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Adaptive Control Design

Design controllers that can adapt to changing process information

When a control system contains uncertainties that change over time, such as unmodeled system dynamics and disturbances, an adaptive controller can compensate for the changing process information by adjusting its parameters in real time. By doing so, such a controller can achieve desired reference tracking despite the uncertainties in the plant dynamics.

Simulink® Control Design™ software provides the following real-time adaptive control methods for computing controller parameters.

Blocks

Extremum Seeking ControlCompute controller parameters in real time by maximizing objective function
Model Reference Adaptive ControlCompute control actions to make a controlled system track a reference model

Topics

Extremum Seeking Control

Extremum Seeking Control

Update controller parameters to maximize an objective function in the presence of unknown system dynamics.

Extremum Seeking Control for Reference Model Tracking of Uncertain Systems

Track a reference plant model by adapting feedforward and feedback gains for an uncertain dynamical system.

Anti-Lock Braking Using Extremum Seeking Control

Design an extremum seeking controller that maximizes the friction coefficient of an ABS system to achieve the shortest stopping distance.

Adaptive Cruise Control Using Extremum Seeking Control

Design an extremum seeking controller to adjust controller gains for an adaptive cruise control system.

Model Reference Adaptive Control

Model Reference Adaptive Control

Compute control actions to make an uncertain controlled system track the behavior of a given reference plant model.

Model Reference Adaptive Control of Satellite Spin

Design MRAC controller that adapts plant uncertainty model parameters to achieve performance that matches an ideal reference model.

Model Reference Adaptive Control of Aircraft Undergoing Wing Rock

Design MRAC controller that adapts wing-rock disturbance model parameters to achieve performance that matches an ideal reference model.