Multi-Mode Active Suspension Control Based on a Genetic K-Means Clustering Linear Quadratic Algorithm

Wu, Kun; Jiang, Liu; Li, Min; Liu, Jianze; Wang, Yushun

doi:10.3390/app112110493

Cited by 6 publications

(3 citation statements)

References 24 publications

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“…Genetic algorithm (GA) is a well-known population-based metaheuristic algorithm that is inspired by the biological evolution processes [28,29]. The fundamental components of GA are chromosome representation, fitness function, and biological-inspired operators which are selection, mutation, and crossover.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Genetic Algorithm Combined with the K-Means Algorithm: A Hybrid Technique for Unsupervised Feature Selection

Bennaceur,

Almutairy,

Alhussain

2023

Intelligent Automation &Amp; Soft Computing

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The dimensionality of data is increasing very rapidly, which creates challenges for most of the current mining and learning algorithms, such as large memory requirements and high computational costs. The literature includes much research on feature selection for supervised learning. However, feature selection for unsupervised learning has only recently been studied. Finding the subset of features in unsupervised learning that enhances the performance is challenging since the clusters are indeterminate. This work proposes a hybrid technique for unsupervised feature selection called GAk-MEANS, which combines the genetic algorithm (GA) approach with the classical k-Means algorithm. In the proposed algorithm, a new fitness function is designed in addition to new smart crossover and mutation operators. The effectiveness of this algorithm is demonstrated on various datasets. Furthermore, the performance of GAk-MEANS has been compared with other genetic algorithms, such as the genetic algorithm using the Sammon Error Function and the genetic algorithm using the Sum of Squared Error Function. Additionally, the performance of GAk-MEANS is compared with the stateof-the-art statistical unsupervised feature selection techniques. Experimental results show that GAk-MEANS consistently selects subsets of features that result in better classification accuracy compared to others. In particular, GAk-MEANS is able to significantly reduce the size of the subset of selected features by an average of 86.35% (72%-96.14%), which leads to an increase of the accuracy by an average of 3.78% (1.05%-6.32%) compared to using all features. When compared with the genetic algorithm using the Sammon Error Function, GAk-MEANS is able to reduce the size of the subset of selected features by 41.29% on average, improve the accuracy by 5.37%, and reduce the time by 70.71%. When compared with the genetic algorithm using the Sum of Squared Error Function, GAk-MEANS on average is able to reduce the size of the subset of selected features by 15.91%, and improve the accuracy by 9.81%, but the time is increased by a factor of 3. When compared with the machinelearning based methods, we observed that GAk-MEANS is able to increase the accuracy by 13.67% on average with an 88.76% average increase in time.

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Section: Genetic Algorithmmentioning

confidence: 99%

Genetic Algorithm Combined with the K-Means Algorithm: A Hybrid Technique for Unsupervised Feature Selection

Bennaceur,

Almutairy,

Alhussain

2023

Intelligent Automation &Amp; Soft Computing

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“…25 For MIMO (Multi Input – Multi Output) systems, the Linear – Quadratic Regulator (LQR) controller can be used to replace the conventional PID controller. 26 This algorithm aims to minimize the cost function so the system can be more stable. 27 The mathematical model of this algorithm must be given in the form of a state matrix.…”

Section: Introductionmentioning

confidence: 99%

Design a new control algorithm AFSP (Adaptive Fuzzy – Sliding Mode – Proportional – Integral) for automotive suspension system

Nguyen

2023

Advances in Mechanical Engineering

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When a vehicle is driving, the most significant source of oscillations is the irregularities in the surface of the road. An active suspension is utilized to enhance the vehicle’s stability and ride comfort. In this work, the dynamic model with multi variables is considered to simulate vehicle oscillations based on four excitation cases from the road surface. In addition, this research also established the AFSP complex control solution for an active suspension system. This is a completely novel algorithm with many outstanding advantages. The final control signal of the system is synthesized from the signals of the linear controller PI (Proportional – Integral) and the nonlinear controller SMC (Sliding Mode Control). The controller’s parameters will change continuously depending on the established fuzzy rule. According to simulation outcomes, the vehicle body displacement and acceleration dramatically declined when the AFSP algorithm directed an active suspension. The values of acceleration and displacement do not exceed 80% and 20%, compared to the scenario in which the vehicle just utilized the typical passive suspension system. Besides, a phenomenon of “chattering” also does not appear when combining the controllers. Overall, the efficiency of this algorithm is high. This algorithm can be applied to more complex models.

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“…(Peng et al, 1997) (Conde et al, 2011) (Kaleemullah et al, 2012) (Ab Talib et al, 2015) (Rao & Kumar, 2015) (Sun et al, 2020) (Nguyen & Nguyen, 2022), intelligent regulators (Fuzzy Logic, Neural Networks, etc.) (Hasbullah & Faris, 2010) (Nagarkar et al, 2019), and/or hybrid and metaheuristic regulators (Mahmoodabadi et al, 2018) (Lavasani & Doroudi, 2020) (Wu et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Firefly Algorithm Optimization-Based LQR Controller for 1/4 Vehicle Active Suspension System: Design and Performance Evaluation

Dif

2023

J. Eng. Exact Sci.

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The outcomes of this research significantly contribute to the existing body of knowledge on metaheuristic optimization techniques for active suspension systems. By conducting thorough investigations and analyses, the study effectively demonstrates the remarkable advantages of the Firefly algorithm in optimizing the performance of both conventional and intelligent controllers for active suspension systems. These findings highlight the algorithm's potential to revolutionize the field and pave the way for more efficient and robust control strategies. The demonstrated effectiveness of the Firefly algorithm in minimizing the error between the car's displacements and the disturbances of the road is particularly noteworthy. This achievement ensures a more precise and accurate tracking of the desired trajectory, regardless of the input signal used, whether it be an impulse, sine wave, or step-wise graded signal.

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Multi-Mode Active Suspension Control Based on a Genetic K-Means Clustering Linear Quadratic Algorithm

Cited by 6 publications

References 24 publications

Genetic Algorithm Combined with the K-Means Algorithm: A Hybrid Technique for Unsupervised Feature Selection

Genetic Algorithm Combined with the K-Means Algorithm: A Hybrid Technique for Unsupervised Feature Selection

Design a new control algorithm AFSP (Adaptive Fuzzy – Sliding Mode – Proportional – Integral) for automotive suspension system

Firefly Algorithm Optimization-Based LQR Controller for 1/4 Vehicle Active Suspension System: Design and Performance Evaluation

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