Article
Automation & Control Systems
Sungho Shin, Victor M. M. Zavala
Summary: We propose a time-coarsening strategy called diffusing-horizon MPC to address the computational challenges of optimal control problems with multiple timescales. By using a sparse time discretization grid, motivated by the property of exponential decay of sensitivity (EDS), we establish conditions under which this property holds for a constrained MPC formulation. Furthermore, we demonstrate the effectiveness of this approach using a case study with a HVAC plant.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Automation & Control Systems
Raffaele Soloperto, Matthias A. Muller, Frank Allgower
Summary: We propose a novel learning-based model predictive control framework for nonlinear systems that guarantee the closed-loop learning of the controlled system. By combining an economic cost function with a user-defined learning cost function, we incentivize the learning of the unknown system. We show that existing MPC schemes can be easily modified to ensure closed-loop learning of the system by including a suitable discount factor in the learning cost function and implementing an additional constraint in the original MPC scheme.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Computer Science, Interdisciplinary Applications
Lloyd MacKinnon, Hao Li, Christopher L. E. Swartz
Summary: Robust MPC aims to minimize the impact of uncertainty on MPC performance by directly incorporating MPC subproblems into the overall formulation. This approach, which combines a scenario-based method with embedded closed-loop prediction, outperforms standard MPC formulation in linear and nonlinear case studies.
COMPUTERS & CHEMICAL ENGINEERING
(2021)
Article
Computer Science, Interdisciplinary Applications
Yunning Yang, Renchu He, Guo Yu, Wei Du, Minglei Yang, Wenli Du
Summary: This paper addresses a crude oil scheduling problem in a marine-access refinery, considering the storage, blending, and processing of complex types of crude and the demands of downstream units. A full-space MINLP model is proposed to optimize the operation of crude unloading and blending, and handle the complex mixtures in the tank. An efficient rolling horizon approach is developed for large model sizes. Case studies show the efficiency of this approach and the influence of new constraints on the final solution.
COMPUTERS & CHEMICAL ENGINEERING
(2023)
Article
Energy & Fuels
Kaiyu Cao, Sang Hwan Son, Jiyoung Moon, Joseph Sang-Il Kwon
Summary: An integrated model and reduced-order model were developed to consider the scheduling and control of hydraulic fracturing operations for enhancing the economic performance of shale gas systems. Through an online integrated framework and the use of an offset-free model predictive control system, the performance degradation caused by plant-model mismatch was reduced, resulting in an overall improvement of the economic performance of the shale gas system.
Article
Engineering, Chemical
Praveen Sundaresan Ramesh, Christopher L. E. Swartz, Prashant Mhaskar
Summary: The concept of dynamic real-time optimization (DRTO) is introduced, and a closed-loop DRTO (CL-DRTO) formulation is proposed to handle unstable operating points. By embedding a stabilizing MPC to handle trajectory tracking, the resulting CL-DRTO problem is reformulated.
Article
Green & Sustainable Science & Technology
Gang Li, Weidong Zhu
Summary: This paper presents a control method for an infinitely variable transmission (IVT) that combines nonlinear closed-loop control with integral time-delay feedback control to adjust its speed ratios according to variable tidal speeds. Experimental results show that the proposed control strategy achieves good tracking response and reduces output speed fluctuations.
Article
Agriculture, Multidisciplinary
Fernando Montenegro-Dos Santos, Francisco Perez-Galarce, Carlos Monardes-Concha, Alfredo Candia-Vejar, Marcelo Seido-Nagano, Javier Gomez-Lagos
Summary: Agriculture has evolved from a human-intensive activity to a highly automated process, with multiple technological advances being incorporated to increase harvest efficiency. However, uncertainty from weather conditions and crop characteristics pose challenges. This study proposes a non-myopic rolling horizon method to reschedule agricultural harvest plans, exemplified by olive oil production, and formulates a bi-objective problem to maximize production and minimize plan variability.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2023)
Article
Automation & Control Systems
Ian McInerney, Eric C. Kerrigan, George A. Constantinides
Summary: First-order optimization solvers, like the fast gradient method, are used to solve model predictive control problems. The convergence rate of these solvers is affected by the problem data, requiring a large number of iterations for ill-conditioned problems. To reduce iterations, a method for computing a horizon-independent preconditioning matrix for the Hessian is presented. The proposed method produces comparable performance to an optimal preconditioner and significantly speeds up the fast gradient method.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Transportation Science & Technology
Housheng Zhou, Jianguo Qi, Lixing Yang, Jungang Shi, Pengli Mo
Summary: This study proposes a joint optimization method for train scheduling and rolling stock circulation planning on a tidal oversaturated metro line. By using different types of rolling stocks with various loading capacities to meet the passenger demand in different periods, transportation costs can be effectively reduced and passenger waiting time can be minimized.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2022)
Article
Computer Science, Information Systems
Yuhe Chen, Xuying Zhou, Wei Wang, Huiqiong Wang, Zhi Zhang, Zhaoyang Zhang
Summary: The letter proposes a delay-optimal multi-destination computation offloading system by optimizing task assignment and offloading scheduling, which reduces the delay by up to 62.4% compared to non-scheduling offloading method.
IEEE WIRELESS COMMUNICATIONS LETTERS
(2021)
Article
Economics
Xiaoyu Liu, Azita Dabiri, Jing Xun, Bart De Schutter
Summary: This paper addresses the train scheduling problem in metro networks, taking into account time-dependent passenger origin-destination demands and train speed profiles. By developing an extended passenger absorption model and a bi-level MPC approach, the passenger satisfaction and operational costs are jointly optimized. Numerical experiments based on real-life data showcase the effectiveness of the proposed approach.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2023)
Article
Computer Science, Information Systems
Sukmun Oh, Kwanghun Chung, Jiwoong Choi
Summary: In this paper, a practical approach called Resource-oriented Augmentation of a Train Timetable (RATT) is presented to tackle the integrated problem of train timetabling and rolling stock scheduling. The RATT approach extends an existing timetable through train augmentation under given constraints, and includes a mixed integer programming model, train generation technique, and iterative decomposition scheme to solve real-world problems from a Korean high-speed railway company.
Article
Engineering, Civil
Xi Wang, Shukai Li, Tao Tang, Lixing Yang
Summary: This article investigates the real-time train regulation and passenger load control problem in metro rail lines during peak hours. It proposes a predictive control strategy and an event-triggered strategy to improve operation efficiency and riding comfort. Numerical examples based on the Beijing Yizhuang Metro Line demonstrate the effectiveness of the proposed scheme.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Han Leng, Lihui Ren, Yuanjin Ji
Summary: This paper proposes a scalable cascade modular path following control strategy for a newly designed gantry virtual track train (G-VTT), focusing on lateral control during low-speed turning manoeuvers. Mathematical modeling and frequency domain analysis are used to optimize the stability and following ability of the vehicle, and steady-state and dynamic forward predictive models are proposed to compensate for lag time.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2022)
Article
Computer Science, Information Systems
Mark Bosschaart, Egidio Quaglietta, Bob Janssen, Rob M. P. Goverde
INFORMATION SYSTEMS
(2015)
Article
Computer Science, Interdisciplinary Applications
Nikola Besinovic, Rob M. P. Goverde, Egidio Quaglietta
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
(2017)
Article
Economics
Nikola Besinovic, Rob M. P. Goverde, Egidio Quaglietta, Roberto Roberti
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
(2016)
Article
Transportation Science & Technology
Rob M. P. Goverde, Nikola Besinovic, Anne Binder, Valentina Cacchiani, Egidio Quaglietta, Roberto Roberti, Paolo Toth
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2016)
Article
Transportation Science & Technology
Egidio Quaglietta, Paola Pellegrini, Rob M. P. Goverde, Thomas Albrecht, Birgit Jaekel, Gregory Marliere, Joaquin Rodriguez, Twan Dollevoet, Bruno Ambrogio, Daniele Carcasole, Marco Giaroli, Gemma Nicholson
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2016)
Article
Transportation Science & Technology
Francesco Corman, Egidio Quaglietta, Rob M. P. Goverde
TRANSPORTATION PLANNING AND TECHNOLOGY
(2018)
Article
Computer Science, Interdisciplinary Applications
Egidio Quaglietta
SIMULATION MODELLING PRACTICE AND THEORY
(2014)
Article
Transportation
Egidio Quaglietta, Panagiotis Spartalis, Meng Wang, Rob M. P. Goverde, Paul van Koningsbruggen
Summary: This paper introduces the concept of Virtual Coupling train operations and addresses the concern of safety violations. By using a dynamic safety margin, train separations can be adjusted to maintain safe distances even during operational hazards, leading to increased capacity and safer operations.
JOURNAL OF RAIL TRANSPORT PLANNING & MANAGEMENT
(2022)
Article
Engineering, Civil
Nikola Besinovic, Yihui Wang, Songwei Zhu, Egidio Quaglietta, Tao Tang, Rob M. P. Goverde
Summary: This paper proposes an integrated disruption management model that combines train rescheduling and passenger flow control to quickly recover train operation and reduce passenger waiting time outside stations. An iterative metaheuristic approach is used to solve the integrated disruption management problem, demonstrating the effectiveness of the solution method.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Rafael Mendes Borges, Egidio Quaglietta
Summary: The Hyperloop is a concept of ground transportation system aiming to provide a fast, inexpensive, and sustainable alternative. While a theoretical investigation shows that Virtual Coupling may be a more satisfactory operational concept offering higher transport capacity while respecting safety and comfort standards.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Transportation
R. N. van Lieshout, J. M. van den Akker, R. Mendes Borges, T. Druijf, E. Quaglietta
Summary: This paper analyzes the effectiveness of decentralized strategies for dispatching rolling stock and train drivers in a railway system, and finds that such strategies can ensure the target frequencies of the lines and highly regular train services, with strategies allowing rolling stock to switch between lines performing better.
JOURNAL OF RAIL TRANSPORT PLANNING & MANAGEMENT
(2022)
Article
Transportation
Ziyulong Wang, Egidio Quaglietta, Maarten G. P. Bartholomeus, Rob M. P. Goverde
Summary: This paper proposes and evaluates different ATO architecture configurations to improve capacity and punctuality in railways. The analysis shows that different configurations have diverse advantages and limitations depending on railway governance and technological development. Additionally, a scientific argumentation is provided for the ATO-over-ETCS architecture being developed by the European Union Agency for Railways.
JOURNAL OF RAIL TRANSPORT PLANNING & MANAGEMENT
(2022)
Article
Transportation
Nikola Besinovic, Egidio Quaglietta, Rob M. P. Goverde
JOURNAL OF RAIL TRANSPORT PLANNING & MANAGEMENT
(2013)
Article
Transportation
Egidio Quaglietta, Francesco Corman, Rob M. P. Goverde
JOURNAL OF RAIL TRANSPORT PLANNING & MANAGEMENT
(2013)
Article
Transportation Science & Technology
Yue Zhao, Liujiang Kang, Huijun Sun, Jianjun Wu, Nsabimana Buhigiro
Summary: This study proposes a 2-population 3-strategy evolutionary game model to address the issue of subway network operation extension. The analysis reveals that the rule of maximum total fitness ensures the priority of evolutionary equilibrium strategies, and proper adjustment minutes can enhance the effectiveness of operation extension.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Hongtao Hu, Jiao Mob, Lu Zhen
Summary: This study investigates the challenges of daily storage yard management in marine container terminals considering delayed transshipment of containers. A mixed-integer linear programming model is proposed to minimize various costs associated with transportation and yard management. The improved Benders decomposition algorithm is applied to solve the problem effectively and efficiently.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Zhandong Xu, Yiyang Peng, Guoyuan Li, Anthony Chen, Xiaobo Liu
Summary: This paper studied the impact of range anxiety among electric vehicle drivers on traffic assignment. Two types of range-constrained traffic assignment problems were defined based on discrete or continuous distributed range anxiety. Models and algorithms were proposed to solve the two types of problems. Experimental results showed the superiority of the proposed algorithm and revealed that drivers with heightened range anxiety may cause severe congestion.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Chuanjia Li, Maosi Geng, Yong Chen, Zeen Cai, Zheng Zhu, Xiqun (Michael) Chen
Summary: Understanding spatial-temporal stochasticity in shared mobility is crucial, and this study introduces the Bi-STTNP prediction model that provides probabilistic predictions and uncertainty estimations for ride-sourcing demand, outperforming conventional deep learning methods. The model captures the multivariate spatial-temporal Gaussian distribution of demand and offers comprehensive uncertainty representations.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Benjamin Coifman, Lizhe Li
Summary: This paper develops a partial trajectory method for aligning views from successive fixed cameras in order to ensure high fidelity with the actual vehicle movements. The method operates on the output of vehicle tracking to provide direct feedback and improve alignment quality. Experimental results show that this method can enhance accuracy and increase the number of vehicles in the dataset.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Mohsen Dastpak, Fausto Errico, Ola Jabali, Federico Malucelli
Summary: This article discusses the problem of an Electric Vehicle (EV) finding the shortest route from an origin to a destination and proposes a problem model that considers the occupancy indicator information of charging stations. A Markov Decision Process formulation is presented to optimize the EV routing and charging policy. A reoptimization algorithm is developed to establish the sequence of charging station visits and charging amounts based on system updates. Results from a comprehensive computational study show that the proposed method significantly reduces waiting times and total trip duration.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)