Event-Triggered, Adaptive, Exponentially Asymptotic Tracking Control of Stochastic Nonlinear Systems

Furong He,Jing Li,Jian Wu,Dewen Cao

doi:10.3390/sym14030451

Furong He, Jing Li + Show 2 more

Open Access

https://doi.org/10.3390/sym14030451

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Journal: Symmetry	Publication Date: Feb 24, 2022
Citations: 2	License type: CC BY 4.0

Affiliation: Anqing Normal University, Xidian University

Abstract

This paper investigates the problem of event-triggered, adaptive, asymptotic tracking control for a class of non-strict feedback stochastic nonlinear systems with symmetrical structures and sensor faults. Based on the negative exponential function, the event-triggered adaptive tracking control strategy deals with the problem of exponentially asymptotic convergence for the first time. The radial basis function neural network (RBFNN) mechanism addresses uncertain factors and unknown external disturbances in the system. The developed strategy ensures that all the signals of the closed-loop system are semi-globally uniformly bounded in probability, and that the tracking error can exponentially converge to zero. Finally, a simulation example demonstrates the effectiveness of the proposed method.

Highlights

Subject to the growing demand from the development of practice and theory, many adaptive tracking control problems for stochastic nonlinear systems [1,2,3,4,5,6,7,8,9,10,11,12,13] are being studied
Li addresses the problem of adaptive tracking control for non-strict feedback stochastic nonlinear systems with dead-zones and output constraints [13]
In order to demonstrate the superiority of the event triggering mechanism, we present the quantitative analysis of the data from the experimental results in Table 1, where the integrated absolute error (IAE) and integrated time absolute error (ITAE), defined as

Summary

Introduction

Subject to the growing demand from the development of practice and theory, many adaptive tracking control problems for stochastic nonlinear systems [1,2,3,4,5,6,7,8,9,10,11,12,13] are being studied. For the purpose of addressing the problem of limited transmission resources and achieving asymptotic convergence performance, Liu proposes the event-triggered, fuzzy adaptive compensation control design for uncertain stochastic nonlinear systems [34]. For the exponential asymptotic convergence method, an adaptive control scheme proposed in [42] for non-stochastic systems achieves asymptotic zero-error tracking It employs a periodic time-triggered mechanism, greatly occupying communication resources, and does not consider the possibility of sensor faults occurring. To address this problem, this paper designs the controller with the event-triggered mechanism to reduce the communication resource occupation rate, which is more suitable for practical application scenarios. It is assumed that hi(t) > 0, and that there exist positive constants, hi and hi, satisfying 0 < hi ≤ hi(t) ≤ hi

Controller Design and Stability Analysis

Conclusions