Adaptive robot manipulator control based on plant-controller model reference using soft computing and performance index analyzer

This paper addresses an application that involves the adaptive control of robot manipulator joint. It tries to explore the potential of using soft computing methodologies in control of plant (robot manipulator) with unknown internal behavior and environmental changes. The main methodology is based on using dual controller instead of one. One of them is for controlling identified plant (plant model) which used as model reference, and the other one for controlling the real plant (manipulator). The real controller presents a PID tuning method that uses a fuzzy logic as a main gain estimator and a signal analyzer, which extract some performance indexes (overshoot, rise time and steady state error) from controller error signal. By defining these PI parameters and the one which is available as a reference (from reference model), the controller tries to match its output to reach the desired PIs, come from model reference. The suggested approach is used to tune the PID gains for different response specifications. Experimental results demonstrate that better performance can be achieved with suggested adaptive controller. Robot manipulator which was used as case study in this work was simulated with professional simulation software package consist of Matlab/Simulink, Solid work and Visual Nastran.