Melda Ulusoy, MathWorks
Model Predictive Control Toolbox™ provides functions, an app, and Simulink® blocks for designing and simulating model predictive controllers (MPCs). Model Predictive Control Toolbox lets you specify plant models, horizons, constraints, and weights. You can evaluate the performance of your model predictive controller by running it against the nonlinear Simulink model. The toolbox lets you adjust the run-time weights and constraints of your model predictive controller. For nonlinear plants with a wide operating range, you can implement adaptive or gain-scheduled MPC controllers. The toolbox supports C code and IEC-61131 Structured Text generation for targeting embedded microprocessors and PLCs.
Model Predictive Control Toolbox™ lets you design and simulate model predictive controllers to control multi-input multi-output systems subject to input/output constraints for applications such as process control, powertrain control, and advanced driver-assistance systems.
Using the MPC Designer app, you can define an internal plant model, specify prediction and control horizons, input and output constraints, and controller tuning input/output weights. You can interactively tune your MPC controller, simulate it against the linear plant model, and verify its performance by running it against the nonlinear Simulink® model.
You can adjust weights and constraints of your MPC controller at run time. For nonlinear plants with a wide operating range, you can implement adaptive MPC controllers that let you update the internal plant model at each computation step.
The toolbox supports C code and IEC-61131 Structured Text generation for targeting embedded microprocessors and PLCs. For applications with fast sample times, you can use explicit MPC controllers that require fewer run-time computations than traditional MPC controllers by using optimal solutions precomputed offline. Another option to ensure you won’t exceed the desired execution time is to use an approximate solution by limiting the number of iterations for the QP solver. In addition to the built-in QP solver, the toolbox also provides the flexibility to use a custom QP solver.
For more information on Model Predictive Control Toolbox™, please return to the product page.
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