Wavelet Fuzzy Neural Network With Asymmetric Membership Function Controller for Electric Power Steering System via Improved Differential Evolution

Hung, Ying Chih; Lin, Faa Jeng; Hwang, Jonq Chin; Chang, Jin Kuan; Ruan, Kai Chun

doi:10.1109/tpel.2014.2327693

Cited by 89 publications

(43 citation statements)

References 40 publications

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“…To avoid this problem, EC algorithms are extensively employed as a global optimization technique. Among all of the parameters of the NN, weight [9][10][11][12][13][14][15][16][17][18][19][20], structure [11, 14, 19, 21-26, 29, 30], and learning rates [27,28] are most frequently optimized by EC algorithms.…”

Section: Optimization Of Nnmentioning

confidence: 99%

When Neural Network Computation Meets Evolutionary Computation: A Survey

Chen

Zhan

Shi

et al. 2016

Advances in Neural Networks – ISNN 2016

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Abstract. Neural network (NN) and evolutionary computation (EC) are two of the most popular and important techniques in computational intelligence, which can be combined together to solve the complex real world problems. This paper represents a review of the researches that combined NN and EC. There are 3 main research focuses as follows. In the first research focus, EC algorithms have been widely used to optimize the structure and parameter of the NN, including weight, structure, learning rates, and others. In the second research focus, lots literatures have witnessed that EC based NNs are widespread in the applications such as classification, automatic control, prediction, and many other fields. These two kinds of researches into combining NN and EC are mainly focuses on using EC algorithms to optimize NN, to enhance the NN performance and the NN application ability. Our survey finds that particle swarm optimization is the most popular EC algorithm that the researchers choice to optimize NN in recent year, while genetic algorithm and differential evolution are also widely used. In the third research focus, there are also researches adopted NN as a tool to enhance the performance of EC algorithms. Although the literatures in this focus are not as many as the above two focuses, the existing results show that NN has great potential in enhancing EC algorithms. The survey shows that when NN and EC meet, combining them would result in an effective way to deal with the real world application. This interesting research topic has become more and more significant in the field of computer science and still has much room for development.

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Section: Optimization Of Nnmentioning

confidence: 99%

When Neural Network Computation Meets Evolutionary Computation: A Survey

Chen

Zhan

Shi

et al. 2016

Advances in Neural Networks – ISNN 2016

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show abstract

“…There have been many studies on the control logic of EPS generating a torque tracking command to create a comfortable steering sensation [2][3][4][5]. However, there have been relatively few attempts to enhance EPS performance by improving the performance of the motor torque controller.…”

Section: Introductionmentioning

confidence: 99%

A Robust Current Controller for Uncertain Permanent Magnet Synchronous Motors with a Performance Recovery Property for Electric Power Steering Applications

et al. 2018

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This paper presents a robust current tracking controller for permanent magnet synchronous motors (PMSMs) with a performance recovery property for electric power steering (EPS) applications. The contributions of this work are twofold. First, a disturbance observer (DOB) is designed to compensate the disturbances arising from the model-plant mismatches while reducing the closed-loop sensitivity. Second, a current controller is designed to improve the current tracking performance in the frequency domain by assigning the performance recovery property to the closed-loop system. The closed-loop performance is verified through simulations and experiments using a 500 W PMSM connected to an EPS system.

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“…It is composed of two feedback loops: internal loop for system friction estimation and external loop for minimizing the tracking error. Ying-Chih Hung et al [11] proposed a wavelet fuzzy neural network using asymmetric membership function (WFNN-AMF) with improved differential evolution (IDE) algorithm to control the EPS. It improved the stability and comfort of the vehicle to great extent.…”

Section: Introductionmentioning

confidence: 99%

EPS Current Tracking Method Research Based on Hybrid Sensitivity H∞ Control Algorithm

Hai-rong

et al. 2018

Journal of Electrical and Computer Engineering

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For electric power steering system (EPS), road interference, noise of the sensor, and the uncertainty of the steering system may make EPS control effect and the driver's road sense worse. EPS system which takes advantage of good current tracking ability, good anti-interference ability, and good operation stability is becoming more and more important in automotive research. The traditional H ∞ control algorithm can solve the system uncertainty theoretically, but it cannot solve the contradiction between robustness and performance without considering the performance of the system. Therefore, this paper proposes a EPS current tracking method based on the hybrid sensitivity H ∞ control algorithm, which takes the current tracking performance as one of the control objectives, so that the system can maximize the robustness and performance. Firstly, the dynamic model of EPS is established. Then, the twodegree-of-freedom vehicle model and tire model are introduced. The state space equation of the system is constructed on the basis of the system state space with random disturbance signals, the hybrid sensitivity H ∞ controller is designed in the sensitivity index design, and the proposed algorithm can use weighting function to minimize the performance of the current tracking error as well as the robustness of the yaw rate error in response to robustness. Simulation analysis and experimental verification of EPS system are also carried out. The results show that the control method of the hybrid sensitivity H ∞ can better achieve EPS target current tracking, effectively suppress the effect of external interference and noise, improve the system performance and robustness, ensure the driver get good road sense, and improve the system of steering stability.

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Wavelet Fuzzy Neural Network With Asymmetric Membership Function Controller for Electric Power Steering System via Improved Differential Evolution

Cited by 89 publications

References 40 publications

When Neural Network Computation Meets Evolutionary Computation: A Survey

When Neural Network Computation Meets Evolutionary Computation: A Survey

A Robust Current Controller for Uncertain Permanent Magnet Synchronous Motors with a Performance Recovery Property for Electric Power Steering Applications

EPS Current Tracking Method Research Based on Hybrid Sensitivity H∞ Control Algorithm

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