For applications with fast sample rates, you can generate an explicit model predictive controller from a regular controller or implement an approximate solution.įor rapid prototyping and embedded system implementation, the toolbox supports C code and IEC 61131-3 Structured Text generation.ĭesign MPC controllers to control MIMO systems subject to input and output constraints. To control a nonlinear plant, you can implement adaptive and gain-scheduled MPCs. You can adjust the behavior of the controller by varying its weights and constraints at run time. By running closed-loop simulations, you can evaluate controller performance. The toolbox lets you specify plant and disturbance models, horizons, constraints, and weights. Model Predictive Control Toolbox™ provides functions, an app, and Simulink ® blocks for designing and simulating model predictive controllers (MPCs).
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