FPGA-Based Adaptive PID Controller Using MLP Neural Network for Tracking Motion Systems

Abstract

In this study, the Field Programmable Gate Array (FPGA) technology is employed to integrate multi-loop controllers for motion systems. To provide precise positioning and trajectory tracking for multi-axis systems, the proportional-integral (PI) control is used in the speed control loop and adaptive PID control in the position control loop. The motion system under consideration comprises an X-Y table driven by permanent magnet synchronous motors (PMSMs), and controlled by two programmable servo systems, each designed to regulate a separate axis. Each axis of this system consists of a motion planning module, a speed PI controller in the inner loop, and an adaptive PID position controller in the outer loop. The adaptive PID controller is specifically designed using a multilayer perceptron (MLP) neural network and parameter tuning methods. The control objective is to enhance trajectory tracking accuracy, especially in the presence of dynamic variations and uncertain disturbances. The Very High-speed IC Hardware Description Language (VHDL) is utilized to implement the desirable features of the control system. The control development is based on an FPGA device using Altera’s Quartus II and Nios II software environment. The VHDL designs are analyzed and synthesized within this software environment. Simulation results demonstrate that the on-chip control system can achieve accurate positioning and tracking performance for the X-Y table motion.

Keywords

• FPGA
• PMSM drives
• MLP neural network
• adaptive PID controller
• X-Y table