Abstract
This paper considers a novel parallel control algorithm of Best Weight CMAC (Cerebella Model Articulation Controller) and PD (Proportional Derivative) for the actuator electric load simulator of Unmanned Aerial Vehicles (UAVs). Although the conventional parallel controller can effectively reduce loading error and restrain the surplus torque of load simulator, it could destabilize the system due to overlearning of CMAC when tracking continuously variable loading signals like sinusoidal instructions. The overlearning issue of CMAC is investigated in detail and its divergent trend is predicted. A novel weight updating rule of CMAC, Best Weight Method, is proposed and its convergence is theoretically analyzed. This novel parallel controller can not only retain the normal learning process of conventional parallel controller, but also avoid the overlearning of CMAC. The experimental results have demonstrated that the proposed controller has good robustness, can effectively reduce the loading error, eliminate the surplus torque and assure the stability of system as well.
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 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.
