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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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Robust Adaptive Control with Active Learning for Fed-Batch Process based on approximate dynamic programming

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IFAC-PapersOnLine 2020年第2期53卷 5201-5206页

作者： Ha-Eun Byun Boeun Kim Jay H. Lee Department of Chemical and Biomolecular Engineering Korea Advanced Institute of Science and Technology 291 Daehak-ro Yuseong-gu Daejeon 34141 Republic of Korea Department of Chemical and Biological Engineering University of Wisconsin-Madison Madison WI 53706 USA

Batch process is often subject to a high degree of uncertainty in raw material quality and other initial feedstock conditions. One of the key objectives in operating a batch process is achieving consistent performance and constraint satisfaction in the presence of these uncertainties. This study presents a method for optimal control of a fed-batch process, which can actively and robustly cope with system uncertainty. As in dual control, the method aims to achieve an optimal balance between control actions (exploitation) and probing actions (exploration), leading to improved process performance by actively reducing system uncertainty. An optimal solution of the dual control problem can be found by stochastic dynamic programming but it is computationally intractable in most practical cases. In this study, an approximate dynamic programming (ADP) method for solving the dual control problem is tailored to a batch process which involves non-stationary and nonlinear dynamics. Rewards are formulated to maximize a given end objective while satisfying path constraints. Performance of the ADP-based dual controller is tested on a fed-batch bioreactor with two uncertain parameters.

关键词： Robust adaptive control Dual control Stochastic optimal control approximate dynamic programming Fed-batch process Model uncertainty

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Scheduling Elective Surgeries with Markov Decision Process and approximate dynamic programming 9

Scheduling Elective Surgeries with Markov Decision Process a...

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9th IFAC/IFIP/IFORS/IISE/INFORMS Conference on Manufacturing Modelling, Management and Control (IFAC MIM)

作者： Zhang, Jian Dridi, Mahjoub El Moudni, Abdellah Univ Bourgogne Franche Comte Lab Nanomed Imagerie & Therapeut UTBM Rue Thierry Mieg F-90010 Belfort France

This paper deals with the dynamic advance scheduling of elective surgeries with multiple sources of uncertainties taken into consideration. A waiting list is established to facilitate the management of elective patients from different specialties. Each patient in the waiting list is assigned a dynamic priority which is dependent on the relative importance of specialty, urgency level, and actual waiting time. At the end of each week, the number and type of elective surgeries to be performed in the following week should be properly determined to minimize an integrated cost function, including the costs incurred by performing and delaying surgeries as well as the penalties for overuse of operating rooms and shortage of recovery beds. The studied problem is formulated as an infinite-horizon Markov decision process (MDP) model. Considering that conventional dynamic programming algorithms cannot efficiently solve MDP models for real-sized problems, we develop an approximate dynamic programming (ADP) approach that combines recursive least-squares temporal difference learning and mixed integer programming. Results of numerical experiments validate the efficiency and accuracy of the proposed ADP approach and indicate that this approach can be employed by hospital managers in the future to efficiently solve real-sized surgery scheduling problems. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： surgery scheduling Markov decision process approximate dynamic programming recursive least-squares temporal difference learning mixed integer programming

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Control of a networked microgrid system with an approximate dynamic programming approach 38

Control of a networked microgrid system with an approximate ...

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38th Chinese Control Conference (CCC)

作者： Zhuo, Wenhao Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW Australia

ISBN: (纸本)9789881563972

Constructing microgrids with renewable energy systems and storage units can be a feasible solution to smooth the fluctuation of renewable power and increase its penetration. In recent years, research on microgrids has proven that the overall electricity and operational costs in microgrids can be further reduced with a networked microgrid system. One major issue in controlling microgrids with renewable power systems is the uncertainties due to forecasting errors, and optimization problems in a microgrid network can be could be challenging to implement due to a large number of uncertainties. In this paper, we propose an approximate dynamic programming-based power management strategy in a microgrid network system, and the objective is to minimize the overall cost over a control horizon with predicted renewable power, electricity price and load demand. The network consists of several microgrids and a centralized energy management system. The use of the approximate dynamic programming algorithm is to address the 'curse of dimensionality' due to high dimensional state and control spaces in the network.

关键词： Control of power systems control of microgrids optimal control battery energy storage system approximate dynamic programming

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Research on Autonomous Maneuvering Decision of UCAV Based on approximate dynamic programming 2

Research on Autonomous Maneuvering Decision of UCAV Based on...

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2nd International Conference on Image and Video Processing, and Artificial Intelligence (IPVAI)

作者： Hu, Zhencai Gao, Peng Wang, Fei Harbin Inst Technol Shenzhen Peoples R China

ISBN: (数字)9781510634107

ISBN: (纸本)9781510634107

Unmanned aircraft systems can perform some more dangerous and difficult missions which manned aircraft systems cannot perform. For tasks with high complexity, such as air combat, maneuvering decision mechanism is required to sense the combat environment and make the optimal strategy in real time. This paper formulates one-to-one air combat maneuvering problem in 3D environment, and proposes an approximate dynamic programming approach to make optimal maneuvering decisions automatically. The aircraft searches for combat strategies based-on Reinforcement Leaning, while sensing the environment, taking available maneuvering actions and receiving feedback reward signals. To solve the problem of dimensional explosion in the air combat, the proposed method is implemented through feature selection, trajectory sampling, function approximation and Bellman backup operation in the air combat simulation environment. This approximate dynamic programming approach provides a fast response to rapid changing tactical situations, and learns effective strategies to fight against the opponent aircraft.

关键词： Air Combat Maneuvering Decision approximate dynamic programming Reinforcement Learning

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approximate Optimal tracking Control for Nonlinear Discrete-time Switched Systems via approximate dynamic programming 31

Approximate Optimal tracking Control for Nonlinear Discrete-...

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第31届中国控制与决策会议

作者： Chunbin Qin Yizhe Huang Yabin Yang Jishi Zhang Xianxing Liu The School of Computer and Information Engineering Henan University The School of Software Henan University

ISBN: (纸本)9781728101071;9781728101064

In this paper,an iterative approximate dynamic programming algorithm is proposed for solving the approximate optimal tracking control problem for a class of nonlinear discrete-time switched ***,the optimal tracking problem is converted into designing an optimal regulator for the tracking error *** then,the iterative approximate dynamic programming algorithm is proposed to obtain the approximate solution of the Hamilton-JacobiBellman(HJB)***,two neural networks are used to approximate the iterative cost function and the iterative control law,***,simulation results are given to verify the effectiveness of the proposed algorithm.

关键词： Tracking control Nonlinear switched system approximate dynamic programming

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Optimal Stochastic Scheduling of Restoration of Infrastructure Systems from Hazards: An approximate dynamic programming Approach

Optimal Stochastic Scheduling of Restoration of Infrastructu...

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作者： Nozhati, Saeed Colorado State University

学位级别：Ph.D.

This dissertation introduces approximate dynamic programming (ADP) techniques to identify near-optimal recovery strategies following extreme natural hazards. The proposed techniques are intended to support policymakers, community stakeholders, and public or private entities to manage the restoration of critical infrastructure of a community following disasters. The computation of optimal scheduling schemes in this study employs the rollout algorithm, which provides an effective computational tool for optimization problems dealing with real-world large-scale networks and communities. The Markov decision process (MDP)-based optimization approach incorporates different sources of uncertainties to compute the restoration policies. The fusion of the proposed rollout method with metaheuristic algorithms and optimal learning techniques to overcome the computational intractability of large-scale, multi-state communities is probed in detail. Different risk attitudes of policymakers, which include risk-neutral and riskaverse attitudes in community recovery management, are taken into account. The context for the proposed framework is provided by objectives related to minimizing foodinsecurity issues and impacts within a small community in California following an extreme earthquake. Probabilistic food security metrics, including food availability, accessibility, and affordability, are defined and quantified to provide risk-informed decision support to policymakers in the aftermath of an extreme natural hazard. The proposed ADP-based approach then is applied to identify practical policy interventions to hasten the recovery of food systems and reduce the adverse impacts of food insecurity on a community. All proposed methods in this study are applied on a testbed community modeled after Gilroy, California, United States, which is impacted by earthquakes on the San Andreas Fault. Different infrastructure systems, along with their spatial distributions, are modeled as part of the e

关键词： community resilience Markov decision process rollout food security approximate dynamic programming optimal recovery management

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approximate dynamic programming for patient admission scheduling 12

Approximate dynamic programming for patient admission schedu...

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12th International Conference on the Practice and Theory of Automated Timetabling, PATAT 2018

作者： De Oliveira Silva, Thiago Augusto Vancroonenburg, Wim Berghe, Greet Vanden Departamento de Engenharia de Produgao Institute- de Ciencias Exatas e Aplicadas Universidade Federal de Ouro Preto Brazil Department of Computer Science CODeS and Imec KU Leuven Ghent Technology Campus Gebroeders De Smetstraat 1 Gent9000 Belgium

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An approximate dynamic programming Based Home Energy Management System And Its Impact On The Distribution System

An Approximate Dynamic Programming Based Home Energy Managem...

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作者： Li Wang Kansas State University

学位级别：硕士

The residential sector consumes more than 20 percent of electricity in the United States. In order to shift peak load, a utility company provides time-of-use rates or even real-time pricing to its customers. Typically, peak electricity rates can be up to twice as much as the off-peak rates. Therefore, electricity costs can be different even when the customer is using the same amount of energy. A home energy management system (HEMS) can provide autonomous control of residential appliances to minimize the electricity cost while maintaining the customer's thermal comfort in the presence of uncertain weather and electricity consumption. In this thesis, a HEMS is developed to optimize forward-looking schedules for a residential home's heating, ventilation, air conditioning (HVAC), water heater (WH), electric vehicle (EV), and battery storage (ES). An approximate dynamic programming (ADP) approach is used to define, model, and solve the HEMS problem. Moreover, a model predictive control (MPC) based simulation framework is developed to study the impact of the HEMS on the distribution system and compare the ADP with another prevailing algorithm - mixed integer linear programming (MILP). Extensive simulation has been carried out and simulation results show that the proposed ADP-based HEMS can effectively schedule appliances to minimize the electricity cost and maintain customer thermal comfort. Furthermore, a coordinated two-stage real-time market mechanism in an unbalanced distribution system is proposed to fully utilize flexibility services provided by the HEMS for alleviating line congestion, voltage violation, and substation level power imbalance.

关键词： approximate dynamic programming Home energy management system Demand response Smart home Mixed integer linear programming Electric vehicle

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Using approximate dynamic programming for multi-ESM scheduling to track ground moving targets

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Journal of Systems Engineering and Electronics 2018年第1期29卷 74-85页

作者： WAN Kaifang GAO Xiaoguang LI Bo LI Fei Key Laboratory of Aerospace Information Perception and Photoelectric Control Ministry of EducationSchool of Electronics and Information Northwestern Polytechnical University

This paper researches the adaptive scheduling problem of multiple electronic support measures(multi-ESM) in a ground moving radar targets tracking application. It is a sequential decision-making problem in uncertain environment. For adaptive selection of appropriate ESMs, we generalize an approximate dynamic programming(ADP) framework to the dynamic case. We define the environment model and agent model, respectively. To handle the partially observable challenge, we apply the unsented Kalman filter(UKF) algorithm for belief state estimation. To reduce the computational burden, a simulation-based approach rollout with a redesigned base policy is proposed to approximate the long-term cumulative reward. Meanwhile, Monte Carlo sampling is combined into the rollout to estimate the expectation of the rewards. The experiments indicate that our method outperforms other strategies due to its better performance in larger-scale problems.

关键词： sensor scheduling target tracking approximate dynamic programming non-myopic rollout belief state

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