检索结果-内蒙古大学图书馆

European Control Conference

作者： Paul Beuchat Angelos Georghiou John Lygeros Automatic Control Laboratory Department of Electrical Engineering and Information Technology ETH Zürich Switzerland

ISBN: (纸本)9781509025923

The simplicity of approximate dynamic programming offers benefits for large-scale systems compared to other synthesis and control methodologies. A common technique to approximate the dynamic Program, is through the solution of the corresponding Linear Program. The major drawback of this approach is that the online performance is very sensitive to the choice of tuning parameters, in particular the state relevance weighting parameter. Our work aims at alleviating this sensitivity. To achieve this, we propose to find a set of approximate Q-functions, each for a different choice of the tuning parameters, and then to use the pointwise maximum of the set of Q-functions for the online policy. The pointwise maximum promises to be better than using only one of individual Q-functions for the online policy. We demonstrate that this approach immunizes against tuning errors through a stylized portfolio optimization problem.

关键词： Alleviating tuning sensitivity approximate dynamic programming pointwise maximum

来源：评论

学校读者我要写书评

暂无评论

Optimal Output Synchronization of Nonlinear Multi-agent Systems using approximate dynamic programming

Optimal Output Synchronization of Nonlinear Multi-agent Syst...

引用

International Joint Conference on Neural Networks

作者： Hamidreza Modares Frank L. Lewis Ali Davoudi University of Texas at Arlington Research Institute

ISBN: (纸本)9781509006212

Optimal output synchronization of multi-agent leader-follower systems is considered. The agents are assumed heterogeneous so that the dynamics may be non-identical. An optimal control protocol is designed for each agent based on the leader state and the agent local state. A distributed observer is designed to provide the leader state for each agent. A model-free approximate dynamic programming algorithm is then developed to solve the optimal output synchronization problem online in real time. No knowledge of the agents' dynamics is required. The proposed approach does not require explicitly solving of the output regulator equations, though it implicitly solves them by imposing optimality. A simulation example verifies the suitability of the proposed approach.

关键词： Output Synchronization Multi-agent Systems approximate dynamic programming Optimal Control Multi-agent systems dynamic programming Optimal control Agents approximate

来源：评论

学校读者我要写书评

暂无评论

Solving stochastic resource-constrained project scheduling problems by closed-loop approximate dynamic programming

引用

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2015年第1期246卷 20-33页

作者： Li, Haitao Womer, Norman K. Univ Missouri Coll Business Adm St Louis MO 63121 USA

Project scheduling problems with both resource constraints and uncertain task durations have applications in a variety of industries. While the existing research literature has been focusing on finding an a priori open-loop task sequence that minimizes the expected makespan, finding a dynamic and adaptive closed-loop policy has been regarded as being computationally intractable. In this research, we develop effective and efficient approximate dynamic programming (ADP) algorithms based on the rollout policy for this category of stochastic scheduling problems. To enhance performance of the rollout algorithm, we employ constraint programming (CP) to improve the performance of base policy offered by a priority-rule heuristic. We further devise a hybrid ADP framework that integrates both the look-back and look-ahead approximation architectures, to simultaneously achieve both the quality of a rollout (look-ahead) policy to sequentially improve a task sequence, and the efficiency of a lookup table (look-back) approach. Computational results on the benchmark instances show that our hybrid ADP algorithm is able to obtain competitive solutions with the state-of-the-art algorithms in reasonable computational time. It performs particularly well for instances with non-symmetric probability distribution of task durations. (C) 2015 Elsevier B.V. and Association of European Operational Research Societies (EURO) within the International Federation of Operational Research Societies (IFORS). All rights reserved.

关键词： Resource-constrained project scheduling Uncertain task durations Stochastic scheduling approximate dynamic programming Simulation

来源：评论

学校读者我要写书评

暂无评论

Optimal Hour-Ahead Bidding in the Real-Time Electricity Market with Battery Storage Using approximate dynamic programming

引用

INFORMS JOURNAL ON COMPUTING 2015年第3期27卷 525-543页

作者： Jiang, Daniel R. Powell, Warren B. Princeton Univ Dept Operat Res & Financial Engn Princeton NJ 08540 USA

There is growing interest in the use of grid-level storage to smooth variations in supply that are likely to arise with an increased use of wind and solar energy. Energy arbitrage, the process of buying, storing, and selling electricity to exploit variations in electricity spot prices, is becoming an important way of paying for expensive investments into grid-level storage. Independent system operators such as the New York Independent System Operator (NYISO) require that battery storage operators place bids into an hour-ahead market (although settlements may occur in increments as small as five minutes, which is considered near "real-time"). The operator has to place these bids without knowing the energy level in the battery at the beginning of the hour and simultaneously accounting for the value of leftover energy at the end of the hour. The problem is formulated as a dynamic program. We describe and employ a convergent approximate dynamic programming (ADP) algorithm that exploits monotonicity of the value function to find a revenue-generating bidding policy;using optimal benchmarks, we empirically show the computational benefits of the algorithm. Furthermore, we propose a distribution-free variant of the ADP algorithm that does not require any knowledge of the distribution of the price process (and makes no assumptions regarding a specific real-time price model). We demonstrate that a policy trained on historical real-time price data from the NYISO using this distribution-free approach is indeed effective.

关键词： approximate dynamic programming bidding hour-ahead energy storage real-time market

来源：评论

学校读者我要写书评

暂无评论

A Novel Quantum-Inspired approximate dynamic programming Algorithm for Unit Commitment Problems

A Novel Quantum-Inspired Approximate Dynamic Programming Alg...

引用

2016 3rd International Conference on Materials Engineering,Manufacturing Technology and Control(ICMEMTC 2016)

作者： Hua Qin Guangxi Key Laboratory of Power System Optimization and Energy Technology Guangxi University

A novel quantum-inspired approximate dynamic programming algorithm(ADP) is proposed for solving unit commitment(UC) problems. The quantum computing theory is applied to tackle some new issues rising from ADP. In details, the unit states in UC problems 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 UC 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 quantum-inspired ADP algorithm can find the better sub-optimal solutions of large scale UC problems within a reasonable time.

关键词： approximate dynamic programming Quantum Computing Unit Commitment

来源：评论

学校读者我要写书评

暂无评论

FINITE-HORIZON ε-OPTIMAL TRACKING CONTROL OF DISCRETE-TIME LINEAR SYSTEMS USING ITERATIVE approximate dynamic programming

引用

ASIAN JOURNAL OF CONTROL 2015年第1期17卷 176-189页

作者： Tan, Fuxiao Luo, Bin Guan, Xinping Anhui Univ Sch Comp Sci & Technol Hefei Anhui Peoples R China Anhui Univ Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei Anhui Peoples R China Fuyang Teachers Coll Sch Comp & Informat Anhui Fuyang Peoples R China Shanghai Jiao Tong Univ Sch Elect & Elect Engn Shanghai 200030 Peoples R China

In this paper, we propose a finite-horizon neuro-optimal tracking control strategy for a class of discrete-time linear systems. In applying the iterative approximate dynamic programming (ADP) algorithm to determine the optimal tracking control law for linear systems, we need finite iterations to obtain the result in practical applications, instead of infinite iterations. An epsilon-error bound is introduced into the ADP algorithm to determine the number of iteration steps. The approximation optimal tracking control law will approach the solution of the Hamilton-Jacobi-Bellman (HJB) equation through a self-adaptive iteration within the given value of epsilon-error bound. epsilon error bound is used to stop the iteration process. So, we can obtain the epsilon-approximation tracking control law in a finite number of iterations. Nevertheless, different epsilon will produce different control performances. Furthermore, we will find an optimal epsilon error bound, which can obtain optimal performance of the ADP algorithm on the basis of the controlled system tracking the desired trajectory. One example is included to complete the ADP algorithm under different error bounds. From the simulation results, we can find the optimal epsilon error bound. Finally, the simulation validates the efficiency of the proposed algorithm.

关键词： Error bound approximate dynamic programming neural networks optimal tracking control finite iterations

来源：评论

学校读者我要写书评

暂无评论

An approximate dynamic programming Algorithm for Monotone Value Functions

引用

OPERATIONS RESEARCH 2015年第6期63卷 1489-1511页

作者： Jiang, Daniel R. Powell, Warren B. Princeton Univ Dept Operat Res & Financial Engn Princeton NJ 08540 USA

Many sequential decision problems can be formulated as Markov decision processes (MDPs) where the optimal value function (or cost-to-go function) can be shown to satisfy a monotone structure in some or all of its dimensions. When the state space becomes large, traditional techniques, such as the backward dynamic programming algorithm (i. e., backward induction or value iteration), may no longer be effective in finding a solution within a reasonable time frame, and thus we are forced to consider other approaches, such as approximate dynamic programming (ADP). We propose a provably convergent ADP algorithm called Monotone-ADP that exploits the monotonicity of the value functions to increase the rate of convergence. In this paper, we describe a general finite-horizon problem setting where the optimal value function is monotone, present a convergence proof for Monotone-ADP under various technical assumptions, and show numerical results for three application domains: optimal stopping, energy storage/allocation, and glycemic control for diabetes patients. The empirical results indicate that by taking advantage of monotonicity, we can attain high quality solutions within a relatively small number of iterations, using up to two orders of magnitude less computation than is needed to compute the optimal solution exactly.

关键词： approximate dynamic programming monotonicity optimal stopping energy storage glycemic control

来源：评论

学校读者我要写书评

暂无评论

dynamic Power Management for Portable Hybrid Power-Supply Systems Utilizing approximate dynamic programming

引用

ENERGIES 2015年第6期8卷 5053-5073页

作者： Park, Jooyoung Chung, Gyo-Bum Lim, Jungdong Yang, Dongsu Korea Univ Dept Control & Instrumentat Engn Sejong 339700 South Korea Hongik Univ Dept Elect & Elect Engn Sejong 339701 South Korea LG Elect Seoul 135860 South Korea

Recently, the optimization of power flows in portable hybrid power-supply systems (HPSSs) has become an important issue with the advent of a variety of mobile systems and hybrid energy technologies. In this paper, a control strategy is considered for dynamically managing power flows in portable HPSSs employing batteries and supercapacitors. Our dynamic power management strategy utilizes the concept of approximate dynamic programming (ADP). ADP methods are important tools in the fields of stochastic control and machine learning, and the utilization of these tools for practical engineering problems is now an active and promising research field. We propose an ADP-based procedure based on optimization under constraints including the iterated Bellman inequalities, which can be solved by convex optimization carried out offline, to find the optimal power management rules for portable HPSSs. The effectiveness of the proposed procedure is tested through dynamic simulations for smartphone workload scenarios, and simulation results show that the proposed strategy can successfully cope with uncertain workload demands.

关键词： hybrid power-supply systems dynamic power management approximate dynamic programming

来源：评论

学校读者我要写书评

暂无评论

An approximate dynamic programming heuristic to support non-strategic project selection for the Royal Canadian Navy

引用

JOURNAL OF DEFENSE MODELING AND SIMULATION-APPLICATIONS METHODOLOGY TECHNOLOGY-JDMS 2015年第2期12卷 83-90页

作者： Fisher, Brent Brimberg, Jack Hurley, W. J. Royal Mil Coll Canada Kingston ON K7K 7B4 Canada Royal Mil Coll Canada Dept Math & Comp Sci Operat Res Kingston ON K7K 7B4 Canada

We consider an approximate dynamic programming heuristic to support the selection of defense projects when projects have different values and are originated intermittently but fairly frequently. We show that a simple policy reserving a positive fraction of the available budget for high-value projects not yet originated is superior to a greedy knapsack approach.

关键词： Defense project selection knapsack problem Online Knapsack Problem approximate dynamic programming

来源：评论

学校读者我要写书评

暂无评论

An approximate dynamic programming Approach to Urban Freight Distribution with Batch Arrivals 6th

An Approximate Dynamic Programming Approach to Urban Freight...

引用

6th International Conference on Computational Logistics (ICCL)

作者： van Heeswijk, Wouter Mes, Martijn Schutten, Marco Univ Twente Dept Ind Engn & Business Informat Syst NL-7500 AE Enschede Netherlands

ISBN: (纸本)9783319242644;9783319242637

We study an extension of the delivery dispatching problem (DDP) with time windows, applied on LTL orders arriving at an urban consolidation center. Order properties (e.g., destination, size, dispatch window) may be highly varying, and directly distributing an incoming order batch may yield high costs. Instead, the hub operator may wait to consolidate with future arrivals. A consolidation policy is required to decide which orders to ship and which orders to hold. We model the dispatching problem as a Markov decision problem. dynamic programming (DP) is applied to solve toy-sized instances to optimality. For larger instances, we propose an approximate dynamic programming (ADP) approach. Through numerical experiments, we show that ADP closely approximates the optimal values for small instances, and outperforms two myopic benchmark policies for larger instances. We contribute to literature by (i) formulating a DDP with dispatch windows and (ii) proposing an approach to solve this DDP.

关键词： Urban distribution Transportation planning Consolidation approximate dynamic programming

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：