Abstract
Based on the Lyapunov theory, a new principle of synthesizing robot tracking control in the presence of model uncertainties has been developed. We propose a control algorithm which assures desired tracking performances in a presence of model uncertainties. Uncertainties can be in a structure or/and in parameters of a model of a robot. We have defined a sliding surface based on the output error and a quadratic Lyapunov function has been constructed using a sliding mode function. Then the tracking problem is changed to that of staying in the neighborhood of the sliding surface (or keeping Lyapunov function close to zero). Control synthesis has been made in task-space, without meeting the inverse kinematics problem (no need to invert the Jacobian matrix). It is guaranteed that the tracking error becomes close to zero in a settling time which is less than a prescribed finite time. Simulation results are incorporated.
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