Neural network adaptive control of nonlinear systems preceded by hysteresis
Source abstract
Abstract
Neural network adaptive control is proposed for a class of nonlinear system preceded by hysteresis. A novel model is developed to represent the hysteresis characteristics in explicit form. Furthermore, the auxiliary variable of the proposed model is proved to be bounded, which is essential for controller design. Then, neural network adaptive controller is directly applied to mitigate the influence of the hysteresis without constructing the hysteresis inverse. The updated law and control law of the controllers are derived from Lyapunov stability theorem, so that the boundedness of the close-loop system is guaranteed. Finally, the experimental tests are carried out to validate the effectiveness of the proposed approach.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Piezoelectric Actuators and Control · Shape Memory Alloy Transformations · Magnetic Properties and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Mechanical Engineering and Automation, Zhejiang Sci-Tech University, Hangzhou, Zhejiang, China
College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, China
Funding
National Natural Science Foundation of China
References (42)
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