Abstract
ABSTRACTIn this paper, a self-recurrent wavelet neural network (SRWNN)-based indirect adaptive control architecture is modified for performing speed control of a motion platform. The transient behaviour of the original learning algorithm has been improved by modifying the learning rate updates. The contribution of the proposed modification has been verified via both simulations and experiments. Moreover, the performance of the proposed architecture is compared with robust RST designs performed on a similar benchmark system, to show that via adaptive nonlinear control, it is possible to obtain a fast step response without degrading the robustness of a multi-body mechanical system. Finally, the architecture is further improved so as to possess structural learning for populating the SRWNNs automatically, rather than employing static network structures, and simulation results are provided to show the performance of the proposed structural learning algorithm.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
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.