A novel hybrid Fuzzy-PID controller for tracking control of robot manipulators

In this paper, a novel hybrid fuzzy proportional-integral-derivative (PID) controller based on learning automata for optimal tracking of robot systems including motor dynamics is presented. Learning automata is used at the supervisory level for adjustment of the parameters of hybrid Fuzzy-PID controller during the system operation. The proposed method has better convergence rate in comparison with standard back-propagation algorithms, less computational requirements than adaptive network based fuzzy inference systems (ANFIS) or neural based controllers and having the ability of working in uncertain environments without any previous knowledge of environments' parameters. The proposed controller has been successfully applied in simulation to control a 6-DOF Puma 560 manipulator using robotic toolbox, and has satisfactory results. In this simulation also, external disturbance and noise are addressed. The result of simulation has also shown that the rate of convergence and robustness of the designed controller guarantees practical stability.