Abstract
Cerebellar model articulation controller (CMAC) has been already validated that it can approximate a nonlinear function over a domain of interest to any desired accuracy. This paper proposes an adaptive CMAC (PIACMAC) system with a PI-type learning algorithm. The PIACMAC system is composed of a CMAC and a compensation controller. CMAC is used to mimic an ideal controller and the compensation controller is designed to dispel the approximation error between CMAC and ideal controller. The Lyapunov stability theorems is utilized to derive the parameter learning algorithm, so that the uniformly ultimately bounded of PIACMAC system can be guaranteed. Then, the PIACMAC system is applied to a Duffing-Holmes chaotic system. Simulation results verify that the proposed PIACMAC system with a PI-type learning algorithm can achieve better control performance than other control methods.
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 callDisclaimer: 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.
