Output Tracking Control via Neural Networks for High-Order Stochastic Nonlinear Systems with Dynamic Uncertainties

Shan Shan Feng, Zong Yao Sun, Cheng Qian Zhou, Chih Chiang Chen, Qinghua Meng

Research output: Contribution to journalArticlepeer-review

Abstract

This paper is concerned with the problem of output tracking control for a class of high-order stochastic nonlinear systems with dynamic uncertainties. The systems under investigation have dynamic uncertainties, unknown high-order terms, and uncertain nonlinear functions simultaneously. The packaged unknown nonlinearities are manipulated successful by using radial basis function neural networks. Two dynamic signals are introduced to dominate the dynamic uncertainties and adjust the tracking accuracy, respectively. The proposed continuous controller guarantees that all states of the closed-loop system are bounded in probability, and the tracking error converges to a preassigned range. Finally, a simulation example is provided to demonstrate the effectiveness of the control scheme.

Original languageEnglish
JournalInternational Journal of Fuzzy Systems
DOIs
Publication statusAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Computational Theory and Mathematics
  • Artificial Intelligence

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