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Force control algorithm of the robot manipulator contacting with a constraint surface with uncertain figure and stiffness error is discussed in this paper. To accomplish the accurate parameter less-relying constraint motion, a real-time adjusting fuzzy logic of reference trajectories in impedance model is proposed. The adjustment depends on the real-time force and position feedback. Simulation experiments...
In this paper, the neural impedance controller is formulated to regulate the contact force with the environment. When robot uncertainties are present, the performance of the impedance controller is degraded. To compensate for uncertainties in both robot dynamics and environment, neural network is introduced at the desired trajectory. The training signal is defined to satisfy the desired goal. This...
In this work, two globally stabilizing bounded control schemes for the tracking control of robot manipulators with saturating inputs are proposed. They may be seen as extensions of the so-called PD+ algorithm to the bounded input case. With respect to previous works on the topic, the proposed approaches give a global solution to the problem through static feedback. Moreover, they are not defined using...
This paper addresses the issue of trajectory tracking control based on a neural network controller for industrial manipulators. A new control scheme is proposed based on neural network technology and linear feedback approach for tracking a planned trajectory. The control system is established with two parallel subsystems designed separately. One is a linear controller based on state feedback with...
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