This study proposes an improved finite-state predictive torque control (FS-PTC) to minimise the torque ripples of switched reluctance motor (SRM) drive. Firstly, based on the accurate analytical method, a discrete time model is established for predicting the future states of SRM drive system. Secondly, to reduce the computational burden, a new switching table is constructed for the predictive controller by using the sector partition technique. Further, the torque ripple, copper losses, and average switching frequency are considered synchronously by using a multi-objective cost function. As a result, the proposed FS-PTC method not only can minimise the torque ripple but also can reduce effectively the copper losses and average switching frequency. Finally, the experimental results are carried out for a three phase 12/8 poles 1.5 kW SRM with the proposed control algorithm and the results are compared with conventional direct instantaneous torque control algorithm. These results demonstrate the effectiveness of the proposed method.

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This paper presents a novel direct adaptive neural network controller for switched reluctance motor (SRM) speed control, which takes into account parameter variations, external load disturbances, and input saturation constraint. The radial basis function (RBF) neural network based on the technology of minimal learning parameters (MLP) is employed to approximate an ideal control law that includes parameter variations and external disturbances. An auxiliary dynamic system is constructed to handle the input saturation constraint. Furthermore, uniform ultimate boundedness of all signals in the SRM drive closed‐loop control system is guaranteed through rigorous Lyapunov analysis. Comparative studies are carried out between the proposed control scheme and conventional proportional‐integral (PI) control, and the simulation and experimental results show that the proposed control scheme has better performance for parameter variations and external load disturbances. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

This article presents a robust adaptive neural network controller for switched reluctance motor (SRM) speed control with both parameter variations and external load disturbances. The radial basis function neural network with the technology of minimal learning parameters is employed to approximate an ideal control law which includes the parameter variations and external disturbances. Furthermore, a proportional control term is introduced to improve the transient performance and chattering phenomena of the SRM drive system. The asymptotic stability of the proposed controller is guaranteed through rigorous Lyapunov analysis. A main advantage of the proposed control scheme is that it contains only one adaptive parameter that needs to be updated on-line. This advantage result in a much simpler adaptive control algorithm, which is convenient to implement in switched reluctance drives. Finally, the simulations and experiments are carried out to demonstrate the effectiveness of the proposed control scheme.

This paper proves the problem of losing incremental samples' information of the present SVM incremental learning algorithm from both theoretic and experimental aspects, and proposes a new incremental learning algorithm with support vector machine based on hyperplane-distance. According to the geometric character of support vector, the algorithm uses Hyperplane-Distance to extract the samples, selects samples which are most likely to become support vector to form the vector set of edge, and conducts the support vector machine training on the vector set. This method reduces the number of training samples and effectively improves training speed of incremental learning. The results of experiment performed on Chinese webpage classification show that this algorithm can reduce the number of training samples effectively and accumulate historical information. The HD-SVM algorithm has higher training speed and better precision of classification.