Abstract

In this paper, the problem of prescribed performance adaptive neural tracking control design is investigated for strict-feedback stochastic nonlinear systems. The unknown nonlinear functions are tackled using the neural networks. Combining with prescribed performance function and backstepping technique, the desired adaptive controller is developed. It is proved that all the signals of the close-loop system are bounded in probability and the tracking error remains within a predefined arbitrarily small residual set with the prescribed performance bounds. Simulation results are provided to show the effectiveness of the proposed control method.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.