Abstract
SummaryIn this paper, an observer‐based adaptive neural output‐feedback control scheme is developed for a class of nonlinear stochastic nonstrict‐feedback systems with input saturation in finite‐time interval. The mean value theorem and the property of the smooth function are applied to cope with the difficulties caused by the existence of input saturation. According to the universal approximation capability of the radial basis function neural network, it will be utilized to compensate the unknown nonlinear functions. Based on the state observer, the finite‐time Lyapunov stability theorem, we propose an adaptive neural output‐feedback control scheme for nonlinear stochastic systems in nonstrict‐feedback form. The developed controller guarantees that the system output signal can track the given reference signal trajectory, and all closed‐loop signals are semi‐globally finite‐time stability in probability. The observer errors and the tracking error can converge to a small neighborhood of the origin. Finally, simulation results demonstrate the effectiveness of the developed control scheme.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: International Journal of Adaptive Control and Signal Processing
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.