Robust Neural Network Tracking Controller Using Simultaneous Perturbation Stochastic Approximation

Qing Song, J.C. Spall, Yeng Chai Soh, Jie Ni
2008 IEEE Transactions on Neural Networks  
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
more » ... he 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. Index Terms-Conic sector, dead zone, neural network, simultaneous perturbation stochastic approximation (SPSA).
doi:10.1109/tnn.2007.912315 pmid:18467211 fatcat:6lhrbegek5fmtj52u5q35kg7d4