분석

런타임 설계 오류 및 안정성 문제 검토, 가중치가 성능에 미치는 영향 분석, 제약 조건이 없는 제어기를 선형 분석에 맞게 변환

모델 예측 제어기를 생성하고 설계한 후에는 잠재적인 설계 문제가 있는지 검토할 수 있습니다. 자세한 내용은 Review Model Predictive Controller for Stability and Robustness Issues 항목을 참조하십시오.

함수

review Examine MPC controller for design errors and stability problems at run time

compare Compare two MPC objects

`cloffset`	Compute closed-loop DC gain from output disturbances to measured outputs assuming constraints are inactive at steady state
`sensitivity`	Calculate the value of a performance metric and its sensitivity to the diagonal weights of an MPC controller
`size`	Size and order of MPC Controller
`trim`	Compute steady-state value of MPC controller plant model state for given inputs and outputs

`d2d`	Change sampling time of an MPC controller
`ss`	Convert unconstrained MPC controller to state-space linear system form
`tf`	제약 조건이 없는 MPC 제어기를 선형 전달 함수 형식으로 변환
`zpk`	Convert unconstrained MPC controller to zero/pole/gain form

Review Model Predictive Controller for Stability and Robustness Issues
You can detect potential issues with your MPC controller design at the command line and using MPC Designer.
Test MPC Controller Robustness Using MPC Designer
Test the robustness of your model predictive controller by simulating it with MPC Designer.

Compute Steady-State Output Sensitivity Gain
Compute the closed-loop, steady-state gain for each output when a sustained, unit disturbance is added to each output.
Extract Linear Controller from MPC Controller
Obtain a linear state-space model of an unconstrained MPC controller. You can use this model to analyze the frequency response and performance of the controller.
Compare Multiple Controller Responses Using MPC Designer
You can compare the time-domain and frequency-domain responses of multiple MPC controller designs using MPC Designer.
Adjust Input and Output Weights Based on Sensitivity Analysis
Compute numerical derivatives of a closed-loop cumulated performance index with respect to weights and use them to improve model predictive controller performance.
Understanding Control Behavior by Examining Optimal Control Sequence
You can analyze the optimal control sequence computed by a model predictive controller at each sample time.