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IEEE PES Transmission and Distribution Conference and Exhibition - Latin America (T and D LA)

作者： Castellano, Agustin Martinez, Camila Monzon, Pablo Andres Bazerque, Juan Ferragut, Andres Paganini, Fernando Univ Republica Montevideo Uruguay Univ ORT Uruguay Montevideo Uruguay

ISBN: (纸本)9781728141558

We address the problem of scheduling water resources in a power system via approximate dynamic programming. To this goal, we model a finite horizon economic dispatch problem with convex stage cost and affine dynamics, and consider a quadratic approximation of the value functions. Evaluating the achieved policy entails solving a quadratic program at each time step, while value function fitting can be cast as a semidefinite program. We test our proposed algorithm on a simplified version of the Uruguayan power system, achieving a four percent cost reduction with respect to the myopic policy.

关键词： approximate dynamic programming economic dispatch convex optimization power systems

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Decentralized Waveform Co-design for Integrated Sensing and Communications systems via approximate dynamic programming 21

Decentralized Waveform Co-design for Integrated Sensing and ...

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IEEE 21st Consumer Communications and Networking Conference (CCNC)

作者： Doly, Shammi A. Chiriyath, Alex R. Herschfelt, Andrew Azam, Md Ali Ragi, Shankarachary Bliss, Daniel W. Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85287 USA Arizona State Univ Ctr Wireless Informat Syst & Computat Architectur Tempe AZ 85281 USA

ISBN: (纸本)9798350304572

The increasing demand for cost-efficient yet reliable swarms of multi-function unmanned aerial systems (UASs) has tremendous potential in both military and civilian applications. Advancements in ISACs research, such as RF Convergence exclusively provide a promising path toward implementing multi-function UASs, often limited by insufficient front-end capabilities as the network scales up. Because the interconnection bandwidth and computational complexity required to process the data among the ISACs nodes using centralized architectures are high. In this paper, we propose a decentralized waveform co-design method to reduce computational complexity while maximizing the mutual benefits of users in a multi-function UASs network based on the theory of decentralized, partially-observable Markov decision processes (Dec-POMDPs). To address the computational intractability of solving Dec-POMDPs (as with any decision-theoretic framework), we extend an approximate dynamic programming approach we recently developed-nominal belief-state optimization (NBO) in the context of radar-communications waveform co-design. We conduct a numerical study to benchmark the performance of the DecPOMDP-based waveform design approach against a centralized decision optimization approach, which demonstrates a 50% reduction in computation time at the cost of moderate loss in ranging precision.

关键词： RF convergence ISACs Dec-POMDP waveform co-design UASs target tracking approximate dynamic programming

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approximate dynamic programming STRATEGY FOR DUAL ADAPTIVE CONTROL

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IFAC Proceedings Volumes 2005年第1期38卷 459-464页

作者： Jong Min Lee Jay H. Lee School of Chemical and Biomolecular Engineering Georgia Institute of Technology Atlanta GA 30332 USA

An approximate dynamic programming (ADP) strategy for a dual adaptive control problem is presented. An optimal control policy of a dual adaptive control problem can be derived by solving a stochastic dynamic programming problem, which is computationally intractable using conventional solution methods that involve sampling of a complete hyperstate space. To solve the problem in a computationally amenable manner, we perform closed-loop simulations with different control policies to generate a data set that defines a subset of a hyperstate within which the Bellman equation is iterated. A local approximator with a penalty function is designed for estimation of cost-to-go values over the continuous hyperstate space. An integrating process with an unknown gain is used for illustration.

关键词： approximate dynamic programming dual control adaptive control stochastic dynamic programming

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Online Operation of Bike Sharing System: An approximate dynamic programming Approach 20

Online Operation of Bike Sharing System: An Approximate Dyna...

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20th COTA International Conference of Transportation Professionals (CICTP) - Advanced Transportation, Enhanced Connection

作者： Tang, Xindi Ji, Congyuan He, Fang Li, Meng Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China Tsinghua Univ Dept Civil Engn Beijing 100084 Peoples R China

ISBN: (数字)9780784482933

ISBN: (纸本)9780784482933

Shared bicycle liberates travelers' walking load, promoting their commuting speed, and owns the characteristic of convenience and accessibility. However, at the meantime they create chaos since tons of companies flowing into this market. In hence, it is vital to propose novel management strategies for the shared bicycle. This study contributes to proposing a methodological framework to solve the online operation problem of shared bicycles, considering efficiency, and security requirements. We build a centralized control system, establish dynamic optimization model and dynamically make repositioning decisions using approximate dynamic programming. Using a neural network as approximate function, it can avoid myopic decisions in that it considers the impact of current actions on future system states. We design a hypothetical network to capture spatial and temporal heterogeneity of demand and supply, and utilize hypothetical network to verify the effectiveness of the proposed model and algorithm.

关键词： Shared bicycle approximate dynamic programming Online operation

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Introducing a User-friendly approximate dynamic programming through Region-based Approximation

Introducing a User-friendly Approximate Dynamic Programming ...

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American Control Conference (ACC)

作者： Sardarmehni, Tohid Song, Xingyong Univ Texas Rio Grande Valley Mech Engn Edinburg TX 78539 USA Texas A&M Univ Dept Engn Technol & Ind Distribut College Stn TX 77843 USA Texas A&M Univ Dept Mech Engn Coll Engn College Stn TX 77843 USA

ISBN: (纸本)9781665441971

One of the main challenges in applying approximate dynamic programming (ADP) to solve real-world optimal control problems is finding a proper Value Function Approximator (VFA). Generally, one selects the proper VFA by trial & error, which might be challenging and time-consuming. This paper tries to alleviate the need for trial & error in selecting a proper VFA in ADP solutions. Also, the paper provides a fast and computationally light training method. The backbone of the proposed solution is partitioning the domain of training to smaller regions in which the value function varies slowly. Afterward, a separate Linear-in-Parameter Neural Network (LIPNN) is trained to capture the value function's behavior in each region. The paper shows that the method improves the precision in value function approximation, which leads to improvement in the performance of the closed-loop system. Meanwhile, the paper discusses the possibility of expanding the training domain in ADP solutions by region-based approximation. At last, the paper provides two real-world simulation examples to evaluate the effectiveness of the solution.

关键词： region-based approximation approximate dynamic programming optimal control

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Research on Open-pit Mine Vehicle Scheduling Problem with approximate dynamic programming

Research on Open-pit Mine Vehicle Scheduling Problem with Ap...

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IEEE International Conference on Industrial Cyber Physical Systems (ICPS)

作者： Xu, Te Shi, Fengyuan Liu, Wenbo Northeastern Univ Key Lab Data Analyt & Optimizat Smart Ind Minist Educ Boston MA 02115 USA Northeastern Univ Liaoning Engn Lab Operat Analyt & Optimizat Smart Shenyang 110819 Peoples R China Northeastern Univ Liaoning Key Lab Mfg Syst & Logist Inst Ind & Syst Engn Shenyang 110819 Peoples R China

ISBN: (纸本)9781538685006

Open-pit mine vehicle scheduling problem is mainly about allocating and designing the schedule of trucks and electric forklift under the constraints, which includes the decision of transportation route, distribution of electric forklifts and trucks number at different mining area with the objective of maximizing equipment utilization rate and reducing production cost. In addition, the various real-time practical changes in mining lead open-pit mine vehicle scheduling problem a dynamic scheduling problem. In this paper, the mathematical model of open-pit mines vehicle scheduling problem using continuous time modeling is established. The transport process, characteristics and requirements of vehicle scheduling problem in open-pit mines are analyzed. For large-scale examples, an approximate dynamic programming model is established by ADP algorithm based on Q-Learning. Numerical experiments of different extraction methods of feature vector and update methods of coefficient vector are carried out, and the results are compared with the results by using solver. The experimental results present that the ADP algorithm designed in this paper can effectively solve the large scale open-pit mine vehicle scheduling problem.

关键词： Open-pit mine Vehicle scheduling approximate dynamic programming

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Improving Quality of Prediction in Highly dynamic Environments Using approximate dynamic programming

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2010年第7期26卷 717-732页

作者： Ganesan, Rajesh Balakrishna, Poornima Sherry, Lance George Mason Univ Ctr Air Transportat Syst Res Dept Syst Engn & Operat Res Fairfax VA 22030 USA

In many applications, decision making under uncertainty often involves two steps-prediction of a certain quality parameter or indicator of the system under study and the subsequent use of the prediction in choosing actions. The prediction process is severely challenged by highly dynamic environments that particularly involve sequential decision making, such as air traffic control at airports in which congestion prediction is critical for smooth departure operations. Taxi-out time of a flight is an excellent indicator of surface congestion and is a quality parameter used in the assessment of airport delays. The regression, queueing, and moving average models have been shown to perform poorly in predicting taxi-out times because they are slow in adapting to the changing airport dynamics. This paper presents an approximate dynamic programming approach (reinforcement learning, RL) to taxi-out time prediction. The taxi-out prediction performance was tested on flight data obtained from the Federal Aviation Administration's (FAA) Aviation System Performance Metrics (ASPM) database on Detroit International (DTW), Washington Reagan National (DCA), Boston (BOS), New York John F. Kennedy (JFK) and Tampa International (TPA) airports. For example, at the Boston airport (presented in detail) the prediction accuracy by RI model was 14% higher than the queueing model and 39% higher than a running-average model. In general, the RL model was 35-50% more accurate than the regression model for all of the above airports. Copyright (c) 2010 John Wiley & Sons, Ltd.

关键词： air transportation approximate dynamic programming quality of prediction reinforcement learning taxi-out time

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Computationally-Efficient Energy Management in Buildings with Phase Change Materials using approximate dynamic programming

Computationally-Efficient Energy Management in Buildings wit...

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14th IEEE Madrid PowerTech Conference (IEEE POWERTECH)

作者： Rahimpour, Zahra Verbic, Gregor Chapman, Archie C. Univ Sydney Sch Elect & Informat Engn Sydney NSW Australia Univ Queensland Sch Informat Technol & Elect Engn Brisbane Qld Australia

ISBN: (纸本)9781665435970

This paper considers energy management in buildings with phase change material (PCM) that serves as a thermal energy storage system. In this setting, the optimal scheduling of an HVAC system is challenging because of the nonlinear characteristics of the PCM, which makes solving the corresponding optimization problem using conventional optimization techniques impractical. Instead, we propose a novel approximate dynamic programming (ADP) methodology to reduce the computational burden, while maintaining the quality of the solution. Specifically, the method incorporates multi-timescale Markov decision processes and a neural network function approximator of the state transition model, coupled with an underlying state-space approximation. The method is demonstrated on an energy management problem for a typical building in Sydney, Australia, over a year. The results demonstrate that the proposed method performs well with a computational speed-up of up to 157,600 times compared to the direct application of DP.

关键词： Demand response approximate dynamic programming home energy management phase change materials thermal inertia

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Improving Quality of Prediction in Highly dynamic Environments Using approximate dynamic programming

Improving Quality of Prediction in Highly Dynamic Environmen...

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INFORMS Annual Meeting on Recent Advancements in Quality and Reliability

作者： Ganesan, Rajesh Balakrishna, Poornima Sherry, Lance George Mason Univ Ctr Air Transportat Syst Res Dept Syst Engn & Operat Res Fairfax VA 22030 USA

关键词： air transportation approximate dynamic programming quality of prediction reinforcement learning taxi-out time

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dynamic Energy Management for Hybrid Electric Vehicle Based on approximate dynamic programming

Dynamic Energy Management for Hybrid Electric Vehicle Based ...

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7th World Congress on Intelligent Control and Automation

作者： Li, Weimin Xu, Guoqing Wang, Zhancheng Xu, Yangsheng Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Chinese Univ HongKong Dept MAE Hong Kong Hong Kong Peoples R China Chinese Acad Sci CUHK Shenzhen Inst Adv Integrat Technol Shenzhen 518067 Peoples R China

ISBN: (纸本)9781424421138

In this paper, an approximate dynamic programming (ADP) based strategy for real-time energy control of parallel hybrid electric vehicles(HEV) is presented. The aim is to develop a fuel-optimal control which is not relying on the priori knowledge of the future driving conditions (global optimal control), but only upon the current system operation. approximate dynamic programming is an on-line learning method, which controls the system while simultaneously learning its characteristics in real time. A suboptimal energy control is then obtained with a proper definition of a cost function to be minimized at each time instant. The cost function includes the fuel consumption, emissions and the deviation of battery soc. Our approach guarantees an optimization of vehicle performance and an adaptation to driving conditions. Simulation results over standard driving cycles are presented to demonstrate the effectiveness of the proposed stochastic approach. It was found that the obtained ADP control algorithm outperforms a traditional rule-based control strategy.

关键词： hybrid electric vehicle approximate dynamic programming energy management strategy

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