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Related Experiment Videos

Adaptive hybrid intelligent control for uncertain nonlinear dynamical systems.

Chi-Hsu Wang¹, Tsung-Chih Lin, Tsu-Tian Lee

¹Sch. of Microelectron. Eng., Griffith Univ., Brisbane, Qld., Australia.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|February 5, 2008

Summary

A novel hybrid adaptive fuzzy neural network (FNN) controller enhances control of uncertain nonlinear systems. This adaptive FNN controller ensures system stability and improves tracking performance with reduced error and control effort.

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Area of Science:

Control Engineering
Artificial Intelligence
Nonlinear Dynamics

Background:

Uncertain nonlinear dynamic systems present significant control challenges.
Existing adaptive control methods often struggle with complex system dynamics and parameter uncertainties.
Fuzzy neural networks (FNNs) offer a powerful framework for approximating complex nonlinear functions.

Purpose of the Study:

To develop a hybrid direct/indirect adaptive fuzzy neural network (FNN) controller for uncertain nonlinear dynamic systems.
To incorporate a state observer and a supervisory controller for enhanced stability and performance.
To demonstrate the controller's effectiveness in tracking sinusoidal signals in challenging nonlinear systems.

Main Methods:

A hybrid adaptive FNN controller combining direct and indirect adaptive approaches is proposed.
An observer-based output feedback control law and adaptive law tune the controller's free parameters online.
A supervisory controller is integrated to guarantee system stability, activating only when necessary.

Main Results:

The proposed hybrid adaptive FNN controller ensures global stability, with all signals uniformly bounded.
Simulations on an inverted pendulum and Chua's chaotic circuit demonstrate superior tracking performance compared to conventional methods.
The controller achieves smaller tracking errors and reduced control effort, offering greater design flexibility.

Conclusions:

The developed hybrid direct/indirect adaptive FNN controller effectively manages uncertain nonlinear dynamic systems.
The integration of a supervisory controller guarantees system stability, enhancing robustness.
The approach offers a flexible and high-performance solution for complex control applications.