Railway collaborative ecodrive via dissension based switching nonlinear model predictive control

Farooqi, Hafsa; Incremona, Gian Paolo; Colaneri, Patrizio

doi:10.1016/j.ejcon.2019.04.005

Cited by 13 publications

(13 citation statements)

References 29 publications

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“…In [10], the existence of a unique stationary state marginalized probability vector Πm follows from the fact that the network CTMC is ergodic and has unique stationary network solution. Because our extended network model (13) maintains the ergodicity property, the marginalization (33) also has a unique stationary state solution, found by solving…”

Section: Proofmentioning

confidence: 99%

“…This model was fitted with centralized tuning parameters for influence strength, interagent trust and compliance in [11], and can in some cases be reduced into a lower order marginalized model. A version of this model has been applied on a political problem in [12] and in [13] to describe the decision process of choosing parameters in a collective energy minimization problem for communicating trains.…”

Section: Introductionmentioning

confidence: 99%

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Collective Decision Making using Attractive and Repulsive Forces in Markovian Opinion Dynamics

Heiker¹,

Falcone²

2022

Preprint

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In this paper, we model a decision making process in a network of interacting agents using Markovian opinion dynamics, where each agent switches between decisions according to a continuous time Markov chain. We are inspired by the problem of modeling how interaction among road users in a traffic intersection affects the choice that each agent makes concerning where to exit and whether or not to give way. We introduce attractive/repulsive forces that act within and between groups of agents, with the objective of resembling the behaviors emerging in networks where agents make decisions that depend both on their own preferences and the decisions of the surrounding agents. Our model extends the possibility of using Markovian opinion dynamics to describe a wider scope of practical scenarios, in which groups of stochastic agents make collective decisions. In traffic applications, our method could be used in autonomous vehicles to predict the decisions of human road users.

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Section: Proofmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Collective Decision Making using Attractive and Repulsive Forces in Markovian Opinion Dynamics

Heiker¹,

Falcone²

2022

Preprint

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“…Naturally, many approaches exist for this problem. In (Farooqi et al, 2018) a cooperative solution is presented by using Nonlinear Model Predictive Control, which was further extended with Dissension based Adaptive Law in (Farooqi et al, 2019.). Answer Set Programming was used in (Abels et al, 2019), whilt in (Toletti et al, 2016), the authors propose the utilization of Resource Conflict Graphs.…”

Section: Related Workmentioning

confidence: 99%

MCTS Based Approach for Solving Real-time Railway Rescheduling Problem

Lövétei

Kővári

Bécsi

2021

Period. Polytech. Transp. Eng.

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Solving a real-time Railway Traffic Management Problem (rtRTMP) is a challenging task for human operators. To solve the traffic situation, many factors need to be considered. Traditionally, the most critical factor is the availability of the possible routes and the relative position of the vehicles to each other. Besides, additional constraints can be found, such as the velocity, the length, and railway company regulations. The human decision-making process is essential in case of any disturbance (deviation from the pre-planned timetable). The human operator may solve this situation, but generally, the solution is not optimal. In this paper, the authors present a new method, where they consider an MCTS based algorithm to solve the traffic situation in a fast way in a given station. The performance of the algorithm is examined in two abstraction levels. The main purpose is to execute an experimental study to examine the efficiency of the MCTS based algorithms to solve railway traffic situations.

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“…Shang et al proposed an online energy-saving driving strategy for metro [29]. In Reference [30], a switched nonlinear model predictive controller (NMPC) is designed for collaborative ecodrive control of railway vehicles. Furthermore, to use more renewable energy, a dissension-based adaptive law (DAL) is proposed, to adjust the parameters of the NMPC cost.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Speed Profile Optimization and Sliding Mode Speed Tracking for Metros

et al. 2020

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Nowadays, most metro vehicles are equipped with an automatic train operation (ATO) system, and the speed control method, combining cruise speed planning and proportional-integral-derivative (PID) control, is widely used. The automation is achieved, and the energy-efficient can be improved. This paper presents an improved artificial bee colony algorithm for speed profile optimization with coast mode and an adaptive terminal sliding mode method for speed tracking. Specifically, a multi-objective optimization model is established, which considers energy consumption, comfortableness, and punctuality. Then, a novel artificial bee colony algorithm named regional reinforcement artificial bee colony (RR-ABC) is designed, to search the optimal speed profile with coast mode, in which some improvements are made to speed up convergence and to avoid local optimal solutions. For speed-tracking control, the adaptive terminal sliding mode controller (ATSMC) is used to improve the speed error, robustness, and energy saving. In addition, a disturbance observer (DOB) is designed to improve the anti-interference ability of the system and further improve the robustness and anti-disturbance, which are also conducive to speed error and energy saving. Finally, the line and train data of the Qingdao Metro Line 6 are used for simulation, which proves the effectiveness of the study. Specific to the energy saving rate, and compared with normal algorithms, RR-ABC with coast mode is approximately 9.55%, and ATSMC+DOB is 7.58%.

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Railway collaborative ecodrive via dissension based switching nonlinear model predictive control

Cited by 13 publications

References 29 publications

Collective Decision Making using Attractive and Repulsive Forces in Markovian Opinion Dynamics

Collective Decision Making using Attractive and Repulsive Forces in Markovian Opinion Dynamics

MCTS Based Approach for Solving Real-time Railway Rescheduling Problem

Energy-Efficient Speed Profile Optimization and Sliding Mode Speed Tracking for Metros

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