미세 조정

사용자 지정 외란 모델, 사용자 지정 상태 추정기, 종단 가중치, 사용자 지정 제약 조건 지정

플랜트에 대한 모델 예측 제어기를 생성한 후에는 MPC 디자이너 앱을 사용하거나 명령줄에서 시스템의 폐루프 응답을 조정할 수 있습니다.

함수

`get`	Get property values from MPC object
`getname`	MPC 플랜트 모델에서 I/O 신호 이름 가져오기
`set`	Set or modify MPC object properties
`setname`	Set I/O signal names in MPC plant model

`getconstraint`	Obtain mixed input/output constraints from model predictive controller
`setconstraint`	Set mixed input/output constraints for model predictive controller
`setterminal`	Terminal weights and constraints

`getEstimator`	Obtain Kalman gains and model for estimator design
`setEstimator`	Modify a model predictive controller’s state estimator

`getindist`	Retrieve unmeasured input disturbance model
`getoutdist`	Retrieve unmeasured output disturbance model
`setindist`	Modify unmeasured input disturbance model
`setoutdist`	Modify unmeasured output disturbance model

`ActiveSet`	Options object for active-set QP solver used within an `mpc` object (R2024a 이후)
`InteriorPoint`	Options object for interior point QP solver used in `mpc` object (R2024a 이후)
`ADMM`	Options object for ADMM solver used in `mpc` object (R2024a 이후)
`BranchBound`	Options object for branch and bound MIQP solver used in `mpc` object (R2024a 이후)

`mpcActiveSetSolver`	Solve quadratic programming problem using active-set algorithm
`mpcActiveSetOptions`	Create default option set for `mpcActiveSetSolver`
`mpcInteriorPointSolver`	Solve a quadratic programming problem using an interior-point algorithm
`mpcInteriorPointOptions`	Create default option set for `mpcInteriorPointSolver`
`setCustomSolver`	Configures an MPC object to use the QP solver from Optimization Toolbox as a custom solver (R2021b 이후)

모델 예측 제어기 설계 및 시뮬레이션

Setting Targets for Manipulated Variables
If your plant has more manipulated variables than outputs, you can hold the excess manipulated variables at target values for economical or operational reasons.
Constraints on Linear Combinations of Inputs and Outputs

You can design and simulate a model predictive controller with mixed input/output constraints.
- Use Custom Constraints in Blending Process
Terminal Weights and Constraints

To achieve infinite horizon control, you can use terminal weights at the final prediction horizon step. To ensure stability for constrained systems, you might have to also define terminal constraints at the end of the prediction horizon.
- Provide LQR Performance Using Terminal Penalty Weights

Adjust Disturbance and Noise Models
MPC controllers model unknown events using input and output disturbance models, and measurement noise models.
Custom State Estimation
You can override the default MPC controller state estimation method by changing the default Kalman gains or by supplying your own controller state estimates.
Implement Custom State Estimator Equivalent to Built-In Kalman Filter
Design a state estimator equivalent to the linear Kalman filter of an MPC controller.
Use MPC with Extended State Observer to Reject Unmeasured Output Disturbances
Use an extended state observer to reject unmeasured disturbance at the plant output.

QP Optimization Problem for Linear MPC
Model predictive controllers compute optimal manipulated variable control moves by solving a quadratic program at each control interval.
QP Solvers for Linear MPC
The model predictive controller QP solvers convert an MPC optimization problem to a general form quadratic programming problem.
Manipulated Variable Blocking
You can improve the robustness of your controller and smooth manipulated variable adjustments by dividing the prediction horizon into a series of blocking intervals.
Specifying Alternative Cost Function with Off-Diagonal Weight Matrices
You can specify an alternative cost function for your model predictive controller to minimize during optimization.
Control Quarter-Car Suspension Dynamics Using ADMM Solver
Design an MPC controller which uses the Alternating Direction Method of Multipliers (ADMM) as a solver to control the dynamics of the active suspension of a quarter-car suspension system.