Novel Multi-input Multi-output Brain Emotional Learning Based Intelligent Controller for PUMA 560 Robotic Arm

摘要

A novel Multi-Input Multi-Output Brain Emotional Learning Based Intelligent Controller (MIMO-BELBIC) is introduced and applied as a controller of PUMA 560 robotic arm. PUMA 560 model is strongly coupled highly nonlinear model, which necessitate effective controller capable of dealing with high degree of coupling and nonlinearity. Furthermore, the robot is subjected to many sources of disturbances, which can affect the performance significantly. Mathematical model of MIMO-BELBIC is introduced and tailored to work as a controller of PUMA 560. Moreover, new optimization algorithm, designed especially for this problem, is used to optimize the 51 parameters of the controller. The results show remarkable success of the proposed controller in decreasing the tracking error (with/without) disturbances in comparison to the traditional PID controllers that were optimized by two different algorithms. Moreover, the proposed controller has minimal control effort with respect to the PID controllers.