Adaptive Tracking Control for Robotic Manipulators with Time-Varying State Constraints

The problem of adaptive neural control is mainly addressed for the robotic manipulators with time-varying full state constraints and dead-zone in this paper, which can ensure the desired performance in a finite time. In order to overcome the problems of the time-varying state constraints, the time-varying barrier Lyapunov functions (TVBLFs)are adopted to insure that all the states are not violated from the predetermined range. Moreover, by employing the backstepping procedure and Lyapunov stability theory, the system stability and desired performance are guaranteed in finite time. Finally, the effectiveness of the devised approach is proved via some simulation results.

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