NEURO-ADAPTIVE CONTROL OF ROBOTIC MANIPULATORS

The need to meet demanding control requirements in increasingly complex dynamical control systems under significant uncertainties makes neural networks very attractive, because of their ability to learn, to approximate functions, to classify patterns and because of their potential for massively parallel hardware implementation. This paper proposes the use of artificial neural networks (ANN) as a novel approach to the control of robot manipulators. These are part of the general class of non-linear dynamic systems where non-linear compensators are required in the controller. A major objective of the work described has been to develop ANN architectures which will provide fast and robust on-line learning of the dynamic relations required by the robot controller at the executive hierarchical level. The approach to robot control proposed involves using a neural network feedforward loop together with a conventional PD feedback loop. Such a use of an ANN with a back-propagation learning algorithm is considered to be a new approach to adaptive control of a non-linear robot system. The controller architecture developed has been simulated and its effect on the trajectory tracking performance of a manipulator has been evaluated.