earticle

영어

This paper deals with the dynamic control of redundant robot manipulator. Traditionally, the kinematic control schemes for redundant robot manipulator were developed from the point of speed and used under the assumption that the dynamic control of manipulator is perfect. However, in reality, the precise control of redundant robot manipulator is very difficult due to their dynamics. Therefore, the kinematic controllers for redundant robot manipulator were employed in the acceleration dimension and may be combined with the computed torque method to achieve the accurate control performance. But their control performance is limited by the accuracy of the manipulator parameters such as the link mass, length, moment of inertia and varying payload. Hence in this paper, the proportional and derivative control gains of the computed torque controller are optimized by the genetic algorithm on the typical payloads, and the neural network is applied to obtain the proper control gains for arbitrary loads. The simulation results show that the proposed control method has better performance than the conventional control method for redundant robot manipulator.