Model Predictive Control Toolbox provides functions, Simulink blocks, and a graphical tool for designing andsimulating model predictive controllers in MATLAB and Simulink. The ability to modelprocess interactions often enables model predictive controllers to outperform multiple PID control loops, whichrequire individual tuning and other techniques to reduce loop coupling. Because they base their actions on an internal plant model, model predictivecontrollers can forecast future process behavior and adjust control actions accordingly.
Model predictive controllers can be used to optimize closed-loop system performance of MIMO plants subject toinput and output constraints. MPC Controller block (red) for designing and simulating model predictive controllers directly in Simulink.ĭesigning and Simulating Model Predictive Controllers Getting Started with Model Predictive Control Toolbox 10:07Use Model Predictive Control Toolbox to design and simulate model predictivecontrollers. Robustness Support for C-code generation with Simulink Coder
Key Features Design and simulation of model predictive controllers in MATLAB and Simulink Customization of constraints and weights with advisory tools for improved performance and robustness Control of plants over a range of operating conditions using multiple model predictive controllers withīumpless control transfer Run-time adjustment of controller performance through constraint and weight changes Specialized model predictive control quadratic programming (QP) solver optimized for speed, efficiency, and For rapid prototyping and embeddedsystem design, the toolbox supports C-code generation. You can adjustcontroller performance as it runs by tuning weights and varying constraints. By runningdifferent scenarios in linear and nonlinear simulations, you can evaluate controller performance. The toolbox enables you to diagnose issues that could lead to run-time failuresand provides advice on changing weights and constraints to improve performance and robustness. You can set and modify the predictive model, control and prediction horizons, input andoutput constraints, and weights. You can design and simulate model predictive controllers using functions in MATLAB orblocks in Simulink. Model Predictive Control Toolbox provides tools for systematically analyzing, designing, and tuning modelpredictive controllers. Model Predictive Control ToolboxDesign and simulate model predictive controllers
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