Simulation

Simulate controllers against linear or nonlinear plants in MATLAB^® and Simulink^®

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.

Functions

`mpcmove`	Compute optimal control action and update controller states
`mpcmoveopt`	Option set for `mpcmove` function
`mpcstate`	MPC controller state
`sim`	Simulate an MPC controller in closed loop with a linear plant
`mpcsimopt`	MPC simulation options
`plot`	Plot responses generated by MPC simulations

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.
- Improving Control Performance with Look-Ahead (Previewing)
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.
- Vary Input and Output Bounds at Run Time
Tune Weights at Run Time

You can adjust the cost function penalty weights for your MPC controller while the controller operates.
- Tuning MPC Controller Weights at Run-Time
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.
- Evaluate Control Performance Using Run-Time Horizon Adjustment
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 Solver

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.

Related Information

How to Run MPC Faster