Event-Triggered Neural Control of Nonlinear Systems With Rate-Dependent Hysteresis Input Based on a New Filter

• Published in: IEEE transaction on neural networks and learning systems Vol. 31; no. 4; pp. 1270 - 1284
• Main Authors: Lu, Kaixin, Liu, Zhi, Chen, C. L. Philip, Zhang, Yun
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Actuators; Adaptive control; Adaptive filters; Artificial neural networks; Backstepping; Computer simulation; Control design; Control methods; Control stability; Control systems; Design; Design modifications; event-triggered; Hysteresis; networked control systems (NCSs); neural network (NN); Neural networks; Nonlinear control; Nonlinear systems; Nonlinearity; rate-dependent nonlinearity; Tracking errors
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2019.2919641

In controlling nonlinear uncertain systems, compensating for rate-dependent hysteresis nonlinearity is an important, yet challenging problem in adaptive control. In fact, it can be illustrated through simulation examples that instability is observed when existing control methods in canceling hysteresis nonlinearities are applied to the networked control systems (NCSs). One control difficulty that obstructs these methods is the design conflict between the quantized networked control signal and the rate-dependent hysteresis characteristics. So far, there is still no solution to this problem. In this paper, we consider the event-triggered control for NCSs subject to actuator rate-dependent hysteresis and failures. A new second-order filter is proposed to overcome the design conflict and used for control design. With the incorporation of the filter, a novel adaptive control strategy is developed from a neural network technique and a modified backstepping recursive design. It is proved that all the control signals are semiglobally uniformly ultimately bounded and the tracking error will converge to a tunable residual around zero.