Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 33, Issue 11, Pages 6206-6214Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2021.3072784
Keywords
Hysteresis; Nonlinear systems; Adaptive systems; Control systems; Actuators; Control design; Mathematical model; Adaptive control; input hysteresis; neural networks; nonlinear systems; self-triggered
Categories
Funding
- Innovative Talents Project of Guangdong Education Department, China [2018KQNCX197]
- Natural Science Foundation of Guangdong Province, China [2019A1515110995]
- Science and Technology Planning Project of Guangzhou, China [202002030286]
- Science and Technology Project of Guangzhou University, China [YG2020009]
Ask authors/readers for more resources
This paper addresses the issue of neural adaptive self-triggered tracking control for uncertain nonlinear systems with input hysteresis. A new control approach is proposed, which effectively compensates for the hysteresis effect and bounds the tracking error.
The issue of neural adaptive self-triggered tracking control for uncertain nonlinear systems with input hysteresis is considered. Combining radial basis function neural networks (RBFNNs) and adaptive backstepping technique, an adaptive self-triggered tracking control approach is developed, where the next trigger instant is determined by the current information. Compared with the event-triggered control mechanism, its biggest advantage is that it does not need to continuously monitor the trigger condition of the system, which is convenient for physical realization. By the proposed controller, the hysteresis's effect can be compensated effectively and the tracking error can be bounded by an explicit function of design parameters. Simultaneously, all other signals in the closed-loop system can be remaining bounded. Finally, two examples are presented to verify the effectiveness of the proposed method.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available