Nonlinear tracking control of kinematically redundant robot manipulators

In this study, we consider the nonlinear control of kinematically redundant robot manipulators. Specifically, we use a Lyapunov technique to design a model based nonlinear controller that achieves exponential link position and subtask tracking. We then illustrate how the model based controller can be redesigned as an adaptive full-state feedback controller that achieves asymptotic link position and subtask tracking despite parametric uncertainty associated with the dynamic model. We also illustrate how the model based controller can be redesigned as an exact model knowledge output feedback controller that achieves semi-global exponential link position and sub-task tracking despite the lack of link velocity measurements. We note that the control strategy does not require the computation of inverse kinematics and does not place any restriction on the self-motion of the manipulator; hence, the extra degrees of freedom are available for subtasks (i.e., maintaining manipulability, avoidance of joint limits and obstacle avoidance). Simulation results are included to illustrate the performance of the control law