Dynamic neural networks based kinematic control for redundant manipulators with model uncertainties

Redundant design can greatly improve the flexibility of robot manipulators, but may suffer from potential limitations such as system complicity, model uncertainties, physical limitations, which make it challenging to achieve accurate tracking. In this paper, we propose a novel kinematic controller based on a recurrent neural network(RNN) which is competent in model adaption. An identifier which is related to joint velocity and tracking error is designed to learn the kinematic parameters online. In the inner loop, the redundancy resolution is formulated as a quadratic optimization problem, and a RNN is built to obtain the optimal solution recurrently, and the minimum norm of joint velocity is derived as the secondary task. Theoretical analysis demonstrates the global convergence of tracking error. Compared with existing methods, uncertain kinematic model of the robot is allowed in this paper, and pseudo-inverse of Jacobian matrix is avoided, with the consideration of physical limitations in a joint framework. Numerical and actual experiments based on a serial robot Kinova JACO2 show the effectiveness of the proposed controller.