The toolbox provides tools for simulating your controller from the command line and in Simulink. If you are designing a controller using the MPC Designer app, you can simulate control scenarios during the design process and generate a Simulink model from your design.
|MPC Controller||Simulate model predictive controller|
|MPC Designer||Design and simulate model predictive controllers|
- Simulating MPC Controller with Plant Model Mismatch
Simulate an MPC controller when there is a mismatch between the controller prediction model and the actual plant dynamics.
- Test MPC Controller Robustness using MPC Designer
You can test the robustness of your model predictive controller by simulating it with MPC Designer.
- Generate Simulink Model from MPC Designer
You can automatically generate a Simulink model that uses the current model predictive controller to control its internal plant model.
- Test an Existing MPC Controller with Simulink
Test an existing MPC controller within a Simulink model.
- Signal Previewing
Signal previewing can improve reference tracking and measured disturbance rejection if your application allows you to anticipate trends in such signals.
- Simulate Linear MPC Controller with Nonlinear Plant using Successive Linearizations
Simulate a model predictive controller with a nonlinear plant at the command line. At each control interval, relinearize the nonlinear plant and define a new controller based on the updated plant model.
- Update Constraints at Run Time
You can update the constraints of your MPC controller at each control interval.
- Tune Weights at Run Time
You can adjust the cost function penalty weights for your MPC controller while the controller operates.
- Adjust Horizons at Run Time
You can adjust the prediction and control horizons for your MPC controller while the controller operates.
- Switch Controller Online and Offline with Bumpless Transfer
Reduce large actuator movements when changing controller operating modes.
- Switching Controllers Based on Optimal Costs
You can switch between multiple MPC controllers based on their optimal objective function cost values.
- Monitoring Optimization Status to Detect Controller Failures
You can detect controller failures in real time by using the optimization status controller output.
- Simulate MPC Controller with a Custom QP Solver
Simulate the closed-loop response of a model predictive controller with a custom quadratic programming solver.
- Use Suboptimal Solution in Fast MPC Applications
You can guarantee the worst-case execution time for your MPC controller by applying a suboptimal solution after the number of optimization iterations exceeds a specified maximum value.
- Design and Cosimulate Control of High-Fidelity Distillation Tower with Aspen Plus Dynamics
Design a model predictive controller in MATLAB and use cosimulation validate whether the controller is robust enough to control a nonlinear plant.