
CONVEXIFY: Algorithm 6 from Verschueren2017, https://cdn.syscop.de/publications/Verschueren2017.pdf
PROJECT_REDUC_HESS: experimental

Note: default eps = 1e-4

Default: ‘NO_REGULARIZE’.

property shooting_nodes¶: Vector with the shooting nodes, time_steps will be computed from it automatically. Default: None

property sim_method_jac_reuse¶: Integer determining if jacobians are reused within integrator or ndarray of ints > 0 of shape (N,). 0: False (no reuse); 1: True (reuse) Default: 0

property sim_method_newton_iter¶: Number of Newton iterations in simulation method. Type: int > 0 Default: 3

property sim_method_newton_tol¶: Tolerance of Newton system in simulation method. Type: float: 0.0 means not used Default: 0.0

property sim_method_num_stages¶: Number of stages in the integrator. Type: int > 0 or ndarray of ints > 0 of shape (N,). Default: 4

property sim_method_num_steps¶: Number of steps in the integrator. Type: int > 0 or ndarray of ints > 0 of shape (N,). Default: 1

property tf¶: Prediction horizon Type: float > 0 Default: None

property time_steps¶: Vector with time steps between the shooting nodes. Set automatically to uniform discretization if N and tf are provided. Default: None

property tol¶: NLP solver tolerance. Sets or gets the max of nlp_solver_tol_eq, nlp_solver_tol_ineq, nlp_solver_tol_comp and nlp_solver_tol_stat.

class acados_template.acados_model.AcadosModel¶

Class containing all the information to code generate the external CasADi functions that are needed when creating an acados ocp solver or acados integrator. Thus, this class contains:

the name of the model,
all CasADi variables/expressions needed in the CasADi function generation process.

con_h_expr¶: CasADi expression for the constraint $h$; Default: None

con_h_expr_0¶: CasADi expression for the initial constraint $h^0$; Default: None

con_h_expr_e¶: CasADi expression for the terminal constraint $h^e$; Default: None

con_phi_expr¶: CasADi expression for the constraint phi; Default: None

con_phi_expr_0¶: CasADi expression for the terminal constraint $\phi_0$; Default: None

con_phi_expr_e¶: CasADi expression for the terminal constraint $\phi_e$; Default: None

con_r_expr¶: CasADi expression for the constraint phi(r), dummy input for outer function; Default: None

con_r_expr_0¶: CasADi expression for the terminal constraint $\phi_0(r)$, dummy input for outer function; Default: None

con_r_expr_e¶: CasADi expression for the terminal constraint $\phi_e(r)$, dummy input for outer function; Default: None

con_r_in_phi¶: CasADi expression for the terminal constraint $\phi(r)$, input for outer function; Default: None

con_r_in_phi_0¶: CasADi expression for the terminal constraint $\phi_0(r)$, input for outer function; Default: None

con_r_in_phi_e¶: CasADi expression for the terminal constraint $\phi_e(r)$, input for outer function; Default: None

cost_conl_custom_outer_hess¶: CasADi expression for the custom hessian of the outer loss function (only for convex-over-nonlinear cost); Default: None Used if acados_template.acados_ocp.AcadosOcpOptions.cost_type is ‘CONVEX_OVER_NONLINEAR’.

cost_conl_custom_outer_hess_0¶: CasADi expression for the custom hessian of the outer loss function (only for convex-over-nonlinear cost), initial; Default: None Used if acados_template.acados_ocp.AcadosOcpOptions.cost_type_0 is ‘CONVEX_OVER_NONLINEAR’.

cost_conl_custom_outer_hess_e¶: CasADi expression for the custom hessian of the outer loss function (only for convex-over-nonlinear cost), terminal; Default: None Used if acados_template.acados_ocp.AcadosOcpOptions.cost_type_e is ‘CONVEX_OVER_NONLINEAR’.

cost_expr_ext_cost¶: CasADi expression for external cost; Default: None

cost_expr_ext_cost_0¶: CasADi expression for external cost, initial; Default: None

cost_expr_ext_cost_custom_hess¶: CasADi expression for custom hessian (only for external cost); Default: None

cost_expr_ext_cost_custom_hess_0¶: CasADi expression for custom hessian (only for external cost), initial; Default: None

cost_expr_ext_cost_custom_hess_e¶: CasADi expression for custom hessian (only for external cost), terminal; Default: None

cost_expr_ext_cost_e¶: CasADi expression for external cost, terminal; Default: None

cost_psi_expr¶: CasADi expression for the outer loss function $\psi(r, p)$; Default: None Used if acados_template.acados_ocp.AcadosOcpOptions.cost_type is ‘CONVEX_OVER_NONLINEAR’.

cost_psi_expr_0¶: CasADi expression for the outer loss function $\psi(r, p)$, initial; Default: None Used if acados_template.acados_ocp.AcadosOcpOptions.cost_type_0 is ‘CONVEX_OVER_NONLINEAR’.

cost_psi_expr_e¶: CasADi expression for the outer loss function $\psi(r, p)$, terminal; Default: None Used if acados_template.acados_ocp.AcadosOcpOptions.cost_type_e is ‘CONVEX_OVER_NONLINEAR’.

cost_r_in_psi_expr¶: CasADi symbolic input variable for the argument $r$ to the outer loss function $\psi(r, p)$; Default: None Used if acados_template.acados_ocp.AcadosOcpOptions.cost_type is ‘CONVEX_OVER_NONLINEAR’.

cost_r_in_psi_expr_0¶: CasADi symbolic input variable for the argument $r$ to the outer loss function $\psi(r, p)$, initial; Default: None Used if acados_template.acados_ocp.AcadosOcpOptions.cost_type_0 is ‘CONVEX_OVER_NONLINEAR’.

cost_r_in_psi_expr_e¶: CasADi symbolic input variable for the argument $r$ to the outer loss function $\psi(r, p)$, terminal; Default: None Used if acados_template.acados_ocp.AcadosOcpOptions.cost_type_e is ‘CONVEX_OVER_NONLINEAR’.

cost_y_expr¶: CasADi expression for nonlinear least squares; Default: None

cost_y_expr_0¶: CasADi expression for nonlinear least squares, initial; Default: None

cost_y_expr_e¶: CasADi expression for nonlinear least squares, terminal; Default: None

disc_dyn_expr¶: CasADi expression for the discrete dynamics $x_{+} = f_\text{disc}(x, u, p)$. Used if acados_template.acados_ocp.AcadosOcpOptions.integrator_type == ‘DISCRETE’. Default: None

dyn_disc_fun¶: name of function discrete dyanamics; Default: None

dyn_disc_fun_jac¶: name of function discrete dyanamics + jacobian; Default: None

dyn_disc_fun_jac_hess¶: name of function discrete dyanamics + jacobian and hessian; Default: None

dyn_ext_fun_type¶: type of external functions for dynamics module; ‘casadi’ or ‘generic’; Default: ‘casadi’

dyn_generic_source¶: name of source file for discrete dyanamics; Default: None

f_expl_expr¶: CasADi expression for the explicit dynamics $\dot{x} = f_\text{expl}(x, u, p)$. Used if acados_template.acados_ocp.AcadosOcpOptions.integrator_type == ‘ERK’. Default: None

f_impl_expr¶: CasADi expression for the implicit dynamics $f_\text{impl}(\dot{x}, x, u, z, p) = 0$. Used if acados_template.acados_ocp.AcadosOcpOptions.integrator_type == ‘IRK’. Default: None

gnsf¶: dictionary containing information on GNSF structure needed when rendering templates. Contains integers nontrivial_f_LO, purely_linear.

name¶: The model name is used for code generation. Type: string. Default: None

p¶: CasADi variable describing parameters of the DAE; Default: []

t¶: CasADi variable representing time t in functions; Default: []

u¶: CasADi variable describing the input of the system; Default: None

x¶: CasADi variable describing the state of the system; Default: None

xdot¶: CasADi variable describing the derivative of the state wrt time; Default: None

z¶: CasADi variable describing the algebraic variables of the DAE; Default: []

acados integrator interface – `AcadosSim` and `AcadosSimSolver`¶

The class AcadosSim can be used to formulate a simulation problem, for which an acados integrator (AcadosSimSolver) can be created. The interface to interact with the acados integrator in C is based on ctypes.

class acados_template.acados_sim.AcadosSim(acados_path='')¶

The class has the following properties that can be modified to formulate a specific simulation problem, see below:

Parameters: acados_path – string with the path to acados. It is used to generate the include and lib paths.

dims of type acados_template.acados_ocp.AcadosSimDims - are automatically detected from model
model of type acados_template.acados_model.AcadosModel
solver_options of type acados_template.acados_sim.AcadosSimOpts
acados_include_path (set automatically)
shared_lib_ext (set automatically)
acados_lib_path (set automatically)
parameter_values - used to initialize the parameters (can be changed)

acados_include_path¶: Path to acados include directory (set automatically), type: string

acados_lib_path¶: Path to where acados library is located (set automatically), type: string

code_export_directory¶: Path to where code will be exported. Default: c_generated_code.

dims¶: Dimension definitions, automatically detected from model. Type acados_template.acados_sim.AcadosSimDims

model¶: Model definitions, type acados_template.acados_model.AcadosModel

property parameter_values¶: $p$ - initial values for parameter - can be updated

solver_options¶: Solver Options, type acados_template.acados_sim.AcadosSimOpts

class acados_template.acados_sim.AcadosSimDims¶

Class containing the dimensions of the model to be simulated.

property np¶: $n_p$ - number of parameters. Type: int >= 0

property nu¶: $n_u$ - number of inputs. Type: int >= 0

property nx¶: $n_x$ - number of states. Type: int > 0

property nz¶: $n_z$ - number of algebraic variables. Type: int >= 0

class acados_template.acados_sim.AcadosSimOpts¶

class containing the solver options

property T¶: Time horizon

property collocation_type¶

Collocation type: relevant for implicit integrators – string in {‘GAUSS_RADAU_IIA’, ‘GAUSS_LEGENDRE’, ‘EXPLICIT_RUNGE_KUTTA’}.

Default: GAUSS_LEGENDRE

property ext_fun_compile_flags¶: String with compiler flags for external function compilation. Default: ‘-O2’.

property integrator_type¶: Integrator type. Default: ‘ERK’.

property newton_iter¶: Number of Newton iterations in simulation method. Default: 3

property newton_tol¶: Tolerance for Newton system solved in implicit integrator (IRK, GNSF). 0.0 means this is not used and exactly newton_iter iterations are carried out. Default: 0.0

property num_stages¶: Number of stages in the integrator. Default: 1

property num_steps¶: Number of steps in the integrator. Default: 1

property output_z¶: Boolean determining if values for algebraic variables (corresponding to start of simulation interval) are computed. Default: True

property sens_adj¶: Boolean determining if adjoint sensitivities are computed. Default: False

property sens_algebraic¶: Boolean determining if sensitivities wrt algebraic variables are computed. Default: False

property sens_forw¶: Boolean determining if forward sensitivities are computed. Default: True

property sens_hess¶: Boolean determining if hessians are computed. Default: False

property sim_method_jac_reuse¶: Integer determining if jacobians are reused (0 or 1). Default: 0

class acados_template.acados_sim_solver.AcadosSimSolver(acados_sim: acados_template.acados_sim.AcadosSim, json_file='acados_sim.json', generate=True, build=True, cmake_builder: acados_template.builders.CMakeBuilder = None, verbose: bool = True)¶

Class to interact with the acados integrator C object.

param acados_sim

type AcadosOcp (takes values to generate an instance AcadosSim) or AcadosSim

param json_file

Default: ‘acados_sim.json’

param build

Default: True

param cmake_builder

type CMakeBuilder generate a CMakeLists.txt and use the CMake pipeline instead of a Makefile (CMake seems to be the better option in conjunction with MS Visual Studio); default: None

classmethod create_cython_solver(json_file)¶

classmethod generate(acados_sim: acados_template.acados_sim.AcadosSim, json_file='acados_sim.json', cmake_builder: acados_template.builders.CMakeBuilder = None)¶: Generates the code for an acados sim solver, given the description in acados_sim

get(field_)¶

Get the last solution of the solver.

param str field

string in [‘x’, ‘u’, ‘z’, ‘S_forw’, ‘Sx’, ‘Su’, ‘S_adj’, ‘S_hess’, ‘S_algebraic’, ‘CPUtime’, ‘time_tot’, ‘ADtime’, ‘time_ad’, ‘LAtime’, ‘time_la’]

options_set(field_: str, value_: bool)¶

Set solver options

param field

string in [‘sens_forw’, ‘sens_adj’, ‘sens_hess’]

param value

Boolean

set(field_: str, value_)¶

Set numerical data inside the solver.

param field

string in [‘x’, ‘u’, ‘p’, ‘xdot’, ‘z’, ‘seed_adj’, ‘T’, ‘t0’]

param value

the value with appropriate size.

simulate(x=None, u=None, z=None, p=None)¶: Simulate the system forward for the given x, u, z, p and return x_next. Wrapper around solve() taking care of setting/getting inputs/outputs.

solve()¶: Solve the simulation problem with current input.

acados_template.acados_sim_solver.sim_get_default_cmake_builder() → acados_template.builders.CMakeBuilder ¶: If AcadosSimSolver is used with CMake this function returns a good first setting. :return: default CMakeBuilder

Builders¶

The default builder for acados is make using a Makefile generated by acados. If cross-platform compatibility is required CMake can be used to build the binaries required by the solvers.

class acados_template.builders.CMakeBuilder¶

Class to work with the CMake build system.

build_targets¶: A comma-separated list of the build targets, if None then all targets will be build; type: List of strings; default: None.

exec(code_export_directory, verbose=True)¶: Execute the compilation using CMake with the given settings. :param code_export_directory: must be the absolute path to the directory where the code was exported to

host¶: Defines the generator, options can be found via cmake –help under ‘Generator’. Type: string. Linux default ‘Unix Makefiles’, Windows ‘Visual Studio 15 2017 Win64’; default value: None.

options_on¶: List of strings as CMake options which are translated to ‘-D Opt[0]=ON -D Opt[1]=ON …’; default: None.

Python Interface¶

Optimal Control Problem description¶

Installation¶

Windows 10+ (WSL) and VSCode¶

Virtual Environment¶

Connect to VS-Code¶

Windows¶

Overview¶

acados OCP solver interface – AcadosOcp and AcadosOcpSolver¶

acados integrator interface – AcadosSim and AcadosSimSolver¶

Builders¶

acados OCP solver interface – `AcadosOcp` and `AcadosOcpSolver`¶

acados integrator interface – `AcadosSim` and `AcadosSimSolver`¶