In this paper, we present a backstepping adaptive hybrid force/position control based on Barrier Lyapunov Function for a robotic manipulator to prevent constraint violation of applied force and position simultaneously. First, the task space is partitioned according to the constrained and unconstrained directions, and a new representation of dynamics is introduced. Next, force/position control is applied using the strict-feedback backstepping technique, in which a time-varying Barrier Lyapunov Function is employed to ensure that the force and position do not violate their constraints. Finally, to deal with uncertainty, disturbance and non-linearity of the system, an adaptive radial basis function neural network (RBFNN) is also implemented in the control algorithm. Stability proof of the proposed control method is presented, and simulation studies on a 2-link manipulator show the effectiveness as well as the performance of the proposed controller in preventing constraint violation.


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    Title :

    Barrier-Lyapunov-Function-Based Backstepping Adaptive Hybrid Force/Position Control for Manipulator with Force and Position Constraints


    Contributors:
    Adinehvand, M (author) / Lai, CY (author) / Hoseinnezhad, R (author)

    Publication date :

    2021-07-28


    Remarks:

    In: Proceedings of the American Control Conference. (pp. pp. 2266-2271). IEEE: New Orleans, LA, USA. (2021)


    Type of media :

    Paper


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629



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