ROBOTIC CONTROL BY NEURO-FUZZY APPROACHES

Abstract

The problem of manipulator control is highly complex problem of controlling a system which is multi-input, multi-output, and non-linear and time variant. A number of different approaches presently followed for the control of manipulator vary from PID to very complex, intelligent, self-learning control algorithms. This report presents a comparative study of simulated performance of some conventional controllers, like the simple PID, Computed torque control, Feed forward inverse dynamic control and critically damped inverse dynamic control and some Intelligent controllers, like Fuzzy control, Neural control, and Neuro-Fuzzy control. IAE is used for comparison as performance index. The study concludes that the Critically damped inverse dynamics controller in general performs better then rest of conventional controllers. When the. unmodeled term is added to the model, PID and Feed forward inverse dynamic control perform badly. Computed torque control and Critically damped inverse dynamics control performance also effected but they do well. A Neuro-Fuzzy controller combines the advantage of neural networks (learning adaptability) with the advantage of fuzzy logic (use of expert knowledge) to achieve the goal of robust adaptive control of robot dynamics, performs better in intelligent controllers and also shows that intelligent controllers are better even when unmodeled terms are added to the model.