A novel neural network (NN) -based output feedback controller with magnitude constraints is designed to deliver a desired tracking performance for a class of multi-input-multi-output (MIMO) discrete-time strict feedback nonlinear systems. Reinforcement learning in discrete time is proposed for the output feedback controller, which uses three NN: 1) a NN observer to estimate the system states with the input-output data; 2) a critic NN to approximate certain strategic utility function; and 3) an action NN to minimize both the strategic utility function and the unknown dynamics estimation errors. The magnitude constraints are manifested as saturation nonlinearities in the output feedback controller design. Using the Lyapunov approach, the uniformly ultimate boundedness (UUB) of the state estimation errors, the tracking errors and weight estimates is shown.


Electrical and Computer Engineering

Keywords and Phrases

Reinforcement learning; , Neural networks (NNs); Output feedback control; Artificial intelligence; Lyapunov functions; MIMO systems; Neural networks (Computer science); Nonlinear control theory

International Standard Serial Number (ISSN)


Document Type

Article - Journal

Document Version

Final Version

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© 2004 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Feb 2005