Adaptive neural control of stochastic nonlinear systems with multiple time-varying delays and input saturation

• Published in: Neural computing & applications Vol. 25; no. 3-4; pp. 779 - 791
• Main Authors: Cui, Guozeng, Jiao, Ticao, Wei, Yunliang, Song, Gongfei, Chu, Yuming
• Format: Journal Article
• Language: English
• Published: London Springer London 01.09.2014; Springer
• Subjects: Adaptative systems; Applied sciences; Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Computer science; control theory; systems; Connectionism. Neural networks; Control theory. Systems; Data Mining and Knowledge Discovery; Exact sciences and technology; Image Processing and Computer Vision; Original Article; Probability and Statistics in Computer Science; System theory; Input saturation; Adaptive neural control; Razumikhin lemma; Time-varying delay; Stochastic nonlinear systems; Non linear control; Feedback regulation; Function approximation; Adaptive control; Stochastic system; Time varying system; Uncertain system; Learning algorithm; Capability index; Closed loop; Probabilistic approach; Delay system; Neural network; Non linear system; Recursive algorithm; Backstepping control; Closed feedback; Neurocontrollers; Artificial intelligence; Lyapunov function

This paper investigates the problem of adaptive neural control for a class of strict-feedback stochastic nonlinear systems with multiple time-varying delays, which is subject to input saturation. Via the backstepping technique and the minimal learning parameters algorithm, the problem is solved. Bas...