We develop a data-driven approach to Pareto optimal control of large-scale systems, where decision makers know only their local dynamics. Using reinforcement learning, we design a control strategy that optimally balan...
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We develop a data-driven approach to Pareto optimal control of large-scale systems, where decision makers know only their local dynamics. Using reinforcement learning, we design a control strategy that optimally balances multiple objectives. The proposed method achieves near-optimal performance and scales well with the total dimension of the system. Experimental results demonstrate the effectiveness of our approach in managing multi-area power systems.
The article studied a finite-iteration control problem. Based on the multi-agent systems (MASs), where each agent has a linear structure, the article proposed the controller for finite-iteration tracking problem. Comb...
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While stability analysis is a mainstay for control science, especially computing regions of attraction of equilibrium points, until recently most stability analysis tools always required explicit knowledge of the mode...
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While stability analysis is a mainstay for control science, especially computing regions of attraction of equilibrium points, until recently most stability analysis tools always required explicit knowledge of the model or a high-fidelity simulator representing the system at hand. In this work, a new data-driven Lyapunov analysis framework is proposed. Without using the model or its simulator, the proposed approach can learn a piecewise affine Lyapunov function with a finite and fixed offline dataset. The learnt Lyapunov function is robust to any dynamics that are consistent with the ofline dataset, and its computation is based on second-order cone programming. Along with the development of the proposed scheme, a slight generalization of the classical Lyapunov stability criteria is derived, enabling an iterative inference algorithm to augment the region of attraction.
Based on large scale of calling record data collected by 12328 Transportation Service Supervision Hotline System, this paper conduct serveral analysis from the hotline's complaint issues. For the urban passenger t...
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ISBN:
(纸本)9798350321050
Based on large scale of calling record data collected by 12328 Transportation Service Supervision Hotline System, this paper conduct serveral analysis from the hotline's complaint issues. For the urban passenger traffic field, studies on the population-based number of complaint issues were conducted to analyze the operational service level of Shanghai, Beijing and other cities with large populations and relatively well-developed public transportation systems. Frequently reported complaint issues and its time distributions in Shanghai were also concluded as a part of research outcomes. Furthermore, this paper established an assessment method towards complaint issues on urban passenger traffic field, the assessment indicators were designed out of consideration on the perspectives of quantity, efficiency and quality. Combining with the recent hot issues of epidemic prevention and anabatic cargo transportation problem, this paper carried out a text clustering method based on LDA topic method, four types of critical cargo transpotation problems throughout the hotline's receving issues were clustered as the meaningful results of the algorithmic experiment.
This paper presents a model-free H2/H∞ Q-learning predictive control strategy for linear discrete-time systems. To design predictive controller with the system measured states, a policy iteration solution algorithm i...
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This article considers the semiglobal cooperative suboptimal output regulation problem of heterogeneous multi-agent systems with unknown agent dynamics in the presence of input saturation. To solve the problem, we dev...
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This article considers the semiglobal cooperative suboptimal output regulation problem of heterogeneous multi-agent systems with unknown agent dynamics in the presence of input saturation. To solve the problem, we develop distributed suboptimal control strategies from two perspectives, namely, model-based and data-driven. For the model-based case, we design a suboptimal control strategy by using the low-gain technique and output regulation theory. Moreover, when the agents' dynamics are unknown, we design a data-driven algorithm to solve the problem. We show that proposed control strategies ensure each agent's output gradually follows the reference signal and achieves interference suppression while guaranteeing closed-loop stability. The theoretical results are illustrated by a numerical simulation example.
Traffic state prediction, a classical task for traffic management, is a central component of intelligent transport systems to maintain safe and efficient operation. While extensive and intensive research has been cond...
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Traffic state prediction, a classical task for traffic management, is a central component of intelligent transport systems to maintain safe and efficient operation. While extensive and intensive research has been conducted on traffic state prediction, most studies have concentrated on enhancing the accuracy of specific traffic state parameters. However, traffic state is a co-evolutionary multivariate time series with various parameters such as flow, velocity, occupancy, etc. At the same time, traffic state data will inevitably be lost during collection. So accurate traffic prediction still faces the following challenges: First, how to deal with the complex missing situations in observational data? Second, how to learn the co-evolutionary relationships between different traffic state parameters while mining the high-dimensional spatio-temporal traffic state patterns? In this paper, we propose a mechanism-data blending-driven co-evolving traffic state parameter prediction method: multi-parameter hybrid tensor deep learning networks (MHT-Net), which consists of a multi-parameter tensor graph convolutional network (MTGCN) and a tensor recurrent neural network (T-RNN). MTGCN implements knowledge embedding of synergistic mechanisms between traffic parameters, ensuring that the road network spatial dependency and the synergistic influence relationship of the parameters can be obtained simultaneously;T-RNN is used to learn high-dimensional temporal features of traffic states. Experiment results on a real-world dataset from Jiangsu province outperform the state-of-the-art baselines, demonstrating the efficacy of the proposed method and providing an effective tool for traffic state prediction with missing values. A mechanism-data blending driven co-evolving traffic state parameter prediction method, multi-parameters hybrid tensor deep learning networks (MHT-Net) is proposed, which implements knowledge embedding of synergistic mechanisms between traffic parameters and learn th
As the main component of gas-steam combined cycle power plant, heavy-duty gas turbine (HDGT) with the property of safety, reliability and high efficiency, which can greatly improve the performance of deep-peak shaving...
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ISBN:
(纸本)9798350321050
As the main component of gas-steam combined cycle power plant, heavy-duty gas turbine (HDGT) with the property of safety, reliability and high efficiency, which can greatly improve the performance of deep-peak shaving capability and fast frequency modulation performance of power system. With respect to the variable load control strategy for HDGT, an interval observer based extended model predictive control is proposed in this study by combining the interval observer and model predictive control, which is built upon the stability analysis of linear state feedback control. The salient property of proposed method lies in the reduction of negative effect caused by external in the process of variable load control. The typical study cases are selected for verifying the effect of the proposed methods, which outperforms the others methods in aspect of stable and safe.
In this paper, a vehicle formation control method based on model-free adaptive iterative learning is proposed for vehicles with the same turn at a road intersection. At road intersections, inconsistencies in speed and...
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In this paper, A novel model-free adaptive recursive optimal control (MFAROC) is proposed for time-varying nonlinear systems. On the basis of the optimization index designed based on the recursive idea, a novel time-v...
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