Trajectory tracking for uncertainty time delayed-state self-balancing train vehicles using observer-based adaptive fuzzy control

Wu, Tzu Sung; Karkoub, Mansour; Weng, Chien Chih; Yu, Wen

doi:10.1016/j.ins.2015.06.019

Cited by 10 publications

(3 citation statements)

References 30 publications

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“…Fuzzy systems have been used to learn unknown disturbances online, 25,26,106 unknown dynamics, 27 unknown system parameters, 107 uncertainty bounds, 29 and control laws. 108,109 The fuzzy system’s parameters were adapted, and the closed-loop stability was analyzed, both by Lyapunov theory.…”

Section: Control Methods Based On Fuzzy Systems For Underactuated Sysmentioning

confidence: 99%

A survey on the application of fuzzy systems for underactuated systems

Huang

Ralescu

Gao

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part I:

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Underactuated systems represent a class of mechanical systems with fewer independent control actuators than degrees of freedom. The control problem of underactuated systems is one of the most challenging in the theory of nonlinear control because such systems exhibit strong coupling and highly nonlinear characteristics. Fuzzy systems have been successfully applied to control underactuated systems in both theoretical analysis and practical application. This survey describes the model and application of underactuated systems and classifies fuzzy systems in a straightforward manner. Moreover, this study analyzes problems facing conventional control methods and presents the development of fuzzy systems to solve these problems.

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Section: Control Methods Based On Fuzzy Systems For Underactuated Sysmentioning

confidence: 99%

A survey on the application of fuzzy systems for underactuated systems

Huang

Ralescu

Gao

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part I:

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show abstract

“…Real-time application evaluation, such as analysis of car-specific on-board accumulation for comfort assessment and flexible pricing strategy and real-time train rescheduling strategies, can be optimized according these estimated SST results. It can also provide comprehensive and effective input data for trajectory tracking [35] , trajectory pattern mining [36] , [37] , [38] and trajectory prediction [39] . Specially, this proposed approach can significantly benefit for real-time monitoring SST of suspected COVID-19 patients at the URT network.…”

Section: Experiments and Analysismentioning

confidence: 99%

Spatio-temporal trajectory estimation based on incomplete Wi-Fi probe data in urban rail transit network

Jiang

Sun

et al. 2021

Knowledge-Based Systems

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This study presents a methodology for estimating passenger’s spatio-temporal trajectory with personalization and timeliness by using incomplete Wi-Fi probe data in urban rail transit network. Unlike the automatic fare collection data that only records passenger’s entries and exits, the Wi-Fi probe data can capture more detailed passenger movements, such as riding a train or waiting on a platform. However, the estimation of spatio-temporal trajectories remains as a challenging task because a few unfavorable situations could result into deficient data. To address this problem, we first describe the Wi-Fi probe data and summarize their common defects. Then, the n -gram method is developed to infer missing spatio-temporal location information. Next, an estimation algorithm is designed to generate feasible spatio-temporal trajectories for each individual passenger by integrating multiple data sources, i.e., urban rail transit network topology, Wi-Fi probe data, train schedules, etc. This proposed method is tested on both simulated data in blind experiments and real-world data from a complex urban rail transit network. The results of case study show that 93% of passengers’ unique physical routes can be estimated. Then, for 80% of passengers, the number of feasible spatio-temporal trajectories can be reduced to one or two. Potential applications of the trajectory estimation approach are also identified.

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“…For example, Liu et al proposed a high-speed railway control system based on the fuzzy control method and designed a control system in the Matlab software according to the expert experience and knowledge [11]. Wu et al used variable structure technique and a time-delayed compensator, to design a state observer-based adaptive fuzzy controller to approximate the unknown system parameters, and thus trajectory tracking problem of a series of twowheeled self-balancing vehicles can be addressed [12]. Gu et al proposed a new energy-efficient train operation model based on real-time traffic information from the geometric and topographic points of view through a nonlinear programming method [13].…”

Section: Introductionmentioning

confidence: 99%

Smart Train Operation Algorithms based on Expert Knowledge and Reinforcement Learning

Zhou

Song

Xue

et al. 2020

Preprint

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During recent decades, the automatic train operation (ATO) system has been gradually adopted in many subway systems. On the one hand, it is more intelligent than traditional manual driving; on the other hand, it increases the energy consumption and decreases the riding comfort of the subway system. This paper proposes two smart train operation algorithms based on the combination of expert knowledge and reinforcement learning algorithms. Compared with previous works, smart train operation algorithms can realize the control of continuous action for the subway system and satisfy multiple objectives (the safety, the punctuality, the energy efficiency, and the riding comfort) without using an offline optimized speed profile. Firstly, through analyzing historical data of experienced subway drivers, we summarize the expert knowledge rules and build inference methods to guarantee the riding comfort, the punctuality and the safety of the subway system. Then we develop two algorithms to realize the control of continuous action and to ensure the energy efficiency of train operation. Among them, one is the smart train operation (STO) algorithm based on deep deterministic policy gradient named (STOD) and another is the smart train operation algorithm based on normalized advantage function (STON). Finally, we verify the performance of proposed algorithms via some numerical simulations with the real field data collected from the Yizhuang Line of the Beijing Subway and their performance will be compared with existing ATO algorithms. The results of numerical simulations show that the developed smart train operation systems are better than manual driving and existing ATO algorithms in respect of energy efficiency. In addition, STOD and STON have the ability to adapt to different trip times and different resistance conditions.

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Trajectory tracking for uncertainty time delayed-state self-balancing train vehicles using observer-based adaptive fuzzy control

Cited by 10 publications

References 30 publications

A survey on the application of fuzzy systems for underactuated systems

A survey on the application of fuzzy systems for underactuated systems

Spatio-temporal trajectory estimation based on incomplete Wi-Fi probe data in urban rail transit network

Smart Train Operation Algorithms based on Expert Knowledge and Reinforcement Learning

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