An Intelligent Coupling 3-Grade Fuzzy Comprehensive Evaluation Approach With AHP for Selection of Levitation Controller of Maglev Trains

During recent years, maglev transportation has made great progress, and as a result, many intelligent levitation control algorithms have emerged. However, enterprises often find it difficult to make a choice when faced with the selection of a controller. The main reason is that the performance evaluation of control algorithms is a complex, multiple-criteria, multifactor coupling problem that cannot be represented by a precise mathematic model. In this paper, a novel artificial intelligent evaluation method for the selection of a levitation controller is developed based on a 3-grade fuzzy method and analytic hierarchy process (AHP). Three kinds of intelligent levitation control algorithms are applied to a full-size test maglev train to collect experimental results with real data. The proposed artificial intelligence method to develop a 3-grade fuzzy multicriteria approach is used to select the best levitation controller for the maglev train. This method can then provide information consultation services to maglev train firms. To the best of our knowledge, for maglev trains, this is the first intelligent evaluation approach with real experimental data. The proposed method can also be applied to other information consultation and decision making systems with appropriate modifications.

[1]  Naveen K. Chilamkurti,et al.  A framework for risk assessment, management and evaluation: Economic tool for quantifying risks in supply chain , 2019, Future Gener. Comput. Syst..

[2]  Wei Xu,et al.  Linear Electric Machines, Drives, and MAGLEVs: An Overview , 2018, IEEE Transactions on Industrial Electronics.

[3]  Jie Li,et al.  An adaptive vibration control method to suppress the vibration of the maglev train caused by track irregularities , 2017 .

[4]  Jie Li,et al.  The Active Control of Maglev Stationary Self-Excited Vibration With a Virtual Energy Harvester , 2015, IEEE Transactions on Industrial Electronics.

[5]  Menghua Zhang,et al.  Adaptive Proportional-Derivative Sliding Mode Control Law With Improved Transient Performance for Underactuated Overhead Crane Systems , 2018, IEEE/CAA Journal of Automatica Sinica.

[6]  Junqi Xu,et al.  Adaptive Neural-Fuzzy Robust Position Control Scheme for Maglev Train Systems With Experimental Verification , 2019, IEEE Transactions on Industrial Electronics.

[7]  Mary Luz Mouronte-López.  Optimizing the Spare Parts Management Process in a Communication Network , 2017, Journal of Network and Systems Management.

[8]  Zhiwu Li,et al.  Operation patterns analysis of automotive components remanufacturing industry development in China , 2017 .

[9]  MengChu Zhou,et al.  Fuzzy Grey Choquet Integral for Evaluation of Multicriteria Decision Making Problems With Interactive and Qualitative Indices , 2021, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[10]  Chen Chen,et al.  Adaptive sliding mode control of maglev system based on RBF neural network minimum parameter learning method , 2019, Measurement.

[11]  Ming Yue,et al.  Neural network based terminal sliding mode control for WMRs affected by an augmented ground friction with slippage effect , 2017, IEEE/CAA Journal of Automatica Sinica.

[12]  Richard D. Thornton,et al.  Efficient and Affordable Maglev Opportunities in the United States , 2009, Proceedings of the IEEE.

[13]  Yin-Feng Xu,et al.  Selecting the Individual Numerical Scale and Prioritization Method in the Analytic Hierarchy Process: A 2-Tuple Fuzzy Linguistic Approach , 2011, IEEE Transactions on Fuzzy Systems.

[14]  Jiuping Xu,et al.  A Direct Consistency Improvement Method for the Probability-Hesitant Analytic Hierarchy Process , 2019, IEEE Access.

[15]  Moeen Radgolchin,et al.  Development of a multi-level adaptive fuzzy controller for beyond pull-in stabilization of electrostatically actuated microplates , 2018 .

[16]  Guobin Lin,et al.  Internet of Things-Based Online Condition Monitor and Improved Adaptive Fuzzy Control for a Medium-Low-Speed Maglev Train System , 2020, IEEE Transactions on Industrial Informatics.

[17]  Zsolt Csaba Johanyák,et al.  Surrogate Model based Optimization of Traffic Lights Cycles and Green Period Ratios using Microscopic Simulation and Fuzzy Rule Interpolation , 2018 .

[18]  MengChu Zhou,et al.  An Evaluation and Optimization Methodology for Efficient Power Plant Programs , 2020, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[19]  Lincheng Shen,et al.  An effective Bayesian network parameters learning algorithm for autonomous mission decision-making under scarce data , 2019, Int. J. Mach. Learn. Cybern..

[20]  Nilanjan Dey,et al.  Case-Based Reasoning for Product Style Construction and Fuzzy Analytic Hierarchy Process Evaluation Modeling Using Consumers Linguistic Variables , 2017, IEEE Access.

[21]  Yuan Zhou,et al.  Stability Analysis of Switched Fuzzy Systems Via Model Checking , 2014, IEEE Transactions on Fuzzy Systems.

[22]  Qingquan Qian,et al.  Dynamic Modeling and Adaptive Sliding Mode Control for a Maglev Train System Based on a Magnetic Flux Observer , 2018, IEEE Access.

[23]  Peide Liu,et al.  Approach to Multi-Attributes Decision Making With Intuitionistic Linguistic Information Based on Dempster-Shafer Evidence Theory , 2018, IEEE Access.

[24]  Claudia-Adina Dragos,et al.  Stabilization of Rössler chaotic dynamical system using fuzzy logic control algorithm , 2014, Int. J. Gen. Syst..

[25]  Guoliang Fan,et al.  Neural-Network-Based Terminal Sliding Mode Control for Frequency Stabilization of Renewable Power Systems , 2018, IEEE/CAA Journal of Automatica Sinica.

[26]  Guangdong Tian,et al.  Green decoration materials selection under interior environment characteristics: A grey-correlation based hybrid MCDM method , 2018 .

[27]  MengChu Zhou,et al.  AHP, Gray Correlation, and TOPSIS Combined Approach to Green Performance Evaluation of Design Alternatives , 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[28]  Junqi Xu,et al.  Hopf bifurcation analysis of maglev vehicle-guideway interaction vibration system and stability control based on fuzzy adaptive theory , 2019, Comput. Ind..

[29]  Abdelaziz Lakehal,et al.  Development and application of a decision making tool for fault diagnosis of turbocompressor basedon Bayesian network and fault tree , 2023, Management and Production Engineering Review.