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 hystere...