IEEE Transactions on Robotics - Special issue on rehabilitation robotics
Dual adaptive dynamic control of mobile robots using neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
Neural network control of mobile robot formations using RISE feedback
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Neural network control of multifingered robot hands using visual feedback
IEEE Transactions on Neural Networks
Real-time robot path planning based on a modified pulse-coupled neural network model
IEEE Transactions on Neural Networks
Neural network output feedback control of robot formations
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multicriteria optimization for coordination of redundant robots using a dual neural network
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
SVR Versus Neural-Fuzzy Network Controllers for the Sagittal Balance of a Biped Robot
IEEE Transactions on Neural Networks
Perspectives on Cognitive Informatics and Cognitive Computing
International Journal of Cognitive Informatics and Natural Intelligence
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Applications of switched reluctance motor SRM to direct drive robot are increasingly popular because of its valuable advantages. However, the greatest potential defect is its torque ripple owing to the significant nonlinearities. In this paper, a fuzzy neural network FNN is applied to control the SRM torque at the goal of the torque-ripple minimization. The desired current provided by FNN model compensates the nonlinearities and uncertainties of SRM. On the basis of FNN-based current closed-loop system, the trajectory tracking controller is designed by using the dynamic model of the manipulator, where the torque control method cancels the nonlinearities and cross-coupling terms. A single link robot manipulator directly driven by a four-phase 8/6-pole SRM operates in a sinusoidal trajectory tracking rotation. The simulated results verify the proposed control method and a fast convergence that the robot manipulator follows the desired trajectory in a 0.9-s time interval.