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Three-motor synchronous speed-regulation system is a multi-input multi-output (MIMO), nonlinear, and coupling complex control system. This paper focuses on the system of induction motors powered by current-track type SPWM transducers, establishes the mathematical model of the system in the way of analytical expression. Self-tuning PID controllers based on RBF neural network and neuron decoupling compensator...
In view of the controlled plant complexity of the horizontal position synchronization system in automatic filling machine, a novel approach of single neuron PID model reference adaptive control for AC permanent magnet synchronous motor (PMSM) servo control based on diagonal recurrent neural network (DRNN) on-line identification is proposed owing to the disadvantage of traditional PID controller. The...
Based on PMSM dynamics and nonlinear load characteristics, a new nonlinear speed controller is designed with vector control scheme. The proposed controller was composed of backstepping speed controller and error regulator based on RBF neural network. The former was designed to ensure a desired speed tracking control, and the later was derived to realize the robust adaptive control against load torque...
The permanent magnet synchronous motor (PMSM) is a dynamic, multi-variable and non-linear system, and the conventional PID control method is very difficult to meet the requirement for high accuracy control. This paper presents an approach of control for PMSM servo system using fuzzy radius basis function (f-RBF) neural network which has the advantages of strong adaptive ability and nonlinear approximation...
Taking the multi-variable of synchronization system of the AC induction motors as study object, focusing on the system of induction motors powered by current-tract SPWM transducers, the mathematical model of the system of two motors is established. Combining decoupling technology of adaptive neuron decoupling compensator, RBF neural network adaptive PID controller is adopted to design the neural network...
This paper presents an intelligent control system for interior permanent magnet motor drives using a wavelet neural network. The wavelet neural network combines the capability of artificial neural networks for learning from processes and the capability of wavelet decomposition for identification and control of dynamic systems. A four-layer wavelet neural network is adopted to implement the proposed...
In controlling nonlinear, time varying and ill defined systems artificial intelligent controllers have been proved to be superior in design and performance when compared to the conventional controllers. This paper presents a novel adaptive-network-based fuzzy inference system (ANFIS) for speed control of interior permanent magnet synchronous motor (IPMSM) drive. By utilizing a learning technique,...
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