Adaptive neural-network-based sliding mode control of switching distributed delay systems with Markov jump parameters

Baoping Jiang; Hamid Reza Karimi; Xin Zhang; Zhengtian Wu

doi:10.1016/j.neunet.2023.06.022

Adaptive neural-network-based sliding mode control of switching distributed delay systems with Markov jump parameters

Neural Netw. 2023 Aug:165:846-859. doi: 10.1016/j.neunet.2023.06.022. Epub 2023 Jun 22.

Authors

Baoping Jiang¹, Hamid Reza Karimi², Xin Zhang³, Zhengtian Wu⁴

Affiliations

¹ School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China; Department of Mechanical Engineering, Politecnico di Milano, 20156 Milan, Italy. Electronic address: bpjiang@usts.edu.cn.
² Department of Mechanical Engineering, Politecnico di Milano, 20156 Milan, Italy. Electronic address: hamidreza.karimi@polimi.it.
³ School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China. Electronic address: xwzj@usts.edu.cn.
⁴ School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China. Electronic address: wzht8@usts.edu.cn.

PMID: 37423030
DOI: 10.1016/j.neunet.2023.06.022

Abstract

This paper is devoted to the issue of observer-based adaptive sliding mode control of distributed delay systems with deterministic switching rules and stochastic jumping process, simultaneously, through a neural network approach. Firstly, relying on the designed Lebesgue observer, a sliding mode hyperplane in the integral form is put forward, on which a desired sliding mode dynamic system is derived. Secondly, in consideration of complexity of real transition rates information, a novel adaptive dynamic controller that fits to universal mode information is designed to ensure the existence of sliding motion in finite-time, especially for the case that the mode information is totally unknown. In addition, an observer-based neural compensator is developed to attenuate the effectiveness of unknown system nonlinearity. Thirdly, an average dwell-time approach is utilized to check the mean-square exponential stability of the obtained sliding mode dynamics, particularly, the proposed criteria conditions are successfully unified with the designed controller in the type of mode information. Finally, a practical example is provided to verify the validity of the proposed method.

Keywords: Distributed delay systems; Neural network; Sliding mode control; Switching systems.

MeSH terms

Computer Communication Networks*
Motion
Neural Networks, Computer*