title: Robust neural network tracking controller using simultaneous perturbation stochastic approximation.
abstract: This paper considers the design of robust neural network tracking controllers for nonlinear systems. The neural network is used in the closed-loop system to estimate the nonlinear system function. We introduce the conic sector theory to establish a robust neural control system, with guaranteed boundedness for both the input/output (I/O) signals and the weights of the neural network. The neural network is trained by the simultaneous perturbation stochastic approximation (SPSA) method instead of the standard backpropagation (BP) algorithm. The proposed neural control system guarantees closed-loop stability of the estimation system, and a good tracking performance. The performance improvement of the proposed system over existing systems can be quantified in terms of preventing weight shifts, fast convergence, and robustness against system disturbance.
authors.affiliation: School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore. eqsong@ntu.edu.sg
authors.forename: Qing
authors.initials: Q
authors.lastname: Song
authors.forename: James C
authors.initials: JC
authors.lastname: Spall
authors.forename: Yeng Chai
authors.initials: YC
authors.lastname: Soh
authors.forename: Jie
authors.initials: J
authors.lastname: Ni
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D015203:Reproducibility of Results; D013269:Stochastic Processes