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By applying direct derivative dynamics (DDD or D3), gradient neural dynamics (GND) and Zhang neural dynamics (ZND) at joint-jerk level, three novel solutions (namely, of types Z1D1G1-D1Z1G1, D2Z1-D1Z1D1-Z1D2 and D1Z2-Z1D1Z1-Z2D1) are proposed, developed and investigated in this paper. It is the first time that a relatively complete hybrid solution framework is proposed at the joint-jerk level. Then,...
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...
An accurate estimation of the dynamics efforts acting on a robot manipulator represents an important issue for both the analysis of its behavior and the synthesis of appropriate controllers. This paper proposes an iterative algorithm, which is based on the Newton-Euler approach, for the efficient evaluation of the manipulators' high-order kinematics and dynamics. In particular, the algorithm computes...
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