Documentation

Gain Scheduling

Tuning of gain-scheduled controllers for nonlinear plants

A gain-scheduled controller is a controller whose gains are automatically adjusted as a function of time, operating condition, or plant parameters. Gain scheduling is a common strategy for controlling systems whose dynamics change with time or operating condition. Such systems include linear parameter-varying (LPV) systems and large classes of nonlinear systems. To tune gain-scheduled controllers in Simulink®, you represent the variable gain as a function of the scheduling variables using the tunableSurface command. For an overview of the workflow for tuning gain-scheduled controllers, see Gain Scheduling Basics.

Functions

tunableSurfaceCreate tunable gain surface for gain scheduling
polyBasisPolynomial basis functions for tunable gain surface
fourierBasisFourier basis functions for tunable gain surface
ndBasisBasis functions for tunable gain surface
viewSurfVisualize gain surface as a function of scheduling variables
evalSurfEvaluate gain surfaces at specific design points
getDataGet current values of tunable-surface coefficients
setDataSet values of tunable-surface coefficients
slTunerInterface for control system tuning of Simulink models
slTunerOptionsSet slTuner interface options
systune (slTuner)Tune control system parameters in Simulink using slTuner interface
varyingGoalVariable tuning goal for gain-scheduled controllers
getGoalEvaluate variable tuning goal at specified design point

Blocks

Varying Lowpass FilterButterworth filter with varying coefficients
Varying Notch FilterNotch filter with varying coefficients
PID ControllerSimulate continuous- or discrete-time PID controllers
PID Controller (2 DOF)Simulate continuous- or discrete-time two-degree-of-freedom PID controllers
Varying Transfer FunctionTransfer function with varying coefficients
Varying State SpaceState-space model with varying matrix values
Varying Observer FormObserver-form state-space model with varying matrix values
Discrete Varying LowpassDiscrete Butterworth filter with varying coefficients
Discrete Varying NotchDiscrete-time notch filter with varying coefficients
Discrete PID ControllerSimulate continuous- or discrete-time PID controllers
Discrete PID Controller (2 DOF)Simulate continuous- or discrete-time two-degree-of-freedom PID controllers
Discrete Varying Transfer FunctionDiscrete-time transfer function with varying coefficients
Discrete Varying State SpaceDiscrete-time state-space model with varying matrix values
Discrete Varying Observer FormDiscrete-time observer-form state-space model with varying matrix values

Topics

Gain-Scheduled Control Systems

Gain Scheduling Basics

Gain scheduling is an approach to control of non-linear systems using a family of linear controllers, each providing satisfactory control for a different operating point of the system.

Model Gain-Scheduled Control Systems in Simulink

In Simulink, model gain schedules using lookup tables, interpolation blocks, or MATLAB Function blocks.

Tune Gain Schedules

Tune Gain Schedules in Simulink

Understand the general tuning workflow for using systune to tune gain-scheduled controllers.

Plant Models for Gain-Scheduled Controller Tuning

To tune a gain-scheduled control system, you need a collection of linear models describing the plant dynamics at the selected design points.

Multiple Design Points in slTuner Interface

For tuning a gain-scheduled control system, associate a family of linear plant models with the slTuner interface to your Simulink model.

Parameterize Gain Schedules

A gain surface parameterizes a variable gain in terms of the scheduling variables. Use gain surfaces to model variable gains in a gain-scheduled control system.

Change Requirements with Operating Condition

When tuning gain-scheduled controllers, you can specify tuning objectives that depend on the scheduling variables.

Validate Gain-Scheduled Control Systems

Tuning gain-scheduled controllers guarantees suitable performance only near each design point. It is important to validate the tuning results over the full range of operating conditions.

Featured Examples

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