Bio-Inspired Speed Curve Optimization and Sliding Mode Tracking Control for Subway Trains

Cao, Yuan; Wang, Zhengchao; Liu, Feng; Li, Peng; Xie, Guo

doi:10.1109/tvt.2019.2914936

Cited by 219 publications

(110 citation statements)

References 23 publications

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“…The simulation results show that the BSO-HMISG algorithm can generate more accurate parameter estimates than the BSO-HSG algorithm. The proposed methods in this paper can be extended to study the parameter estimation problems of different systems with complex disturbances [72][73][74][75] and can be applied to other engineering areas [76][77][78][79][80][81] such as signal modeling and communication networked systems. [82][83][84][85][86]…”

Section: Resultsmentioning

confidence: 99%

Recursive parameter estimation methods and convergence analysis for a special class of nonlinear systems

Xiao

Ding

2019

Intl J Robust & Nonlinear

120

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Summary This paper is concerned with the joint estimation of states and parameters of a special class of nonlinear systems, ie, bilinear systems. The key is to investigate new estimation methods for interactive state and parameter estimation of the considered system based on the interactive estimation theory. Because the system states are unknown, a bilinear state observer is established based on the Kalman filtering principle. Then, the unavailable states are updated by the state observer outputs recursively. Once the state estimates are obtained, the bilinear state observer–based hierarchical stochastic gradient algorithm is developed by using the gradient search. For the purpose of improving the convergence rate and the parameter estimation accuracy, a bilinear state observer–based hierarchical multi‐innovation stochastic gradient algorithm is proposed by expanding a scalar innovation to an innovation vector. The convergence analysis indicates that the parameter estimates can converge to their true values. The numerical example illustrates the effectiveness of the proposed algorithms.

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Section: Resultsmentioning

confidence: 99%

Recursive parameter estimation methods and convergence analysis for a special class of nonlinear systems

Xiao

Ding

2019

Intl J Robust & Nonlinear

120

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“…In the study, with reference to the setting of Babaveisi et al [57], the crossover and mutation probabilities of NSGA-II were, respectively, set as 0.8 and 0.25, and the iteration, the population size, and the mating pool size were set as 80, 25, and 19, respectively. In terms of the range of parameters, the range of K was set as [1,200], the range of η was [0, 1], and…”

Section: Results Of Parametersmentioning

confidence: 99%

“…Wu [27] proposed a method named adaptive terminal sliding mode control. Cao et al [1] and Liu et al [2] designed a sliding mode controller and a predictive control algorithm, respectively, for the ATO systems to track the optimized target speed curve and found that they have superiority compared with the PID controller. Ke et al [3] used the MAX-MIN ant system to optimize the target speed curve and designed a fuzzy PID controller to track the speed curve.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Assume e (0) � e (0) (1), e (0) (2), • • • , e (0) (n) was the original sequence consisting of the sequence of error or deviation e(t) between input and output. en e (1) is the first-order accumulated sequence, that is, e (1) � e (1) (1), e (1) (2), • • • , e (1) (n) , where e (1)…”

Section: Grey Modelmentioning

confidence: 99%

“…For improving comfort and stopping accuracy, and energy saving, a driverless train needs a very stable ATO system. e speed curve and the speed curve tracking ability are two keys to optimize ATO systems [1][2][3]. e accuracy of speed curve tracking is related to the adopted control method.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

NSGA-II-Based Parameter Tuning Method and GM(1,1)-Based Development of Fuzzy Immune PID Controller for Automatic Train Operation System

Chu

Dong

et al. 2020

Mathematical Problems in Engineering

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Automatic train operation (ATO) system is one of the important components in advanced train operation control systems. Ideal controllers are expected for the automatic driving function of ATO systems. Aiming at the intelligence requirements of the systems, an NSGA-II-based parameter tuning method for the fuzzy immune PID (FI-PID) controller and a grey model GM(1,1)-based fuzzy grey immune PID (FGI-PID) controller were proposed. Taking a maglev train’s model as the control object and a velocity-time curve as the input, the feasibility of the parameter tuning method for the FI-PID controller and the applicability of the FI-PID controller and the FGI-PID controller for the ATO system were tested. The results showed that the optimized parameters were ideal, the two controllers all showed good performance on the indicators of traceability and comfort level, and the FGI-PID controller performed better than the FI-PID controller. The results exhibited the effectiveness of the proposed methods.

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Three‐stage least squares‐based iterative estimation algorithms for bilinear state‐space systems based on the bilinear state estimator

Liu

Zhang

Ding

et al. 2020

Adaptive Control & Signal

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Because of the product item of the control input and the state vector, the identification of bilinear systems is difficult. This paper considers the combined parameter and state estimation problems of bilinear state-space systems. On the basis of the observability canonical form and the model transformation, an identification model with a linear combination of the system parameters is obtained. Using the hierarchical principle, the identification model is decomposed into three submodels with fewer variables, and a three-stage least squares-based iterative (3S-LSI) algorithm is presented to estimate the system parameters. Furthermore, we derive a state estimator (SE) for estimating the unknown states, and present an SE-3S-LSI algorithm for estimating the unknown parameters and states simultaneously. After that, the least squares-based iterative algorithm is presented as a comparison. By analyzing the estimation results and the calculation amount, these two algorithms can identify the bilinear system effectively but the 3S-LSI algorithm can improve the computational efficiency. The simulation results indicate the effectiveness of the proposed algorithms.

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Bio-Inspired Speed Curve Optimization and Sliding Mode Tracking Control for Subway Trains

Cited by 219 publications

References 23 publications

Recursive parameter estimation methods and convergence analysis for a special class of nonlinear systems

Recursive parameter estimation methods and convergence analysis for a special class of nonlinear systems

NSGA-II-Based Parameter Tuning Method and GM(1,1)-Based Development of Fuzzy Immune PID Controller for Automatic Train Operation System

Three‐stage least squares‐based iterative estimation algorithms for bilinear state‐space systems based on the bilinear state estimator

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