Abstract
The dynamic response of an adaptive fuzzy neural network (FNN) controlled quick-return mechanism, which is driven by a permanent magnet (PM) synchronous servo motor, is described in this study. The crank and disk of the quick-return mechanism are assumed to be rigid. First, Hamilton’s principle and Lagrange multiplier method are applied to formulate the mathematical model of motion. Then, based on the principle of computed torque, an adaptive controller is developed to control the position of a slider of the quick-return servomechanism. Moreover, since the selection of control gain of the adaptive controller has a significant effect on the system performance, an adaptive FNN controller is proposed to control the quick-return servomechanism. In the proposed adaptive FNN controller, an FNN is adopted to facilitate the adjustment of control gain on line. Simulated and experimental results due to periodic step and sinusoidal commands show that the dynamic behavior of the proposed adaptive FNN control system are robust with regard to parametric variations and external disturbances.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: Journal of Dynamic Systems, Measurement, and Control
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.