Premium
Stable neural control of uncertain multivariable systems
Author(s) -
Mears Mark J.,
Polycarpou Marios M.
Publication year - 2003
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.761
Subject(s) - control theory (sociology) , multivariable calculus , artificial neural network , lyapunov function , bounded function , simple (philosophy) , sliding mode control , stability (learning theory) , computer science , control system , function (biology) , mathematics , control (management) , nonlinear system , control engineering , engineering , artificial intelligence , machine learning , mathematical analysis , philosophy , physics , electrical engineering , epistemology , quantum mechanics , evolutionary biology , biology
Tracking control of a class of non‐linear, uncertain, multi‐input, multiple‐output systems is addressed in this paper. The control system architecture uses neural networks for function approximation, certainty equivalent control inputs to cancel plant dynamics and smoothed sliding mode control to insure that the trajectories remain bounded. Lyapunov analysis is used to derive equations for the sliding mode control, neural network training, and to show uniform ultimate boundedness of the closed‐loop system. Stability analysis results are shown for single‐input single‐output and two‐input two‐output systems. Results are then extended to the more general multiple‐input multiple‐output case where the number of inputs is equal to the number of outputs. Simple simulation examples are used to illustrate control system performance. Copyright © 2003 John Wiley & Sons, Ltd.