Abstract
Model structure representation and fast estimation of perturbations are two key research aspects in adaptive control. This work proposes a composite local learning adaptive control framework, which possesses fast and flexible approximation to system uncertainties and meanwhile smoothens control inputs. Local learning, which is a nonparametric regression approach, is able to automatically adjust the structure of approximator based on data distribution from the local region, but it is sensitive to the outliers and measurement noises. To tackle this problem, the regression filter technique is employed to attenuate the adverse effect of noises by smoothing the output response and state features. In addition, the stable integral adaptation is integrated into local learning framework to further enhance the system robustness and smoothness of the estimation. Through the online elimination of uncertainties, the nominal control performance is recovered when the plant encounters violent perturbations. Stability analysis and numerical simulations are performed to demonstrate the effectiveness and benefits of the proposed control method. The proposed approach exhibits a promising performance in terms of rapid perturbation elimination and accurate tracking control.
Sign-in/Register to access full text options 
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 callMore From: IEEE Transactions on Neural Networks and Learning Systems
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.
