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In this paper we present a low dimensional adaptive neural network controller for robot manipulators with fast convergence of tracking error. Its novelty lies in the low dimensional network, smooth control input and very fast convergence that reduce the computational cost that face the problem of over parameterization. The control strategy is based on a second order sliding surface which drives the controller and the online computation of weights with a chattering-free control output. Furthermore, a time base generator induces wellposed finite time convergence of tracking errors for any initial condition. We validate our approach including experimental results obtained in a planar 2 dgf manipulator.

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When robots grasp a rigid object, homogeneous holonomic constraints give rise to stiff nonlinear constrained dynamics. As a result, a smooth motion is preferred when grasping an object in a tight but gentle manner, making it a difficult control problem that increases when uncertainties and unmodelled dynamics exist. In this paper, a fuzzy design is proposed by exploiting a physics-based orthogonalization of contact mechanics to produce desired velocity and force fields. The proposed scheme enables the regulation of the velocity component in order to navigate smoothly, while the force field enforces grasp at contact in the normal direction of the cooperative joint-velocity field. Then, the robust model-free controller is designed to track such orthogonal fields while enforcing cooperation among all robots, even under uncertainties and unknown dynamics. A representative simulation study is discussed to show the feasibility of the proposal.

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