Abstract
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.
Highlights
With the rapid improvement of the worldwide economic situation and, in particular, urbanization, urban traffic has many difficult problems Examples include traffic accidents, and more so, latterly, exhaust pollution
The evaluation results for the adaptive neural-fuzzy sliding mode controller (ANFSMC) and radial basis function (RBF) neural network sliding mode controllers can be obtained in the same manner and are as follows: BANFSMC = [0.287, 0.126, 0.056] BRBF = [0.056, 0.318, 0.318]
To the best of our knowledge, the proposed method, is the first artificial intelligence evaluation method enabling the selection of maglev levitation controllers capable of utilizing a 3-grade fuzzy multicriteria approach
Summary
With the rapid improvement of the worldwide economic situation and, in particular, urbanization, urban traffic has many difficult problems Examples include traffic accidents, and more so, latterly, exhaust pollution. Sun et al [15] proposed a nonlinear robust control law with an adaptive fuzzy logic approximator These levitation control algorithms have their own advantages and disadvantages, there is still no method for evaluating them, despite the fact that many maglev train companies want to choose new intelligent control algorithms, but in the face of the variety of control algorithms, it is difficult to determine which one is the most suitable. To evaluate and compare the performance of different levitation controllers in a one-dimensional space, it is important to scientifically and objectively synthesize a multi index problem into a single index form and subsequently be used by maglev trains companies to select new intelligent control algorithms. To analyze and evaluate the levitation control algorithm, which can provide consulting services for enterprises to select the control algorithms, a new 3-grade fuzzy comprehensive evaluation approach with AHP method is proposed. Where, ηR < 0.1 and the pairwise comparison matrix is generally considered to have complete consistency; otherwise, the matrix needs to be readjusted until it has satisfactory consistency
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