Remarks on Adaptive Compensator with Quaternion Neural Network in Computed Torque Control

Abstract

Model-based control such as computed torque control is frequently employed to ensure the accurate control of a robot manipulator. However, in some cases control performance is not satisfactory due to unmodeled nonlinearities or dynamics. To overcome this issue, this study investigates how using a quaternion neural network can adaptively compensate for the computed torque control. The control system consists of the quaternion neural network, feedforward model and feedback controller, resulting in a feedback error learning scheme utilised for the training of the quaternion neural network with a backpropagation algorithm extended to quaternion numbers. In computational experiments, the trajectory control of a three-link robot manipulator is performed using the proposed control system. Simulation results confirm the effectiveness of the quaternion neural network in practical control applications.