The toolbox provides built-in solvers for linear MPC problems. 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 solver. Alternatively, for highly nonlinear plants, you can design nonlinear MPC controllers using nonlinear prediction models, cost functions and constraints.įor 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. Alternatively, for highly nonlinear plants, you can design nonlinear MPC controllers using nonlinear prediction models, cost functions and constraints. 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. You can adjust weights, constraints, prediction and control horizons of your MPC controller at run time. 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. Using the MPC Designer app, you can define an internal plant model, specify parameters such as prediction and control horizons, constraints, and controller weights. 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 advanced driver-assistance systems, process control, powertrain control, and robotics. For applications with fast sample rates, the toolbox lets you generate an explicit model predictive controller from a regular controller or implement an approximate solution.įor rapid prototyping and embedded system implementation, including deployment of optimization solvers, the toolbox supports C code and IEC 61131-3 Structured Text generation. 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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