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This paper presents neural estimators of the state variable for drive system with elastic joints. The main stages of the design methodology of neural estimators of the torsional torque and the load machine speed were presented. For the optimization of the structure of each neural networks the Optimal Brain Damage method was implemented. The signals estimated by neural networks were tested in the control...
An adaptive neural network control of a novel type of translational meshing motor with model uncertainties is considered. Owing to its nonlinear characteristic, a model reference control system which consists of two neural networks is used. The torque model is identified based on BP neural network, and then a RBF neural network works as the controller. The model reference control system is trained...
This paper presents a novel direct instantaneous torque and flux controller with an adaptive linear neuron (ADALINE)-based motor model for a high-performance direct drive permanent magnet synchronous motor (DD-PMSM). Usually, high-performance motor drives are characterized by their fast and accurate response, quick recovery from disturbances, and insensitivity to parameter variations. However, the...
Principle of a new adaptive neuro-fuzzy inference system (ANFIS) with supervisory learning algorithm is introduced and is used to regulate the speed of a four-switch, three-phase inverter (FSTPI) brushless DC (BLDC) drive. The proposed algorithm has advantages of neural and fuzzy networks. To enhance of drive's performance, instead of well-known back propagation learning method, a fuzzy based supervisory...
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