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Issues on stability of ADP feedback controllers for dynamical systems.

S N Balakrishnan¹, Jie Ding, Frank L Lewis

¹Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology, Rolla, MO 65401, USA. bala@mst.edu

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

|July 18, 2008

Summary

This study reviews neural-network (NN)-based feedback controllers using adaptive/approximate dynamic programming (ADP). It highlights advancements in NN structures and their closed-loop stability, noting a growing body of research in this area.

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Area of Science:

Control Systems Engineering
Artificial Intelligence
Machine Learning

Background:

Neural-network (NN)-based feedback controllers are increasingly important in complex control systems.
Adaptive/Approximate Dynamic Programming (ADP) provides a theoretical framework for developing intelligent controllers.
Ensuring closed-loop stability is a critical challenge in NN-based control.

Purpose of the Study:

To trace the development of NN-based feedback controllers derived from ADP principles.
To discuss the closed-loop stability of these NN-based controllers.
To review different NN structures and ADP applications in control literature.

Main Methods:

Literature review of NN-based feedback controllers and ADP.
Analysis of various NN structures, including adaptive critics and action-critic networks.
Examination of model-free and model-based neurocontroller developments concerning stability.

Main Results:

Different NN structures for ADP-based controllers have been developed.
The paper distinguishes between two main classes of ADP applications.
Contributions to stability issues in both model-free and model-based approaches are reviewed.

Conclusions:

Research in ADP-based feedback controllers with assured stability is expanding.
Diverse forms of NN-based controllers are emerging in the literature.
The integration of ADP principles with NNs offers promising avenues for robust control systems.