Dynamic neural networks based adaptive admittance control for redundant manipulators with model uncertainties

Abstract

Abstract Force control is of great significance in the field of robotic control, for example, in tasks which requires precise control of contact force between robot and the environment, traditional path-tracking based methods may hardly achieve expected performance. However, it remains challenging for redundant manipulators with bounded constraints and model uncertainties. In this paper, we propose a novel adaptive admittance controller based on the idea of quadratic programming(QP). The control problem is transferred into an optimization one, in which the objective function is defined to make advantage of redundant DOFs, and the desired motion-force task is rebuilt as an equality constraints using admittance control strategy. Simultaneously, the bounded physical restrictions are regarded as inequality constraints. The uncertainties of both interaction model and physical parameters are also taken into consideration, which inspires us to use adaptive technology to identify those parameters online. Then an adaptive recurrent neural network is established to solve the QP problem online. This control scheme generalizes recurrent neural network based kinematic control of manipulators to that of position-force control, which opens a new avenue to shift position-force control of manipulators from pure control perspective to cross design with both convergence and optimality consideration. Besides, this method could deal with model uncertainties, and the calculation of pseudo-inverse of Jacobian matrix is no longer required. Numerical results on a 7-DOF manipulator iiwa and comparisons with existing methods show the validity of the proposed control method.