시뮬레이션
MATLAB® 및 Simulink®에서 선형 또는 비선형 플랜트에 대한 제어기 시뮬레이션
이 툴박스는 명령줄 및 Simulink에서 제어기를 시뮬레이션하기 위한 툴을 제공합니다. MPC 디자이너 앱을 사용하여 제어기를 설계하면, 설계 과정 중에 제어 시나리오를 시뮬레이션할 수 있고 설계로부터 Simulink 모델을 생성할 수 있습니다.
함수
블록
| MPC Controller | Simulate model predictive controller |
앱
| MPC 디자이너 | 모델 예측 제어기 설계 및 시뮬레이션 |
도움말 항목
시뮬레이션 기본 사항
- 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.
런타임 기능
- 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.
선형 MPC를 위한 QP 솔버
- 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.
사례 연구
- 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).
