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Search Results (2)

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Keywords = Kent chaotic map

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25 pages, 7840 KiB  
Article
In-Wheel Motor Control System for Four-Wheel Drive Electric Vehicle Based on CR-GWO-PID Control
by Xiaoguang Xu, Miao Wang, Ping Xiao, Jiale Ding and Xiaoyu Zhang
Sensors 2023, 23(19), 8311; https://doi.org/10.3390/s23198311 - 8 Oct 2023
Cited by 1 | Viewed by 2392
Abstract
In order to improve the driving performance of four-wheel drive electric vehicles and realize precise control of their speed, a Chaotic Random Grey Wolf Optimization-based PID in-wheel motor control algorithm is proposed in this paper. Based on an analysis of the structural principles [...] Read more.
In order to improve the driving performance of four-wheel drive electric vehicles and realize precise control of their speed, a Chaotic Random Grey Wolf Optimization-based PID in-wheel motor control algorithm is proposed in this paper. Based on an analysis of the structural principles of electric vehicles, mathematical and simulation models for the whole vehicle are established. In order to improve the control performance of the hub motor, the traditional Grey Wolf Optimization algorithm is improved. In particular, an enhanced population initialization strategy integrating sine and cosine random distribution factors into a Kent chaotic map is proposed, the weight factor of the algorithm is improved using a sine-based non-linear decreasing strategy, and the population position is improved using the random proportional movement strategy. These strategies effectively enhance the global optimization ability, convergence speed, and optimization accuracy of the traditional Grey Wolf Optimization algorithm. On this basis, the CR-GWO-PID control algorithm is established. Then, the software and hardware of an in-wheel motor controller are designed and an in-wheel motor bench test system is built. The simulation and bench test results demonstrate the significantly improved response speed and control accuracy of the proposed in-wheel motor control system. Full article
(This article belongs to the Topic Vehicle Dynamics and Control)
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Figure 1
<p>Structural diagram of a four-wheel drive in-wheel motor electric vehicle.</p> Full article ">Figure 2
<p>In-wheel motor map.</p> Full article ">Figure 3
<p>Vehicle model simulation diagram. (Green triangles indicate the changes in the number of in-wheel motors).</p> Full article ">Figure 4
<p>The results of vehicle driving simulation: (<b>a</b>) EV battery change and (<b>b</b>) EV mileage.</p> Full article ">Figure 5
<p>Comparison of key indicators of simulated driving under vehicle simulation road conditions: (<b>a</b>) Comparing the required EV driving speed with the actual EV driving speed; (<b>b</b>) Comparing the required motor torque of the EV with the actual motor torque of the EV.</p> Full article ">Figure 6
<p>Algorithm population initialization comparison diagram: (<b>a</b>) and (<b>b</b>).</p> Full article ">Figure 7
<p>Algorithm distance weight comparison diagram.</p> Full article ">Figure 8
<p>Algorithm flowchart.</p> Full article ">Figure 9
<p>Test function comparison results: (<b>a</b>) Test function <span class="html-italic">f</span><sub>1</sub>; (<b>b</b>) test function <span class="html-italic">f</span><sub>2</sub>; (<b>c</b>) test function <span class="html-italic">f</span><sub>3</sub>; (<b>d</b>) test function <span class="html-italic">f</span><sub>4</sub>; (<b>e</b>) test function <span class="html-italic">f</span><sub>5</sub>; and (<b>f</b>) test function <span class="html-italic">f</span><sub>6</sub>.</p> Full article ">Figure 10
<p>CR-GWO-PID motor speed simulation flow diagram.</p> Full article ">Figure 11
<p>Motor speed simulation curves under the four control strategies: (<b>a</b>) Target speed 600 rpm; (<b>b</b>) target speed 500 rpm; and (<b>c</b>) target speed 400 rpm. (The red arrow indicates magnification of the specified location.).</p> Full article ">Figure 12
<p>Flowchart of the in-wheel motor test. (The solid line represents the process and the dashed line represents the constituent structure.).</p> Full article ">Figure 13
<p>Images of the test bench, showing its layout.</p> Full article ">Figure 14
<p>In Test Group 1, the Simulation curve diagram of motor speed under the four control strategies: (<b>a</b>) Target speed 400 rpm; (<b>b</b>) target speed 500 rpm; and (<b>c</b>) target speed 600 rpm.</p> Full article ">Figure 15
<p>In Test Group 2, the Simulation curve diagram of motor speed under the four control strategies: (<b>a</b>) Target speed 400 rpm; (<b>b</b>) target speed 500 rpm; and (<b>c</b>) target speed 600 rpm.</p> Full article ">Figure 16
<p>In Test Group 3, the Simulation curve diagram of motor speed under the four control strategies: (<b>a</b>) Target speed 400 rpm; (<b>b</b>) target speed 500 rpm; and (<b>c</b>) target speed 600 rpm.</p> Full article ">
14 pages, 564 KiB  
Article
Enhance Teaching-Learning-Based Optimization for Tsallis-Entropy-Based Feature Selection Classification Approach
by Di Wu, Heming Jia, Laith Abualigah, Zhikai Xing, Rong Zheng, Hongyu Wang and Maryam Altalhi
Processes 2022, 10(2), 360; https://doi.org/10.3390/pr10020360 - 14 Feb 2022
Cited by 23 | Viewed by 2606
Abstract
Feature selection is an effective method to reduce the number of data features, which boosts classification performance in machine learning. This paper uses the Tsallis-entropy-based feature selection to detect the significant feature. Support Vector Machine (SVM) is adopted as the classifier for classification [...] Read more.
Feature selection is an effective method to reduce the number of data features, which boosts classification performance in machine learning. This paper uses the Tsallis-entropy-based feature selection to detect the significant feature. Support Vector Machine (SVM) is adopted as the classifier for classification purposes in this paper. We proposed an enhanced Teaching-Learning-Based Optimization (ETLBO) to optimize the SVM and Tsallis entropy parameters to improve classification accuracy. The adaptive weight strategy and Kent chaotic map are used to enhance the optimal ability of the traditional TLBO. The proposed method aims to avoid the main weaknesses of the original TLBO, which is trapped in local optimal and unbalance between the search mechanisms. Experiments based on 16 classical datasets are selected to test the performance of the ETLBO, and the results are compared with other well-established optimization algorithms. The obtained results illustrate that the proposed method has better performance in classification accuracy. Full article
(This article belongs to the Special Issue Evolutionary Process for Engineering Optimization)
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<p>The flowchart of the proposed method.</p> Full article ">
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