Safe control of logical control networks with random impulses

Rongpei Zhou; Yuqian Guo; Yuhao Wang; Zejun Sun; Xinzhi Liu

doi:10.1016/j.neunet.2023.06.035

Safe control of logical control networks with random impulses

Neural Netw. 2023 Aug:165:884-895. doi: 10.1016/j.neunet.2023.06.035. Epub 2023 Jul 3.

Authors

Rongpei Zhou¹, Yuqian Guo², Yuhao Wang³, Zejun Sun⁴, Xinzhi Liu⁵

Affiliations

¹ School of Information Engineering, Nanchang University, Nanchang, Jiangxi, 330031, China. Electronic address: zhourp@ncu.edu.cn.
² School of Automation, Central South University, Changsha, Hunan, 410083, China. Electronic address: gyuqian@csu.edu.cn.
³ School of Information Engineering, Nanchang University, Nanchang, Jiangxi, 330031, China. Electronic address: wangyuhao@ncu.edu.cn.
⁴ School of Information Engineering, Pingdingshan University, Pingdingshan, Henan, 467000, China. Electronic address: szj@pdsu.edu.cn.
⁵ Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1. Electronic address: xzliu@uwaterloo.ca.

PMID: 37433232
DOI: 10.1016/j.neunet.2023.06.035

Abstract

Under the framework of a hybrid-index model, this paper investigates safe control problems of state-dependent random impulsive logical control networks (RILCNs) on both finite and infinite horizons, respectively. By using the ξ-domain method and the constructed transition probability matrix, the necessary and sufficient conditions for the solvability of safe control problems have been established. Further, based on the technique of state-space partition, two algorithms are proposed to design feedback controllers such that RILCNs can achieve the goal of safe control. Finally, two examples are shared to demonstrate the main results.

Keywords: Logical control networks; Safe control problems; Semi-tensor product of matrices; State-dependent random impulses.

MeSH terms

Algorithms*
Feedback
Logic
Neural Networks, Computer*
Time Factors