Deterministic learning and neural control of a class of nonlinear systems toward improved performance
 Binhe Wen,
 Cong Wang,
 Tengfei Liu
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A deterministic learning theory was recently presented which states that an appropriately designed adaptive neural controller can learn the system internal dynamics while attempting to control a class of nonlinear systems in normal form. In this paper, we further investigate deterministic learning of the class of nonlinear systems with relaxed conditions, and neural control of the class of system toward improved performance. Firstly, without the assumption on the upper bound of the derivative of the unknown affine term, an adaptive neural controller is proposed to achieve stability and tracking of the plant states to that of the reference model. When output tracking is achieved, a partial PE condition is satisfied, and deterministic learning from adaptive neural control of the class of nonlinear systems is implemented without the priori knowledge on the upper bound of the derivative of the affine term. Secondly, by utilizing the obtained knowledge of system dynamics, a neural controller with constant RBF networks embedded is presented, in which the learned knowledge can be effectively exploited to achieve stability and improved control performance. Simulation studies are included to demonstrate the effectiveness of the results.
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 Title
 Deterministic learning and neural control of a class of nonlinear systems toward improved performance
 Journal

Neural Computing and Applications
Volume 24, Issue 34 , pp 637648
 Cover Date
 20140301
 DOI
 10.1007/s0052101212292
 Print ISSN
 09410643
 Online ISSN
 14333058
 Publisher
 Springer London
 Additional Links
 Topics
 Keywords

 Deterministic learning
 Inputtostate stability
 Smallgain theorem
 Adaptive neural control
 Learning control
 Industry Sectors
 Authors

 Binhe Wen ^{(1)}
 Cong Wang ^{(1)}
 Tengfei Liu ^{(2)}
 Author Affiliations

 1. School of Automation and the Center for Control and Optimization, South China University of Technology, Guangzhou, 510641, People’s Republic of China
 2. Department of Electrical and Computer Engineering, Six Metrotech Center, Polytechnic Institute, New York University, Brooklyn, NY, 11201, USA