Adaptive RBF Network Based Direct Voltage Control for Interior PMSM Based Vehicles

Chaoui, Hicham; Khayamy, Mehdy; Okoye, Okezie

doi:10.1109/tvt.2018.2813666

Cited by 35 publications

(23 citation statements)

References 33 publications

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“…The structure of an RBF neural network is shown in Figure 3. In Figure 3, the RBF neural network consists of the input layer, the basis function layer (hidden layer), and the output layer [35,36]. The input layer is used to complete the information transfer, the basis function layer is the respective field, and the output layer is the outputs of the RBF neural network.…”

Section: The Rbf Neural Networkmentioning

confidence: 99%

Backstepping Adaptive Neural Network Control for Electric Braking Systems of Aircrafts

Zhang

Hui

2019

Algorithms

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This paper proposes an adaptive backstepping control algorithm for electric braking systems with electromechanical actuators (EMAs). First, the ideal mathematical model of the EMA is established, and the nonlinear factors are analyzed, such as the deformation of the reduction gear. Subsequently, the actual mathematical model of the EMA is rebuilt by combining the ideal model and the nonlinear factors. To realize high performance braking pressure control, the backstepping control method is adopted to address the mismatched uncertainties in the electric braking system, and a radial basis function (RBF) neural network is established to estimate the nonlinear functions in the control system. The experimental results indicate that the proposed braking pressure control strategy can improve the servo performance of the electric braking system. In addition, the hardware-in-loop (HIL) experimental results show that the proposed EMA controller can satisfy the requirements of the aircraft antilock braking systems.

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Section: The Rbf Neural Networkmentioning

confidence: 99%

Backstepping Adaptive Neural Network Control for Electric Braking Systems of Aircrafts

Zhang

Hui

2019

Algorithms

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“…This is a potential solution to the optimization process [9,11]. The degree of honey source is estimated by the instantaneous error function [24][25][26][27], as follows:…”

Section: Rbfabc Algorithmmentioning

confidence: 99%

“…However, the existing wireless transmission technology cannot meet the real-time monitoring effect of wireless network under the conditions of low power consumption and long distance. In the existing methods, there is a mesh network architecture that minimizes the number of devices, which greatly reduces the cost of base station establishment [8,9]. At the same time, it is very convenient to deploy the equipment [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

An Improved Artificial Bee Colony Algorithm in LoRa Wireless Communication System for Efficient Multimedia Transmission

Song

Huang

et al. 2018

International Journal of Digital Multimedia Broadcasting

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Video streaming communication networks will be a very important way to send multimedia information anytime and anywhere, and the construction of the network base station which transmits signals is crucial in future. However, there is a contradiction between the power consumption of LoRa nodes and the real-timeliness of mesh network. In order to solve the contradiction, this article aims to combine the mesh network of LoRa wireless communication system with an improved artificial bee colony algorithm. Specifically, an artificial bee colony algorithm, which is based on RBF radial basis neural network trained with random gradient method, is designed. Simulation results show that the proposed algorithm solves the contradiction between power consumption and real-timeliness effectively. When using this improved network system structure to send multimedia information, it shows obvious superiority in terms of the high efficiency and real-timeliness of multimedia transmission.

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“…Permanent magnet synchronous motor (PMSM) has been extensively used in industrial applications, such as wind power systems, medical instruments, consumer electronics, robots, and electric vehicles due to its inherent characteristics of small size, simple structure, high reliability, and good torque and speed control characteristics [1]- [3]. In these applications, good speed control performance is important.…”

Section: Introductionmentioning

confidence: 99%

Speed Regulation Based on Adaptive Control and RBFNN for PMSM Considering Parametric Uncertainty and Load Fluctuation

et al. 2020

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A novel speed control scheme combining adaptive speed controller and radial basis function neural network (ASC-RBFNN) is proposed for the speed regulation of permanent magnet synchronous motor (PMSM) in this paper. On one hand, in order to reduce the effect of parametric uncertainty and complicated load fluctuation on the speed control performance, an ASC is proposed. Meanwhile, the speed control system (SCS) of PMSM with the proposed ASC is asymptotically stable even though the parametric uncertainty and complicated load fluctuation exist. On the other hand, with consideration of the uncertainty of complicated load, PMSM parameters, and ASC parameters, the RBFNN is used to optimize all ASC parameters for optimal speed control performance. Finally, the performance is verified on an experiment platform. The results indicate that the SCS with the ASC-RBFNN speed control scheme is with good system stability, fast speed response, and strong anti-fluctuation performance in whole-speed range.

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Adaptive RBF Network Based Direct Voltage Control for Interior PMSM Based Vehicles

Cited by 35 publications

References 33 publications

Backstepping Adaptive Neural Network Control for Electric Braking Systems of Aircrafts

Backstepping Adaptive Neural Network Control for Electric Braking Systems of Aircrafts

An Improved Artificial Bee Colony Algorithm in LoRa Wireless Communication System for Efficient Multimedia Transmission

Speed Regulation Based on Adaptive Control and RBFNN for PMSM Considering Parametric Uncertainty and Load Fluctuation

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