Abstract
In this paper, we present a novel event-triggered control mechanism for nonlinear systems with hysteresis quantization. To describe the tendency of the dynamic quantizer parameter, an auxiliary parameter is created and the relationship between the quantizer parameter and the auxiliary parameter has been discussed. Via thoroughly analyzing the structure of the hysteresis quantizer, the hysteresis quantizer based event-triggered control mechanism is designed to appropriately decide the accurate signal transmission instants with simple calculation process. Moreover, under the proposed method, the nonlinear system could be stabilized and Zeno behavior could be intrinsically avoided. Furthermore, a numerical example is given to demonstrate the efficiency of the proposed mechanism.
Highlights
Networked control systems (NCSs) are the systems where the plant, sensor, controller and actuator are linked through communication network [1]–[4]
It is demonstrated that the system could be stabilized and the potential Zeno behavior could be eliminated by the proposed hysteresis quantizer based event-triggered control mechanism
Based on the structure of the considered hysteresis quantizer (3) described in Fig. 4, here, we propose the hysteresis quantizer based event-triggered control mechanism: tk+1 = inf{t > tk | q(x(t), (t)) = q(x(tk ),)} (13)
Summary
Networked control systems (NCSs) are the systems where the plant, sensor, controller and actuator are linked through communication network [1]–[4]. With its superiority in convenient installation, easy maintenance, high reliability and facilitate source sharing, networked control systems are about to replace the traditional point-to-point control systems and have been widely applied into many industrial fields, such as industrial process, medical treatment and aerospace industry [5]–[11]. Since the components are linked through communication network, we have to deal with the problem about how to choose appropriate instants to send signals through the network with less channel resource consumption. This paper considers a more general case where networked control systems are modeled in nonlinear form. One of the unavoidable components is the quantizer.
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