FPGA implementation of a fuzzy controller for neural network based adaptive control of a flexible joint with hard nonlinearities
A control strategy based on artificial networks (ANN) has been proposed for a positioning system with a flexible transmission element, taking into account Coulomb friction for both motor and load, and using a variable learning rate for adaptation to parameter changes and to accelerate convergence. The control structure consists of an ANN that approximates the inverse of the model and of a reference model which defines the desired error dynamics. A fuzzy rule based supervisor for on-line adaptation of the reference model bandwidth parameter is used to accelerate the convergence rate of the controller and enhance the stability to the system. The fuzzy controller is implemented on a Virtex2 Pro 2VP30 Field Programmable Gate Array (FPGA) from Xilinx. A pipelined implementation is used to speed-up the process. Simulation results highlight the performance of the controller.
|Conference||International Symposium on Industrial Electronics 2006, ISIE 2006|
Chaoui, H, Yagoub, M.C.E. (Mustapha C.E.), & Sicard, P. (Pierre). (2006). FPGA implementation of a fuzzy controller for neural network based adaptive control of a flexible joint with hard nonlinearities. In IEEE International Symposium on Industrial Electronics (pp. 3124–3129). doi:10.1109/ISIE.2006.296115