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第37届中国控制会议

作者： Wenhao Zhuo School of Electrical Engineering and Telecommunications The University of New South Wales

The growth in distributed renewable power systems provides opportunities to construct more microgrids. With the help of battery energy storage systems(BESS) in the microgrids, the variable and intermittent renewable energy can be smoothed and utilized locally without risking the main electrical grid. Furthermore, the energy costs in microgrids can be reduced significantly with proper control of BESS based on predicted data of renewable power and electricity price. In this paper, we propose an energy management strategy for microgrids with renewable power systems integrated with BESS. A deregulated energy market environment is considered, which allows energy trading from private participates to the main electrical grids. The objective is to maximize the profit gained by selling the excessive renewable energy generated and minimize the cost to meet the load demand in the microgrid, and an approximate dynamic programming algorithm is proposed to solve the issue. The operational cost from battery lifetime consumption is considered, and the rolling horizon approach is utilized for long-term simulation. Both short-term and long-term forecasting are used in the algorithm and updated in every control stage. The algorithm is tested with practical wind power, load demand, and electricity price data.

关键词： microgrid battery energy storage system battery lifetime estimation approximate dynamic programming

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Online Optimal Operation of Microgrid using approximate dynamic programming under Uncertain Environment

Online Optimal Operation of Microgrid using Approximate Dyna...

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第37届中国控制会议

作者： Ying Ji Jianhui Wang Xiaoke Fang Huaguang Zhang College of Information Science and Engineering Northestern University

This paper focuses on economical operation of a grid-connected microgrid in real-time. An online optimization model is developed to achieve the microgrid operation in the process of dynamic optimal control, and through information feedback and online optimization to reduce the impact of uncertainty in a MG. The online optimization model has the characteristics of nonlinear and discontinuous, so an approximate dynamic programming（ADP） algorithm is used to solve this online model. Finally, the proposed online optimization model is tested in the European benchmark microgrid system. The simulation results show that the online optimization model overcomes the impacts of random fluctuations of renewable energy and loads and reduces the operating cost of the microgrid.

关键词： microgrid uncertainty online optimization approximate dynamic programming

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A Quantum-Inspired approximate dynamic programming Algorithm for Unit Commitment Problems Considering Wind Power

A Quantum-Inspired Approximate Dynamic Programming Algorithm...

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IEEE International Conference on Smart Grid and Smart Cities (ICSGSC)

作者： Qin, Hua Wei, Hua Guangxi Univ Guangxi Key Lab Power Syst Optimizat & Energy Tec Nanning Peoples R China

ISBN: (纸本)9781538605042

A quantum-inspired approximate dynamic programming algorithm (QI-ADP) is proposed for solving unit commitment problems considering wind power (WUC). The quantum computing theory is applied to tackle some new issues rising from ADP. In details, the unit states in a WUC problem are expressed by the quantum superposition. Then, the collapsing principle of quantum measurement is applied to solve the Bellman equation of ADP speedily. Based on the quantum rotation gate, the pre-decision states of the ADP are generated by quantum amplitude amplification technology. In the proposal algorithm, the quantum computation balances between state space exploration and exploitation automatically. Test cases of WUC are performed to verify the feasibility of the proposal approximate algorithm for the range of 10 to 100 units with 24-hour with ramp rate constraints. The experimental results show that the QI-ADP algorithm can find the sub-optimal solutions of large scale WUC problems within a reasonable time, and the average operational cost is reduced by 9.38% when the wind power is consumed. As a result, it is feasible to use the quantum computing theory to tackle some new issues rising from ADP, and the QI-ADP can be applied to solve large-scale WUC problems.

关键词： unit commitment wind power approximate dynamic programming quantum computing

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Value Iteration Based approximate dynamic programming For Mobile Robot Trajectory Tracking with Persistent Inputs 5

Value Iteration Based Approximate Dynamic Programming For Mo...

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5th IEEE International Symposium on Robotics and Intelligent Sensors (IEEE IRIS)

作者： Miah, Md Suruz Bradley Univ Elect & Comp Engn Dept Peoria IL 61625 USA

ISBN: (纸本)9781538613429

This paper presents a value iteration based approximate dynamic programming technique to solve the trajectory tracking problem of unicycle like wheeled mobile robots. Given a reference trajectory (2D), an error model is derived to form a nonlinear affine system. The robot is supposed to track the reference trajectory asymptotically. The solution of the error model is used to define the value function (cost-to-go function), which is a measure of the robot's tracking error and the cost of applying its actuator inputs (in this case, linear and angular velocities of the robot). A critic neural network approximates the value function to determine the optimal control inputs that are applied to the robot's actuators. A set of computer simulations is conducted to evaluate the performance of the proposed approximate dynamic programming method. The robot's trajectory tracking performance using the proposed method is also compared with that of the conventional approximate linearization technique in order to show the superiority of the value iteration based approximate dynamic programming.

关键词： approximate dynamic programming mobile robot optimal control trajectory tracking value iteration component formatting style styling insert

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AN approximate dynamic programming APPROACH FOR COMPARING FIRING SOLUTIONS IN A NETWORKED AIR DEFENSE ENVIRONMENT

AN APPROXIMATE DYNAMIC PROGRAMMING APPROACH FOR COMPARING FI...

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作者： Daniel S. Summers Air Force Institute of Technology

学位级别：硕士

The United States Army currently employs a shoot-shoot-look firing policy for air defense. As the Army moves to a networked defense-in-depth strategy, this policy will not provide optimal results for managing interceptor inventories in a conflict to minimize the damage to defended assets. The objective for air and missile defense is to identify the firing policy for interceptor allocation that minimizes expected total cost of damage to defended assets. This dynamic weapon target assignment prob- lem is formulated first as a Markov decision process (MDP) and then approximate dynamic programming (ADP) is used to solve problem instances based on a represen- tative scenario. Least squares policy evaluation (LSPE) and least squares temporal difference (LSTD) algorithms are employed to determine the best approximate poli- cies possible. An experimental design is conducted to investigate problem features such as conflict duration, attacker and defender weapon sophistication, and defended asset values. The LSPE and LSTD algorithm results are compared to two benchmark policies (e. g., firing one or two interceptors at each incoming tactical ballistic missile (TBM)). Results indicate that ADP policies outperform baseline polices when con- flict duration is short and attacker weapons are sophisticated. Results also indicate that firing one interceptor at each TBM (regardless of inventory status) outperforms the tested ADP policies when conflict duration is long and attacker weapons are less sophisticated.

关键词： air and missile defense dynamic weapon target assignment problem Markov decision processes approximate dynamic programming approximate policy iteration least squares policy evaluation least squares temporal difference

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approximate dynamic programming in Tracking Control of a Robotic Manipulator

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2016年第1期13卷 16-16页

作者： Szuster, Marcin Gierlak, Piotr Rzeszow Univ Technol Rzeszow Poland

This article focuses on the implementation of an approximate dynamic programming algorithm in the discrete tracking control system of the three-degrees of freedom Scorbot-ER 4pc robotic manipulator. The controlled system is included in an articulated robots group which uses rotary joints to access their work space. The main part of the control system is a dual heuristic dynamic programming algorithm that consists of two structures designed in the form of neural networks: an actor and a critic. The actor generates the suboptimal control law while the critic approximates the difference of the value function from Bellman's equation with respect to the state. The residual elements of the control system are the PD controller, the supervisory term and an additional control signal. The structure of the supervisory term derives from the stability analysis performed using the Lyapunov stability theorem. The control system works online, the neural networks' weights-adaptation procedure is performed in every iteration step, and the neural networks' preliminary learning process is not required. The performance of the control system was verified by a series of computer simulations and experiments performed using the Scorbot-ER 4pc robotic manipulator.

关键词： approximate dynamic programming Dual Heuristic dynamic programming Neural Network Robotic Manipulator Tracking Control

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From Single Commodity to Multiattribute Models for Locomotive Optimization: A Comparison of Optimal Integer programming and approximate dynamic programming

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TRANSPORTATION SCIENCE 2016年第2期50卷 366-389页

作者： Bouzaiene-Ayari, Belgacem Cheng, Clark Das, Sourav Fiorillo, Ricardo Powell, Warren B. Princeton Univ Dept Operat Res & Financial Engn Princeton NJ 08544 USA Norfolk Southern Corp Atlanta GA 30309 USA

We present a general optimization framework for locomotive models that captures different levels of detail, ranging from single and multicommodity flow models that can be solved using commercial integer programming solvers, to a much more detailed multiattribute model that we solve using approximate dynamic programming (ADP). Both models have been successfully implemented at Norfolk Southern for different planning applications. We use these models, presented using a common notational framework, to demonstrate the scope of different modeling and algorithmic strategies, all of which add value to the locomotive planning problem. We demonstrate how ADP can be used for both deterministic and stochastic models that capture locomotives and trains at a very high level of detail.

关键词： approximate dynamic programming locomotive planning

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Energy-efficient metro train rescheduling with uncertain time-variant passenger demands: An approximate dynamic programming approach

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TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2016年第Sep.期91卷 178-210页

作者： Yin, Jiateng Tang, Tao Yang, Lixing Gao, Ziyou Ran, Bin Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Univ Wisconsin Dept Civil & Environm Engn Madison WI 53706 USA

In a heavily congested metro line, unexpected disturbances often occur to cause the delay of the traveling passengers, infeasibility of the current timetable and reduction of the operational efficiency. Due to the uncertain and dynamic characteristics of passenger demands, the commonly used method to recover from disturbances in practice is to change the timetable and rolling stock manually based on the experiences and professional judgements. In this paper, we develop a stochastic programming model for metro train rescheduling problem in order to jointly reduce the time delay of affected passengers, their total traveling time and operational costs of trains. To capture the complexity of passenger traveling characteristics, the arriving ratio of passengers at each station is modeled as a non-homogeneous Poisson distribution, in which the intensity function is treated as time-varying origin-to-destination passenger demand matrices. By considering the number of on-board passengers, the total energy usage is modeled as the difference between the tractive energy consumption and the regenerative energy. Then, we design an approximate dynamic programming based algorithm to solve the proposed model, which can obtain a high-quality solution in a short time. Finally, numerical examples with real-world data sets are implemented to verify the effectiveness and robustness of the proposed approaches. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Metro train rescheduling Passenger delay Energy efficiency approximate dynamic programming Stochastic programming

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Traffic network micro-simulation model and control algorithm based on approximate dynamic programming

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IET INTELLIGENT TRANSPORT SYSTEMS 2016年第3期10卷 186-196页

作者： Yin, Biao Dridi, Mahjoub El Moudni, Abdellah Univ Technol Belfort Montbeliard Lab Syst & Transports F-90000 Belfort France

This study presents the adaptive traffic signal control algorithm in a distributed traffic network system. The proposed algorithm is based on a micro-simulation model and a reinforcement learning method, namely approximate dynamic programming (ADP). By considering traffic environment in discrete time, the microscopic traffic dynamic model is built. In particular, the authors explore a vehicle-following model using cellular automata theory. This vehicle-following model theoretically contributes to traffic network loading environment in an accessible way. To make the network coordinated, tunable state with weights of queue length and vehicles on lane is considered. The intersection can share information with each other in this state representation and make a joint action for intersection coordination. Moreover, the traffic signal control algorithm based on ADP method performs quite well in different performance measures witnessed by simulations. By comparing with other control methods, experimental results present that the proposed algorithm could be a potential candidate in an application of traffic network control system.

关键词： intelligent transportation systems dynamic programming adaptive control learning (artificial intelligence) cellular automata traffic network microsimulation model approximate dynamic programming adaptive traffic signal control algorithm distributed traffic network system reinforcement learning method microscopic traffic dynamic model vehicle-following model cellular automata theory traffic network loading environment ADP method

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An approximate dynamic programming approach for improving accuracy of lossy data compression by Bloom filters

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2016年第3期252卷 985-994页

作者： Yang, Xinan Vernitski, Alexei Carrea, Laura Univ Essex Dept Math Sci Wivenhoe Pk Colchester CO4 3SQ Essex England Univ Reading Dept Meteorol Reading RG6 6BB Berks England Univ Essex Sch Comp Sci & Elect Engn Wivenhoe Pk Colchester CO4 3SQ Essex England

Bloom filters are a data structure for storing data in a compressed form. They offer excellent space and time efficiency at the cost of some loss of accuracy (so-called lossy compression). This work presents a yes-no Bloom filter, which as a data structure consisting of two parts: the yes-filter which is a standard Bloom filter and the no-filter which is another Bloom filter whose purpose is to represent those objects that were recognized incorrectly by the yes-filter (that is, to recognize the false positives of the yes-filter). By querying the no-filter after an object has been recognized by the yes-filter, we get a chance of rejecting it, which improves the accuracy of data recognition in comparison with the standard Bloom filter of the same total length. A further increase in accuracy is possible if one chooses objects to include in the no-filter so that the no-filter recognizes as many as possible false positives but no true positives, thus producing the most accurate yes-no Bloom filter among all yes-no Bloom filters. This paper studies how optimization techniques can be used to maximize the number of false positives recognized by the no-filter, with the constraint being that it should recognize no true positives. To achieve this aim, an Integer Linear Program (ILP) is proposed for the optimal selection of false positives. In practice the problem size is normally large leading to intractable optimal solution. Considering the similarity of the ILP with the Multidimensional Knapsack Problem, an approximate dynamic programming (ADP) model is developed making use of a reduced ILP for the value function approximation. Numerical results show the ADP model works best comparing with a number of heuristics as well as the CPLEX built-in solver (B&B), and this is what can be recommended for use in yes-no Bloom filters. In a wider context of the study of lossy compression algorithms, our research is an example showing how the arsenal of optimization methods can be applied

关键词： Lossy compression Bloom filter Integer linear program approximate dynamic programming Heuristics

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