You can implement the model predictive controller by generating C code (with Real-Time Workshop®). The toolbox is also capable of converting MPC controllers into real-time executable code which can be easily deployed to typical hardware control platforms. You can estimate the model from experimental data (with System Identification Toolbox™), obtain it from a linearized Simulink model, or specify it directly as a linear time invariant object, such as a transfer function, or a state space model. The toolbox lets you define an internal plant model used by the model predictive controller in three ways. The CT is applicable to a broad class of dynamic systems, but features additional modelling tools specially designed for robotics. Linear and Nonlinear Model Predictive Control Linear Repetitive Process Toolkit linux-prerequisites-sources LOLIMOT Low Discrepancy Sequences Machine Learning Toolbox Make Matrix maple2scilab Mathieu functions toolbox for Scilab Matlab/Octave Compatibility toolbox Matrix Market MED Memetic algorithms toolbox Metanet Metanet and. These controllers optimize the performance of multi-input/multi-output systems that are subject to input and output constraints. We introduce the Control Toolbox (CT), an open-source C++ library for efficient modelling, control, estimation, trajectory optimization and model predictive control.
Model Predictive Control Toolbox™ provides MATLAB® functions, a graphical user interface (GUI), and Simulink® blocks for designing and simulating model predictive controllers in MATLAB and Simulink.
