FPGA Implementation of Neural Network Based Adaptive Control of a Flexible Joint with Hard Nonlinearities

Abstract

An Artificial Neural Network (ANN) based model reference adaptive controller has been developed for a positioning system with a flexible transmission element, taking into account hard nonlinearities in the motor and load models. Due to the presence of Coulomb friction and of the flexible coupling, the inverse model of the system is not realizable. The ability of ANNs to approximate nonlinear functions is exploited to obtain an approximate inverse model for the positioning system and a reference model is used to define the desired error dynamics. The controller uses desired load position and velocity trajectories with measurement of load position, load velocity and motor velocity. The paper describes a VLSI implementation of the controller on a Virtex2 Pro 2VP30 Field Programmable Gate Array (FPGA) from Xilinx. A pipelined adaptation of the on-line back-propagation algorithm is used. The hardware implementation is capable of a high degree of parallelism and pipelining of neural networks allows the controller to operate at even higher speed. The FPGA implementation on the other hand allows fast prototyping and rapid system deployment. The controller can be used to improve both static and dynamic performance of electromechanical systems.