Titlebook: Advances in Neural Networks - ISNN 2004; International Sympos Fu-Liang Yin,Jun Wang,Chengan Guo Conference proceedings 2004 Springer-Verlag

Visual-Field

https://doi.org/10.1007/978-1-84628-645-2ations and rotations. A set of object features is properly defined to derive an invertible nonlinear visual mapping model from visual feedback to robot control. A novel fuzzy neural network is proposed to realize the nonlinear mapping model effectively, which is essential to implement six-degree-of-

Longitude

https://doi.org/10.1007/978-1-84628-645-2ing a rigid object. An impedance force control algorithm is derived using force decomposition, and then singular perturbation method is used to construct the composite control, where an adaptive NF inference system is employed to approximate the inverse dynamics of the space robot. Finally, an examp

expeditious

油膏

Networking Models and Standards,D control method, a reinforcement-learning (RL) module is used to automatically fine-tune the PD coefficients with only evaluative feedback. The optimization of the PD coefficients is modeled as a Markov decision problem (MDP) with continuous state space. Using an improved AHC (Adaptive Heuristic Cr

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