TinyMPC MATLAB Interface

MATLAB wrapper for TinyMPC. Supports code generation and interaction with the C/C++ backend. Tested on Ubuntu and macOS.

Prerequisites

• MATLAB (R2024b +)
• C++ compiler with C++17 support

Building

1. Clone this repo (with submodules):

   git clone --recurse-submodules https://github.com/TinyMPC/tinympc-matlab.git

   If you already cloned without --recurse-submodules, run:

   git submodule update --init --recursive

2. Build the C++ library:

   cd tinympc-matlab
   mkdir build
   cd build
   cmake ..
   make

3. Verify installation:

   % Add paths and test that the module loads correctly
   addpath('src'); addpath('build');
   solver = TinyMPC();
   disp('TinyMPC MATLAB ready to use!');

Examples

The examples/ directory contains scripts demonstrating TinyMPC features:

• interactive_cartpole.m - Interactive cartpole control
• cartpole_example_one_solve.m - One-step solve
• cartpole_example_mpc.m - Full MPC loop
• cartpole_example_mpc_reference_constrained.m - Reference tracking and constraints
• cartpole_example_code_generation.m - Code generation
• quadrotor_hover_code_generation.m - Quadrotor codegen

Usage Example

Basic MPC Workflow

% Add required paths
addpath('src'); addpath('build');

% System matrices (cartpole example)
A = [1.0  0.01  0.0   0.0;
     0.0  1.0   0.039 0.0;
     0.0  0.0   1.002 0.01;
     0.0  0.0   0.458 1.002];
B = [0.0; 0.02; 0.0; 0.067];
Q = diag([10.0, 1.0, 10.0, 1.0]);
R = 1.0;
N = 20;  % Horizon length

% Create and setup solver
solver = TinyMPC();
solver.setup(A, B, Q, R, N, 'rho', 1.0, 'verbose', false);

% Set initial state and references
x0 = [0.5; 0; 0; 0];  % Initial state
solver.set_x0(x0);
solver.set_x_ref(zeros(4, N));      % State reference trajectory
solver.set_u_ref(zeros(1, N-1));    % Control reference trajectory

% Solve and get solution
status = solver.solve();  % Returns status code (0 = success)
solution = solver.get_solution();  % Get actual solution

% Access solution
fprintf('First control: %.3f\n', solution.controls(1));
states_trajectory = solution.states;     % All predicted states (4×20)
controls_trajectory = solution.controls; % All predicted controls (1×19)

Code Generation Workflow

% Setup solver with constraints
solver = TinyMPC();
u_min = -0.5; u_max = 0.5;  % Control bounds
solver.setup(A, B, Q, R, N, 'u_min', u_min, 'u_max', u_max, 'rho', 1.0);

% Generate C++ code
solver.codegen('out');

Adaptive Rho Workflow

% Setup solver first
solver = TinyMPC();
solver.setup(A, B, Q, R, N, 'rho', 1.0, 'adaptive_rho', true);

% Compute sensitivity matrices using built-in numerical differentiation
[dK, dP, dC1, dC2] = solver.compute_sensitivity_autograd();

% Generate code with sensitivity matrices
solver.codegen_with_sensitivity('out', dK, dP, dC1, dC2);

See examples/quadrotor_hover_code_generation.m for a complete example.

API Reference

Core Functions

% Setup solver with system matrices
solver.setup(A, B, Q, R, N, 'rho', rho, 'verbose', verbose, ...)

% Set initial state and references
solver.set_x0(x0)
solver.set_x_ref(x_ref)
solver.set_u_ref(u_ref)

% Solve and get solution
status = solver.solve()          % Returns status code (0 = success)
solution = solver.get_solution() % Get actual solution

Code Generation

% Generate standalone C++ code
solver.codegen(output_dir)

% Generate code with sensitivity matrices
solver.codegen_with_sensitivity(output_dir, dK, dP, dC1, dC2)

Sensitivity Analysis

% Compute sensitivity matrices using built-in autograd function
[dK, dP, dC1, dC2] = solver.compute_sensitivity_autograd()

Configuration

% Update solver settings
solver.update_settings('abs_pri_tol', 1e-6, 'abs_dua_tol', 1e-6, 'max_iter', 100, ...)

% Reset solver
solver.reset()

Solution Structure

The solver.get_solution() function returns a struct with:

• solution.states - Full state trajectory (nx × N matrix)
• solution.controls - Optimal control sequence (nu × (N-1) matrix)

Testing

Run the test suite:

addpath('tests');
run_all_tests;

See https://tinympc.org/ for full documentation.