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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...
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...
In this paper, a neural network based method was presented to estimate the position errors in a robot manipulator calibration process. We use a set of measurement within a window to form the input and output pairs of training data. The utilization of a window of data helps to exploit the local feature of the underline mapping function and also reduce the complexity of the neural network model. After...
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