Fuzzy adaptive control particle swarm optimization based on T-S fuzzy model of maglev vehicle suspension system

Chen, Chen; Xu, Junqi; Lin, Guobin; Sun, Yougang; Gao, Dinggang

doi:10.1007/s12206-019-1247-4

Cited by 23 publications

(8 citation statements)

References 11 publications

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“…However, most of these control strategies degrade in performance when there are disturbances because they rely on a precise model and detailed information about the system. Artificial intelligent control methods, including the fuzzy logic method [19,20], neural networks (NN) [21][22][23][24][25], deep belief networks (DBN) [26], and deep reinforcement learning (DRL) [27], have been implemented to increase the robustness of the control system by automatically adjusting the parameters in the controller or directly acting as a controller.…”

Section: Introductionmentioning

confidence: 99%

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A Review of Levitation Control Methods for Low- and Medium-Speed Maglev Systems

Zhu,

Wang,

2024

Buildings

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Maglev transportation is a highly promising form of transportation for the future, primarily due to its friction-free operation, exceptional comfort, and low risk of derailment. Unlike conventional transportation systems, maglev trains operate with no mechanical contact with the track. Maglev trains achieve levitation and guidance using electromagnetic forces controlled by a magnetic levitation control system. Therefore, the magnetic levitation control system is of utmost importance in maintaining the stable operation performance of a maglev train. However, due to the open-loop instability and strong nonlinearity of the control system, designing an active controller with self-adaptive ability poses a substantial challenge. Moreover, various uncertainties exist, including parameter variations and unknown external disturbances, under different operating conditions. Although several review papers on maglev levitation systems and control methods have been published over the last decade, there has been no comprehensive exploration of their modeling and related control technologies. Meanwhile, many review papers have become outdated and no longer reflect the current state-of-the-art research in the field. Therefore, this article aims to summarize the models and control technologies for maglev levitation systems following the preferred reporting items for systematic reviews and meta-analysis (PRISMA) criteria. The control technologies mainly include linear control methods, nonlinear control methods, and artificial intelligence methods. In addition, the article will discuss maglev control in other scenarios, such as vehicle–guideway vibration control and redundancy and fault-tolerant design. First, the widely used maglev levitation system modeling methods are reviewed, including the modeling assumptions. Second, the principle of the control methods and their control performance in maglev levitation systems are presented. Third, the maglev control methods in other scenarios are discussed. Finally, the key issues pertaining to the future direction of maglev levitation control are discussed.

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Section: Introductionmentioning

confidence: 99%

“…Takagi-Sugeno fuzzy model He et al [19], Chen et al [20], and Su et al [34] used a mathematical tool named Takagi-Sugeno (T-S) fuzzy model [55] to build fuzzy models of the nonlinear system. Then, controllers were designed based on these fuzzy models.…”

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confidence: 99%

A Review of Levitation Control Methods for Low- and Medium-Speed Maglev Systems

Zhu,

Wang,

2024

Buildings

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“…This phase angle makes PSO a self-adaptive, trigonometric, balanced, and nonparametric meta-heuristic algorithm. [10][11] Particle swarm optimization (PSO) is used to increase the system's tracking performance and response time to external disturbances. Optimized fuzzy adaptive control's performance in coordinating the stability of a closed-loop suspension system may be observed in its response time and convergence rate.…”

Section: Introductionmentioning

confidence: 99%

Mutated PSO Tuned PID Parameters for SEPIC Converter

Amarendra

Sekhar

Reddy

et al. 2023

Advances in Transdisciplinary Engineering

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Photovoltaic systems have become widely recognized as a realistic and viable source of power. The PV panel output voltage is strongly dependent on solar parameters, which is one of the most critical issues with PV systems. As a result, PV panel output cannot be directly linked to loads. To act as an interface between PV panels and loads, a SEPIC Converter is necessary. The DC-DC boost converter keeps the PV system’s output voltage constant. It transforms variable PV panel input voltage to output voltage maintained constant via output capacitors to the loads. The duty ratio affects the output voltage of a typical SEPIC converter. The output voltage will fluctuate if the input voltage changes while the duty ratio remains fixed. The duty ratio is regulated depending on the input voltage to achieve a constant output voltage. The input voltage is used to alter the duty ratio. In PV systems, PID controllers are widely utilized as SEPIC converter controllers. The PID controller parameter is tuned to enhance the controller’s operating range. SEPIC converters Kp, Kd, and Ki under various input voltage conditions. The regulated SEPIC converter serves as a link between the solar panels and the SEPIC converter’s load. A unique PID controller was used for a SEPIC converter for control to get the voltage controller in a PV system. When compared to a closed-loop SEPIC converter, the optimized PID SEPIC converter for PV systems provides the optimum performance. The simulation findings confirm that the SEPIC converter controller in a PV system is effective and trustworthy.

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“…Another importance of the TSFM is that can use many recognized linear control theories to analyze the stability conditions of nonlinear systems [13]. Consequently, many stability analyses and controller syntheses of practical systems have been made through the TSFM, such as unmanned marine vehicles, ship fin stabilizing systems, maglev vehicle suspension systems [14][15][16]. On the other hand, the TSFM has been applied to LS systems.…”

Section: Introductionmentioning

confidence: 99%