Abstract
This paper addresses the problem of adaptive compensation event-triggered control for uncertain nonlinear systems with input hysteresis. The communication resources cannot be saved effectively for the considered systems, as traditional event-triggered mechanisms do not consider input hysteresis with nonlinear gain. In a network control system, how to simultaneously overcome the problem of input hysteresis and save communication resources remains a challenge. In this paper, an extended fuzzy approximation method is proposed to estimate uncertain items for control design. The method considers the time-varying error of the approximation instead of viewing the error as a bounded constant. Furthermore, in combination with the extended fuzzy approximation method, an adaptive event-triggered compensation control method for uncertain nonlinear systems with input hysteresis is designed. Under the proposed event-triggered mechanism, the control method can effectively compensate for the input hysteresis and simultaneously save communication resources. Finally, this method is verified through two simulation experiments that the occupation of communication source has been reduced and the stability of the considered system can be guaranteed effectively.
Highlights
Input hysteresis is always present in practical control systems, impairing the stability of the control system [1]–[4]
Adaptive control based on the Prandtl-Ishlinskii(PI) hysteresis model was designed for continuous-time linear systems in [1]
We propose an extended fuzzy adaptive compensation event-triggered control method with the following two main innovations: (1) Input hysteresis with nonlinear control gain is inevitable in actual systems and has a substantial influence on system performance, see [1]–[9]
Summary
Input hysteresis is always present in practical control systems, impairing the stability of the control system [1]–[4]. Many studies have developed methods to address this issue [5]–[9]. Hysteresis model compensation is the main approach. Adaptive control based on the Prandtl-Ishlinskii(PI) hysteresis model was designed for continuous-time linear systems in [1]. Considering a class of systems with hysteresis input, a neural robust adaptive control method was proposed [7]. The aforementioned methods assume that the control gain is constant. The gain is a nonlinear function in a physical control system [4]
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