Abstract
A neural network approach to on-line learning control and real time implementation for a flexible micro-actuator is presented. The flexible micro-actuator is made of a bimorph piezo-electric high-polymer material (Poly Vinylidene Fluoride). The control scheme consists of a feedforward neural network controller and a fixed-gain feedback controller. This neural network controller is trained so as to make the output of the feedback controller zero. In the process, the neural network learns the inverse dynamics of the system. We make some comparisons between PID and LQG controllers for this neural network controller. Experimental and numerical results for the tracking control of a piezopolymer actuator are presented and they show that the feedback-error-learning neural network is effective in accurately tracking a reference signal.
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.
