8/30/2023 0 Comments Model predictive control toolbox![]() To control a nonlinear plant, you can implement adaptive, gain-scheduled, and nonlinear MPC controllers. The toolbox provides deployable optimization solvers and also enables you to use a custom solver. ![]() 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 controllers using linear and nonlinear model predictive control (MPC).
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