Fundamentals of Robotics: Analysis and Control
Fundamentals of Robotics: Analysis and Control
Neural Networks for Optimization and Signal Processing
Neural Networks for Optimization and Signal Processing
Constrained multi-variable generalized predictive control using a dual neural network
Neural Computing and Applications
IEEE Transactions on Robotics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A hierarchical optimization neural network for large-scale dynamic systems
Automatica (Journal of IFAC)
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A Recurrent Neural Network for Hierarchical Control of Interconnected Dynamic Systems
IEEE Transactions on Neural Networks
Robotics and Computer-Integrated Manufacturing
Evolutionary neural networks and DNA computing algorithms for dual-axis motion control
Engineering Applications of Artificial Intelligence
Robotics and Computer-Integrated Manufacturing
Fuzzy Neural Network Control for Robot Manipulator Directly Driven by Switched Reluctance Motor
International Journal of Cognitive Informatics and Natural Intelligence
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Journal of Intelligent and Robotic Systems
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A dual neural-network method for the coordination of kinematically redundant robots is proposed in this paper. The performance criteria for single robots provided by Nedungadi and Kazerounian are generalized to a multicriteria form for the coordinated-manipulation system composed of multiple serial manipulators. By optimizing the local joint torques and generalized forces applied on the object/workpiece using a designed weighting matrix, the proposed method achieves the global stability during the coordinated-manipulation process. Moreover, the proposed algorithm has an explicit physical meaning, i.e., both the global kinetic energy of the coordination system and the two-norm of the generalized forces applied on the object are minimized simultaneously. In addition, the physical limits of both joint torques and the generalized forces applied on the object are considered, which makes the original coordination problem become a complicated optimization problem subject to both equality and inequality constraints. Compared with numerical optimization algorithms used in existing literatures, the dual neural-network method has better computational capability to deal with the complicated optimization problem. Finally, illustrative examples are given to show that the proposed method is effective and efficient for the multirobot coordinated-manipulation system.