title: Neural-network-based decentralized adaptive output-feedback control for large-scale stochastic nonlinear systems.
abstract: This paper focuses on the problem of neural-network-based decentralized adaptive output-feedback control for a class of nonlinear strict-feedback large-scale stochastic systems. The dynamic surface control technique is used to avoid the explosion of computational complexity in the backstepping design process. A novel direct adaptive neural network approximation method is proposed to approximate the unknown and desired control input signals instead of the unknown nonlinear functions. It is shown that the designed controller can guarantee all the signals in the closed-loop system to be semiglobally uniformly ultimately bounded in a mean square. Simulation results are provided to demonstrate the effectiveness of the developed control design approach.
authors.affiliation: Intelligent Systems and Biomedical Robotics Group, School of Creative Technologies, University of Portsmouth, Portsmouth, UK. zhouqi2009@gmail.com
authors.forename: Qi
authors.initials: Q
authors.lastname: Zhou
authors.forename: Peng
authors.initials: P
authors.lastname: Shi
authors.forename: Honghai
authors.initials: H
authors.lastname: Liu
authors.forename: Shengyuan
authors.initials: S
authors.lastname: Xu
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D005246:Feedback; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D013269:Stochastic Processes