Neural network‐based adaptive region tracking control for robot manipulator systems with uncertain kinematics and dynamics

Abstract

Controlling a robot manipulator system with both uncertain kinematics and dynamics is a challenging problem since the traditional control schemes that relying on the robot system models are no longer applicable. Developing a neural network‐based adaptive tracking control for such uncertain robot manipulator systems with region constraints is especially changing. In this paper, region tracking controllers are designed for a robot manipulator systems with uncertain kinematics and dynamics. The developed region tracking controllers ensures that the uncertain robot manipulator can track a moving region other than the traditional fixed point, which has better redundancy characteristics. The results are obtained through the development of the sliding‐mode and a novel proportion‐integration‐differentiation (PID)‐like method to address the region tracking control problem. Numerical simulations are presented to verify the proposed controller's performance.