access icon free Neural network-based output-feedback control for stochastic high-order non-linear time-delay systems with application to robot system

This study is concerned with the output-feedback control problem for a class of stochastic high-order non-linear systems with time-varying delays. A distinctive feature of the control scheme is that the restrictions on delay-dependent drift and diffusion terms are greatly relaxed by using radial basis function neural network (NN) approximation approach. Furthermore, with the approach, the specific knowledge of NN nodes and weights is not required. Under some weaker conditions, by combining dynamic surface control technique with proper Lyapunov–Krasovskii functional, an adaptive NN output-feedback controller is designed constructively such that the closed-loop system is 4-moment (or mean square) semi-globally uniformly ultimately bounded. Finally, the control scheme is applied to both a practical stochastic robot system and a numerical system to demonstrate the effectiveness of the proposed approach.

Inspec keywords: delays; stochastic systems; neurocontrollers; feedback; closed loop systems; nonlinear control systems; mobile robots; radial basis function networks; adaptive control

Other keywords: neural network-based output-feedback control problem; dynamic surface control technique; radial basis function neural network approximation approach; adaptive NN output-feedback controller; stochastic high-order nonlinear time-delay systems; Lyapunov-Krasovskii functional; closed-loop system; practical stochastic robot system; time-varying delays

Subjects: Nonlinear control systems; Time-varying control systems; Self-adjusting control systems; Neurocontrol; Distributed parameter control systems; Mobile robots

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