In this paper, adaptive iterative learning control (AILC) of uncertain robot manipulators in task space is considered for trajectory tracking in an iterative operation mode. The control scheme incluces a PD controller with a gain switching technique plus a learning feedforward term, is exploited to predict the desired actuator torque. By using Lyapunov method, an adaptive iterative learning control scheme is presented for robotic system with both structured and unstructured uncertainty, and the

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... overall stability of the closed-loop system in the iterative domain is established. The validity of the scheme is confirmed through a numerical simulation. 519 Arimoto-type ILC algorithm, we can develop a PID-like update law can be given in [6] . So far some of robot manipulators control in the published literature [7]-[9] and etc, proposed an adaptive ILC to deal with parameter uncertainties, such as the link length, mass inertia, and friction nonlinearity, with a self-organizing capability. In this paper, a new method is given based on a combination of the advantages of a several control methods into a hybrid one. In particular, it is further extended to the task space or the so-called Cartesian space. To apply robot manipulators to a wide class of tasks, it will be necessary to control not only the position of the end-effector, but also the force exerted by the end-effector on the object. By designing the control law in task space, force control can be easily formulated. This paper is organized as follows. Section 2 described a dynamic model of an n-link robot manipulator in task space. Section 3 presents AILC and its features are discussed. By using Lyapunov method to prove the asymptotic convergence of proposed controller. Numerical simulation results of a two-link robot manipulator in task space under the possible occurrence of uncertainties are provided to demonstrate the tracking control performance of the proposed AILC system in section 4. Conclusions are drawn in section 5.