Adaptive Control Design
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.
Extremum Seeking Control — Model-free adaptation to maximize an objective function derived from the control system
Model Reference Adaptive Control — Adaptation to track the output of a known reference model
Active Disturbance Rejection Control — Model-free adaptation to reject internal and external disturbances of a plant
|Extremum Seeking Control||Compute controller parameters in real time by maximizing objective function|
|Model Reference Adaptive Control||Compute control actions to make controlled system track reference model (Since R2021b)|
|Active Disturbance Rejection Control||Design controller for plants with unknown dynamics and disturbances (Since R2022b)|
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.
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 an MRAC controller that adapts plant uncertainty model parameters to achieve performance that matches an ideal reference model.
- Indirect Model Reference Adaptive Control of First-Order System
Design an indirect MRAC controller that estimates the properties of an unknown first-order system.
- Indirect MRAC Control of Mass-Spring-Damper System
Design an indirect MRAC controller that estimates the parameters of an unknown MIMO system.
Active Disturbance Rejection Control
- Active Disturbance Rejection Control
Design a disturbance rejection controller for plants with unknown dynamics and disturbances.
- Design Active Disturbance Rejection Control for Water-Tank System
Design ADRC for a water-tank model and compare performance against a gain-scheduled PID controller.
- Design Active Disturbance Rejection Control for BLDC Speed Control Using PWM
Design ADRC for a brushless DC motor speed controller using pulse width modulation.
- Design ADRC for Multi-Input Multi-Output Plant
Design ADRC for a pilot-scale distillation column MIMO model and compare performance against a model predictive controller. (Since R2023b)