Robust tracking control of robot manipulators via LKF-based estimator

A new control strategy for joint trajectory tracking of robot manipulators is proposed in this paper. The design of the position control system for each joint is based on state feedback technique with two feedforward actions compensating the modelled joint drive system dynamics and the equivalent disturbance, respectively. The independent state feedback control is designed considering linear joint models. The controller gains are tuned on average values of the joint inertia moments, evaluated in the range of the allowable configurations of the manipulator links, and on nominal viscous friction coefficients. The correct tracking of the joint reference trajectories is realised by the feedforward actions compensating the equivalent disturbances rising from the unmodelled joint dynamics, such as nonlinear friction torque terms and parameter variations, centrifugal, Coriolis, and gravity forces, and unknown and time-varying payload. The recursive algorithm, based on the discrete linear Kalman filter (LKF) theory, which has been developed to estimate on-line the nonlinear time-varying equivalent disturbances, guarantees the robustness of the proposed control system. The LKF also gives the estimates of positions and velocities that are used as feedback signals for the joint control systems. The control algorithm has been tested on the main three axes of a COMAU SMART 3S industrial manipulator