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This paper deals with the improvement of the stability of sampled-data (SD) feedback control for nonlinear multiple-input multiple-output time varying systems, such as robotic manipulators, by incorporating an off-line model based nonlinear iterative learning controller. The proposed scheme of nonlinear iterative learning control (NILC) with SD feedback is applicable to a large class of robots because the sampled-data feedback is required for model based feedback controllers, especially for robotic manipulators with complicated dynamics (6 or 7 DOF, or more), while the feedforward control from the off-line iterative learning controller should be assumed as a continuous one. The robustness and convergence of the proposed NILC law with SD feedback is proven, and the derived sufficient condition for convergence is the same as the condition for a NILC with a continuous feedback control input. With respect to the presented NILC algorithm applied to a virtual PUMA 560 robot, simulation results are presented in order to verify convergence and applicability of the proposed learning controller with SD feedback controller attached

This paper presents the design and control of a newly developed five-fingered haptic interface robot named HIRO [II.sup.+]. The developed haptic interface can present force and tactile feeling to the five fingertips of the human hand. Its mechanism consists of a 6 degree of freedom (DOF) arm and a 15 DOF hand. The interface is placed opposite the human hand, which ensures safety and freedom of movement, but this arrangement leads to difficulty in designing and controlling the haptic interface, which should accurately track the fingertip positions of the operator. A design concept and optimum haptic finger layout, which maximizes the design performance index is presented. The design performance index consists of the product space between the operator's finger and the hapic finger, and the opposability of the thumb and fingers. Moreover, in order to reduce the feeling of uneasiness in the operator, a mixed control method consisting of a finger-force control and an arm position control intended to maximize the control performance index, which consists of the hand manipulability measure and the norm of the arm-joint angle vector is proposed. The experimental results demonstrate the high potential of the multifingered haptic interface robot HIRO [II.sup.+] utilizing the mixed control method. Index Terms--Design, force control, haptic interface, manipulability, virtual reality.

This paper presents a new computational algorithm of the model-based adaptive manipulator control, adopting the basic method proposed by Slotine and Li [4]. The manipulator regressor that is represented by an actual velocity vector and a reference velocity vector is computed explicitly by a recursive algorithm based on the Newton-Euler formulation. The proposed algorithm is more efficient than other methods for a manipulator with six or fewer joints. An experimental result is also presented to show the possibility of an adaptive controller having a single DSP for a robot manipulator with six degrees of freedom.