Fuzzy model‐based multi‐objective dynamic programming with modified particle swarm optimization approach for the balance control of bicycle robot

Yi, Sun; Zhao, Haotian; Chen, Zhang; Zheng, Xudong; Zhao, Mingguo; Liang, Bin

doi:10.1049/cth2.12199

Cited by 13 publications

(4 citation statements)

References 40 publications

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“…the improved whale algorithm has high search accuracy, fast convergence speed, and strong stability. The improved whale optimization algorithm solves the optimal value function and actual function problems, and good results are obtained by a large number of experiments, which provides certain theoretical support for the application of the algorithm [19][20].…”

Section: ( )mentioning

confidence: 81%

Spectrum Sensing Algorithm by Multi-Objective Optimization Theory and Fuzzy Integral Method

Dong

2023

IEEE Access

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As spectrum resources become increasingly scarce, efficient spectrum management is crucial in wireless communication. To address this challenge, a new spectrum sensing algorithm based on multi-objective optimization theory and fuzzy integral method has been developed. This innovative algorithm combines the whale algorithm and fuzzy integral algorithm, allowing for multi-objective optimization and interference decision-making. It overcomes limitations in existing multi-user spectrum sensing methods, such as low perception probability, poor detection accuracy, high false alarm probability, and computational complexity. Comparative studies show that the multi-objective whale algorithm achieves excellent optimization performance and fast convergence, reaching a throughput of 16.8 after 600 cycles. The collaborative spectrum sensing method, especially the one based on fuzzy integration, performs exceptionally well, particularly in high signal-to-noise ratio environments. This algorithm has significant application potential and effectively addresses spectrum resource management in wireless communication. It offers benefits such as improved spectrum utilization efficiency, scalability, and practicality, making valuable contributions to technological progress and efficiency enhancement in the field.

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Section: ( )mentioning

confidence: 81%

Spectrum Sensing Algorithm by Multi-Objective Optimization Theory and Fuzzy Integral Method

Dong

2023

IEEE Access

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“…Four components are used to apply risk assessment concepts and prioritize contributing factors: risk exposure, the level of likelihood of risk occurrence, the level of risk impact on cyclist safety, and the severity of risk factors [12,36,37]. These are summarized as follows:…”

Section: Risk Assessment Conceptmentioning

confidence: 99%

Pedal towards Safety: The Development and Evaluation of a Risk Index for Cyclists

Ayad,

Imine,

Lantieri

et al. 2024

Infrastructures

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Cyclists are at a higher risk of being involved in accidents. To this end, a safer environment for cyclists should be pursued so that they can feel safe while riding their bicycles. Focusing on safety risks that cyclists may face is the main key to preserving safe mobility, reducing accidents, and improving their level of safety during their travel. Identifying and assessing risk factors, as well as informing cyclists about them may lead to an efficient and integrated transportation system. Therefore, the purpose of this research is to introduce a risk index that can be adapted to different road areas in order to measure the degree of how risky these areas are for biking. Cyclists’ behavior and demographics were integrated into the risk index calculation. The methodology followed to obtain the risk index composed of four phases: risk factor identification, risk factor weighting, risk index formulation, and risk index validation. Nineteen risk factors are categorized into four major groups: facility features, infrastructure features, cyclist behavior, and weather and traffic conditions.

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“…The introduction of inertia weights coordinates to a certain extent the optimization-seeking ability of the PSO algorithm, 3) [20].…”

Section: Neural Network Intelligent Control Based On Mpso Optimizatio...mentioning

confidence: 99%

Neural Network Intelligent Control Based on MPSO

Aijun

2023

IEEE Access

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With the increasing complexity of mechanical equipment, the control effectiveness of traditional intelligent control systems can no longer meet the needs of modern industrial production. In order to reduce errors in intelligent control systems while ensuring system performance, this study proposes a new Particle Swarm Optimization (PSO) optimization scheme. The study simplified the PSO algorithm from three aspects: algorithm parameters, speed, and position formula, and corrected the formulas for individual optimal values and global optimal values. Research will name the optimized algorithm Modified PSO (MPSO). On the basis of the MPSO algorithm, neural network intelligent control has been innovatively improved. In the experimental results, the MPSO optimized controller controlled the error within 0.01 within 0.02 seconds. At this time, the Whale Optimization Algorithm (WOA) optimized error was 0.072, and the PSO optimized error was 0.478. Compared to PSO and WOA, the control error of MPSO has decreased by 98.95% and 93.06%, respectively. In addition, the proposed method not only has the best control effect, but also has the shortest system response time, with an average time of 1.294 seconds. Compared to PSO and WOA optimization, it reduces by 61.48% and 43.07%, respectively.The results verified that the proposed method in this study can effectively improve the accuracy of intelligent control and control the error within the target range within 0.02 seconds. The research not only simplifies the calculation of the PSO algorithm, but also effectively reduces the error of the algorithm, providing a reference for research in the field of intelligent control.INDEX TERMS Intelligent control, nonlinear problems, neural network, particle swarm optimization (PSO).AIJUN KOU was born in Linyi, Shandong, China, in 1985. He received the master's degree from Jiangsu University, China. He is currently with the School of Electronic Information, Huzhou College. His research interests include artificial intelligence, machine learning, and computer vision.

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Fuzzy model‐based multi‐objective dynamic programming with modified particle swarm optimization approach for the balance control of bicycle robot

Cited by 13 publications

References 40 publications

Spectrum Sensing Algorithm by Multi-Objective Optimization Theory and Fuzzy Integral Method

Spectrum Sensing Algorithm by Multi-Objective Optimization Theory and Fuzzy Integral Method

Pedal towards Safety: The Development and Evaluation of a Risk Index for Cyclists

Neural Network Intelligent Control Based on MPSO

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