POSITION AND ORIENTATION CONTROL OF A 6-DOF ROBOT USING FEEDFORWARD ANFIS-PID CONTROLLER

Abstract

Robotic systems with six degrees of freedom (6-DOF) have become essential in high-precision tasks such as industrial welding and surgical operations. These systems necessitate sophisticated control strategies to address the complexities of nonlinear dynamics, actuator behaviors, and external disturbances. In this research, a feedforward Adaptive Neuro-Fuzzy Inference System (ANFIS)-PID controller was developed for the precise position and orientation control of a 6-DOF robotic manipulator. The kinematic model of the robot was formulated using the DenavitHartenberg (DH) convention, allowing for the derivation of forward and inverse kinematics. The accuracy of the kinematic model was verified through simulations conducted in MATLAB. A dynamic model, which integrated actuator dynamics for all six joints, was developed using MSC Adams and validated in a co-simulation environment. This high-fidelity model enabled the realistic simulation of the robot’s mechanical and dynamic behavior. The ANFIS-PID controller was designed and tested within a MATLAB/Simulink co-simulation environment, which interfaced seamlessly with the dynamic model from MSC Adams. The performance of the developed controller was evaluated in terms of trajectory tracking and disturbance rejection. Results indicated that the controller significantly outperformed traditional PID controllers, achieving position errors below 0.3° under normal and disturbed conditions. These findings highlighted the ANFIS-PID controller’s adaptability to nonlinear dynamics and superior performance in comparison to its conventional counterparts. Despite its successes, limitations were identified. Factors such as link elasticity and joint friction were not incorporated into the dynamic model, and the training of the ANFIS model was constrained by computational resources. These omissions have been recommended for future research to enhance the model’s accuracy and real-world applicability. Nevertheless, the objectives of this research were achieved, and the potential of hybrid controllers in addressing the challenges of robotic control systems was demonstrated.