Simulation
R2026bThe 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.
Functions
Blocks
 | MPC Controller | Simulate model predictive controller |
Apps
| MPC Designer | Design and simulate model predictive controllers |
Topics
Simulation Basics
- 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
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.
Run-Time Features
- 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.
- Setting Time-Varying Weights and Constraints with MPC Designer
When designing an MPC controller, you can specify tuning weights and constraints that vary over the prediction horizon. - 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.
QP Solvers for Linear MPC
- 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.
Case Studies
- 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. - Run Field Oriented Control of PMSM Using Model Predictive Control (Motor Control Blockset)
This example uses Model Predictive Control (MPC) to control the speed of a three-phase permanent magnet synchronous motor (PMSM).
