D. Sun
12 records found
1
Multi-modal transport is getting more popular due to the emergence of new traffic
modes. The increase of modes also adds complexity for the transport researchers.
This paper proposes an augmented link-based super-network approach for modeling
multi-modal transport net ...
modes. The increase of modes also adds complexity for the transport researchers.
This paper proposes an augmented link-based super-network approach for modeling
multi-modal transport net ...
The increasing urbanization, combined with shrinking space for transport infrastructure and private parking, significantly challenges urban accessibility. Moreover, the rising number of vehicles exacerbates congestion in city centers, leading to longer commute times, increased no
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Model predictive control (MPC) and deep reinforcement learning (DRL) have been developed extensively as two independent techniques for traffic management. Although the features of MPC and DRL complement each other very well, few of the current studies consider combining these two
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The railway timetable rescheduling problem is regarded as an efficient way to handle disturbances. Typically, it is tackled using a mixed integer linear programming (MILP) formulation. In this paper, an algorithm that combines both reinforcement learning and optimization is propo
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Adaptive parameterized model predictive control based on reinforcement learning
A synthesis framework
Parameterized model predictive control (PMPC) is one of the many approaches that have been developed to alleviate the high computational requirement of model predictive control (MPC), and it has been shown to significantly reduce the computational complexity while providing compa
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Distributed model predictive control (DMPC) has become a focus in the energy management of shipboard power systems due to its capabilities for privacy preservation, robustness, and distributing computing burdens to local processors. DMPC determines control actions in a distribute
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Optimal Sub-References for Setpoint Tracking
A Multi-level MPC Approach
We propose a novel method to improve the convergence performance of model predictive control (MPC) for setpoint tracking, by introducing sub-references within a multilevel MPC structure. In some cases, MPC is implemented with a short prediction horizon due to limited on-line comp
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A novel bi-level temporally-distributed MPC approach
An application to green urban mobility
Model predictive control (MPC) has been widely used for traffic management, such as for minimizing the total time spent or the total emissions of vehicles. When long-term green urban mobility is considered including e.g. a constraint on the total yearly emissions, the optimizatio
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While Model Predictive Control (MPC) is a promising approach for network-wide control of urban traffic, the computational complexity of the, often nonlinear, online optimization procedure is too high for real-time implementations. In order to make MPC computationally efficient, t
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This thesis focuses on management and control of traffic networks, including urban networks and freeway networks, in which we aim to reduce traffic congestion by minimizing the total time spent of all the vehicles in the network, and also consider green mobility by minimizing the
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Traffic control is essential to reduce congestion in both urban and freeway traffic networks. These control measures include ramp metering and variable speed limits for freeways, and traffic signal control for urban traffic. However, current traffic control methods are either too
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In general, the performance of model-based controllers cannot be guaranteed under model uncertainties or disturbances, while learning-based controllers require an extensively sufficient training process to perform well. These issues especially hold for large-scale nonlinear syste
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