A Neuro-Sliding Mode Control Scheme for Constrained Robots with Uncertain Jacobian |
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Authors: | R García-Rodríguez V Parra-Vega |
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Affiliation: | (1) Department of Electrical Engineering, Universidad de Chile, Av. Tupper 2007, Casilla 213-4 Santiago, Chile;(2) Robotics and Advanced, Manufacturing Division, CINVESTAV, Campus Saltillo, México |
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Abstract: | The joint robot control requires to map desired cartesian tasks into desired joint trajectories, by using the ill-posed inverse
kinematics mapping. In order to avoid inverse kinematics, the control problem is formulated directly in task space to gives
rise to cartesian robot control. In addition, when the robot is constrained due to its kinematic mappings yields a stiff system
and an additional complexity arises to implement cartesian control for constrained robots. In this paper, an alternative approach
is proposed to guarantee global convergence of force and position cartesian tracking errors under the assumption that the
jacobian is not exactly known. A neuro-sliding mode controller is presented, where a small size adaptive neural network compensates
approximately for the inverse dynamics and an inner control loop induces second order sliding modes to guarantee tracking.
The sliding mode variable tunes the online adaptation of the weights. A passivity analysis yields the energy Lyapunov function
to prove boundedness of all closed-loop signals and variable structure control theory is used to finally conclude convergence
of position and force tracking errors. Experimental results are provided to visualize the expected performance. |
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Keywords: | Robot control Constrained motion Neural networks Second order sliding mode Chattering free Jacobian uncertainty |
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