Matlab + Simulink and Octave Interface

In order to use acados from Octave or Matlab, you need to create the acados shared libraries using either the CMake or Make build system, as described on the installation page.

Getting started

Check out the examples minimal_example_ocp.m and minimal_example_sim.m to get started with the Matlab interface of acados. Note that acados currently supports both an old Matlab interface (< v0.4.0) as well as the new one (>= v0.4.0). Unfortunately, not all MATLAB examples have been ported to the new interface yet. If you are new to acados please start with those examples that use the new interface already.

The examples require an installation of CasADi to generate the model functions. The getting_started example offers the option to attempt to automatically download the correct version in the recommended folder. Detailed instructions for a manual installation can be found in the last section of this page Setup CasADi.

The problem formulation is stated in this PDF.

Export environment variables

In order to run the examples, some environment variables need to be exported. Instead of running the scripts below, you can modify an rc file, like .bashrc when launching MATLAB from bash, .matlab7rc.sh or startup.m to always have those environment variables defined when starting Matlab.

Linux / macOS

Navigate into the folder of the example you want to run and execute the following command:

source env.sh # Which can be found in the folder of one of the examples

If you want to run an acados example from another folder, you need to export the environment variable ACADOS_INSTALL_DIR properly. In the env.sh file it is assumed that ACADOS_INSTALL_DIR is two folders above the directory, in which the example is located.

Afterwards, launch Matlab or Octave from the same shell.

If you want to run the examples in a different folder, please close the current shell and open a new one to repeat the procedure: this ensures the correct setting of the environment variables.

Windows

1. Open Matlab and navigate into <acados_root>/examples/acados_matlab_octave.

2. Run acados_env_variables_windows to export the environment variable ACADOS_INSTALL_DIR.

3. Navigate into <acados_root>/examples/acados_matlab_octave/getting_started and run one of the examples.

Interface structure

The interface allows one to conveniently and compactly formulate an OCP (or IVP) and specify solver options. The nonlinear problem functions can be formulated using CasADi symbolics which are generated as C code with the required derivatives using automatic differentiation. The whole problem description is written to a json-file which is then used to render different templates, via the Tera renderer. These are the same templates as in the Python interface (see Python interface). In addition to a MEX wrapper it contains all the C code that is needed for embedded deployment. These templates can be found in <acados_root>/interfaces/acados_template/acados_template/c_templates_tera.

Options documentation

For the template based part of the Matlab interface, we refer to the docstring based documentation of the Python interface.

Simulink

The templates mentioned above also contain templated S-functions and corresponding make functions for both the OCP solver and the acados integrator.

A basic Simulink example can be found in <acados_root>/examples/acados_python/getting_started/simulink_example.m

A more advanced Simulink example which showcases how to customize the inputs and outputs of the Simulink block corresponding to the solver can be found in <acados_root>/examples/acados_python/getting_started/simulink_example.m

List of possible inputs

This is a list of possible inputs to the Simulink block of an OCP solver which can be activated by setting the corresponding values in the acados Simulink options.

Port Name	Description	Shape	MOCP Support
lbx_0	Lower bound on x for stage 0	nbx_0	Yes
ubx_0	Upper bound on x for stage 0	nbx_0	Yes
parameter_traj	Parameters - concatenated for all stages 0 to N	sum(np_i), i=0,..., N	Yes
p_global	Global parameters - first value indicates if update should be performed (0 = no update), followed by new numerical values of p_global	1 + np_global	Yes
y_ref_0	Reference y_ref at stage 0	ny_0	Yes
y_ref	y_ref concatenated for stages 1 to N-1	(N-1) * ny	No
y_ref_e	Reference y_ref at stage N	ny_e	Yes
lbx	Lower bound x values concatenated for stages 1 to N-1	sum(nbx_i), for i = 1,..., N-1	Yes
ubx	Upper bound x values concatenated for stages 1 to N-1	sum(nbx_i), for i = 1,..., N-1	Yes
lbx_e	Lower bound x at shooting node N	nbx_e	Yes
ubx_e	Upper bound x at shooting node N	nbx_e	Yes
lbu	Lower bound u values concatenated for stages 0 to N-1	sum(nbu_i), for i = 0,..., N-1	Yes
ubu	Upper bound u values concatenated for stages 0 to N-1	sum(nbu_i), for i = 0,..., N-1	Yes
lg	Lower bound g values concatenated for stages 0 to N-1	sum(ng_i), for i = 0,..., N-1	No
ug	Upper bound g values concatenated for stages 0 to N-1	sum(ng_i), for i = 0,..., N-1	No
lh	Lower bound h values concatenated for stages 1 to N-1	sum(nh_i), for i = 1,..., N-1	Yes
uh	Upper bound h values concatenated for stages 1 to N-1	sum(nh_i), for i = 1,..., N-1	Yes
lh_0	Lower bound h at stage 0	nh_0	Yes
uh_0	Upper bound h at stage 0	nh_0	Yes
lh_e	Lower bound h at stage N	nh_e	Yes
uh_e	Upper bound h at stage N	nh_e	Yes
cost_W_0	Cost matrix W_0 in column-major format	ny_0 * ny_0	No
cost_W	Cost matrix W in column-major format	ny * ny	No
cost_W_e	Cost matrix W_e in column-major format	ny_e * ny_e	No
cost_zl	Cost zl for all nodes 0 to N	sum(ns_i) for i = 0,..., N	Yes
cost_zu	Cost zu for all nodes 0 to N	sum(ns_i) for i = 0,..., N	Yes
cost_Zl	Cost Zl for all nodes 0 to N	sum(ns_i) for i = 0,..., N	Yes
cost_Zu	Cost Zu for all nodes 0 to N	sum(ns_i) for i = 0,..., N	Yes
reset_solver	Determines if the solver's iterate is set to all zeros before other initializations	1	Yes
ignore_inits	Determines if initialization (x_init, u_init, pi_init, slacks_init) is set (0) or ignored (1)	1	Yes
x_init	Initialization of x for all stages	sum(nx_i), i=0,..., N	Yes
u_init	Initialization of u for stages 0 to N-1	sum(nu_i), i=0,..., N-1	Yes
pi_init	Initialization of pi for stages 0 to N-1	sum(npi_i), i=0,..., N-1	Yes
slacks_init	Initialization of slack values for all stages (0 to N)	2 * ns_total	Yes
rti_phase	Real-time iteration phase	1	Yes

List of possible outputs

This is a list of possible outputs of the Simulink block of an OCP solver which can be activated by setting the corresponding values in the acados Simulink options.

Port Name	Description	Shape	MOCP Support
u0	Control input at node 0	nu_0	Yes
utraj	Control input concatenated for nodes 0 to N-1	sum(nu_i), i=0,..., N-1	Yes
xtraj	State concatenated for nodes 0 to N	sum(nx_i), i=0,..., N	Yes
ztraj	Algebraic states concatenated for nodes 0 to N-1	sum(nz_i), i=0,..., N-1	Yes
pi_all	Equality Lagrange multipliers concatenated for nodes 0 to N-1	sum(npi_i), i=0,..., N-1	Yes
slack_values	Slack values concatenated in order [sl_0, su_0, ..., sl_N, su_N]	2 * ns_total	Yes
solver_status	Acados solver status (0 = SUCCESS)	1	Yes
cost_value	Cost function value	1	Yes
KKT_residual	KKT residual	1	Yes
KKT_residuals	KKT residuals, size [4] (stat, eq, ineq, comp)	4	Yes
x1	State at node 1	nx_1	Yes
CPU_time	CPU time	1	Yes
CPU_time_sim	CPU time for integrator	1	Yes
CPU_time_qp	CPU time for QP solution	1	Yes
CPU_time_lin	CPU time for linearization (including integrator)	1	Yes
sqp_iter	NLP solver iterations	1	Yes
parameter_traj	Parameter trajectory	sum(np_i), i=0,..., N	Yes

Developing extensions

If you want a more advanced interaction with the acados solver via Simulink, feel free to edit the corresponding templates in <acados_root>/interfaces/acados_template/acados_template/c_templates_tera/matlab_templates to add more inputs or outputs.

S-function Mask

The Simulink S-functions interface can be improved using the following mask commands, which shows the names of input and output ports, facilitating debugging operations. These commands use global variables that are automatically generated by the make_sfun command.

1. OCP S-function mask

global sfun_input_names sfun_output_names
for i = 1:length(sfun_input_names)
	port_label('input', i, sfun_input_names{i})
end
for i = 1:length(sfun_output_names)
	port_label('output', i, sfun_output_names{i})
end

1. SIM S-function mask

global sfun_sim_input_names sfun_sim_output_names
for i = 1:length(sfun_sim_input_names)
	port_label('input', i, sfun_sim_input_names{i})
end
for i = 1:length(sfun_sim_output_names)
	port_label('output', i, sfun_sim_output_names{i})
end

To use the mask command just copy-paste it in the “icon drawing commands” field, accessible by right clicking on the S-function block - Mask - Edit Mask.

Setup CasADi

To create external function for your problem, we suggest to use CasADi from the folder <acados_root_folder>/external. Depending on the environment you want to use to generate CasADi functions from, proceed with the corresponding paragraph (Matlab, Octave).

Any CasADi version between 3.4.0 and 3.6.7 should work. If you don’t have CasADi yet, you can install it as described below.

Matlab

Download and extract the CasADi binaries into <acados_root_folder>/external/casadi-matlab:

cd external
wget -q -nc --show-progress https://github.com/casadi/casadi/releases/download/3.4.0/casadi-linux-matlabR2014b-v3.4.0.tar.gz
mkdir -p casadi-matlab
tar -xf casadi-linux-matlabR2014b-v3.4.0.tar.gz -C casadi-matlab
cd ..

Octave version 6.2 or later

Download and extract the CasADi binaries into <acados_root_folder>/external/casadi-octave:

cd external
wget -O casadi-linux-octave.zip https://github.com/casadi/casadi/releases/download/3.6.7/casadi-3.6.7-linux64-octave7.3.0.zip
mkdir -p casadi-octave
unzip casadi-linux-octave.zip -d ./casadi-octave;