检索结果-南通市图书馆

Petroleum Science 2022年第5期19卷 2497-2517页

作者： Yi-Ze Meng Ruo-Ran Chen Tian-Hu Deng Department of Industrial Engineering Tsinghua UniversityBeijing100084China

In short-term operation of natural gas network,the impact of demand uncertainty is not negligible.To address this issue we propose a two-stage robust model for power cost minimization problem in gunbarrel natural gas networks.The demands between pipelines and compressor stations are uncertain with a budget parameter,since it is unlikely that all the uncertain demands reach the maximal deviation simultaneously.During solving the two-stage robust model we encounter a bilevel problem which is challenging to solve.We formulate it as a multi-dimensional dynamic programming problem and propose approximate dynamic programming methods to accelerate the calculation.Numerical results based on real network in China show that we obtain a speed gain of 7 times faster in average without compromising optimality compared with original dynamic programming algorithm.Numerical results also verify the advantage of robust model compared with deterministic model when facing uncertainties.These findings offer short-term operation methods for gunbarrel natural gas network management to handle with uncertainties.

关键词： Natural gas Gunbarrel gas pipeline networks Robust optimization approximate dynamic programming

approximate dynamic programming solutions with a single network adaptive critic for a class of nonlinear systems

在线全文

学校读者我要写书评

暂无评论

控制理论与应用（英文版） 2011年第3期9卷 370-380页

作者： S.N.BALAKRISHNAN Department of Mechanical and Aerospace Engineering Missouri University of Science and Technology

approximate dynamic programming(ADP) formulation implemented with an adaptive critic(AC)-based neural network(NN) structure has evolved as a powerful technique for solving the Hamilton-Jacobi-Bellman(HJB) equations.As interest in ADP and the AC solutions are escalating with time,there is a dire need to consider possible enabling factors for their implementations.A typical AC structure consists of two interacting NNs,which is computationally expensive.In this paper,a new architecture,called the ’cost-function-based single network adaptive critic(J-SNAC)’ is presented,which eliminates one of the networks in a typical AC structure.This approach is applicable to a wide class of nonlinear systems in engineering.In order to demonstrate the benefits and the control synthesis with the J-SNAC,two problems have been solved with the AC and the J-SNAC approaches.Results are presented,which show savings of about 50% of the computational costs by J-SNAC while having the same accuracy levels of the dual network structure in solving for optimal control.Furthermore,convergence of the J-SNAC iterations,which reduces to a least-squares problem,is discussed;for linear systems,the iterative process is shown to reduce to solving the familiar algebraic Ricatti equation.

关键词： approximate dynamic programming Optimal control Nonlinear control Adaptive critic Cost-functionbased single network adaptive critic J-SNAC architecture

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

在线全文

学校读者我要写书评

暂无评论

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

Real-time Risk-averse Dispatch of an Integrated Electricity and Natural Gas System via Condi-tional Value-at-risk-based Lookup-table Ap-proximate dynamic programming

在线全文

学校读者我要写书评

暂无评论

Protection and Control of Modern Power Systems 2024年第2期9卷 47-60页

作者： Jianquan Zhu Guanhai Li Ye Guo Jiajun Chen Haixin Liu Yuhao Luo Wenhao Liu School of Electric Power Engineering South China University of Tech-nologyGuangzhou 510640China South China University of Technology GuangzhouChina

The real-time risk-averse dispatch problem of an integrated electricity and natural gas system(IEGS)is studied in this paper.It is formulated as a real-time conditional value-at-risk(CVaR)-based risk-averse dis-patch model in the Markov decision process framework.Because of its stochasticity,nonconvexity and nonlinearity,the model is difficult to analyze by traditional algorithms in an acceptable time.To address this non-deterministic polynomial-hard problem,a CVaR-based lookup-table approximate dynamic programming(CVaR-ADP)algo-rithm is proposed,and the risk-averse dispatch problem is decoupled into a series of tractable subproblems.The line pack is used as the state variable to describe the impact of one period’s decision on the future.This facilitates the reduction of load shedding and wind power curtailment.Through the proposed method,real-time decisions can be made according to the current information,while the value functions can be used to overview the whole opti-mization horizon to balance the current cost and future risk loss.Numerical simulations indicate that the pro-posed method can effectively measure and control the risk costs in extreme scenarios.Moreover,the decisions can be made within 10 s,which meets the requirement of the real-time dispatch of an IEGS.Index Terms—Integrated electricity and natural gas system,approximate dynamic programming,real-time dispatch,risk-averse,conditional value-at-risk.

关键词： Integrated electricity and natural gas system approximate dynamic programming real-time dispatch risk-averse conditional value-at-risk

approximate dynamic programming Based Controller Design Using an Improved Learning Algorithm with Application to Tracking Control of Aircraft

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Approximate Dynamic Programming Based Controller Design Usin...

2013年中国智能自动化学术会议

作者： Xiong Luo Yuchao Zhou Zengqi Sun School of Computer and Communication Engineering University of Science and Technology Beijing(USTB) Beijing Key Laboratory of Knowledge Engineering for Materials Science Key Laboratory of Knowledge Engineering for Materials Science Department of Computer Science and Technology Tsinghua University

The strategy using approximate/adaptive dynamic programming(ADP) has been widely used to design a learning controller for complex systems of higher dimension in recent years.This paper aims at handling an important problem in the design of ADP learning controllers,which is the improvement of learning algorithm for its convergence performance.We analyze ADP controller implementation framework according to the requirement of tracking control task,with emphasis on providing an improved weight-updating gradient descent approach in optimizing connection weights in network structures.A comparison of the proposed method and classic ADP design for tracking and controlling pitch angle of aircraft is presented.It verifies the feasibility in the design of the proposed ADP based controller.

关键词： approximate dynamic programming Controller Learning algorithm Aircraft control

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

在线全文

cnki会议

学校读者我要写书评

暂无评论

Online Optimal Operation of Microgrid using Approximate Dyna...

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

An approximate dynamic programming approach for wind power dispatch in wind farms

在线全文

cnki会议

学校读者我要写书评

暂无评论

An approximate dynamic programming approach for wind power d...

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

With appropriate control based on predicted data of renewable energy sources, battery energy storage systems（BESS） can increase the dispatchability of wind and solar power. In a deregulated electricity market, proper charge and discharge decisions of BESS made at different price periods are likely to increase the profit for renewable power plants. In this paper, we propose an energy trading strategy for wind farms with BESS to control the actual power sold to the electric grid. To maximize the income and minimize the battery lifetime consumption, an approximate dynamic programming algorithm is produced to solve the optimal charge and discharge actions of the BESS within a future period. The rolling horizon approach is utilized, and the algorithm works with both short-term and long-term predicted data that are updated in every control step. The use of an approximate technique is to avoid the long computation time resulted from a large-scale BESS, and to meet time constraints in the rolling horizon approach. The algorithm is tested with practical wind power and electricity price data.

关键词： Wind power control battery energy storage system battery lifetime estimation approximate dynamic programming

Microgrid energy management strategy with battery energy storage system and approximate dynamic programming

在线全文

cnki会议

学校读者我要写书评

暂无评论

Microgrid energy management strategy with battery energy sto...

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

Online and Model-Free Supplementary Learning Control Based on approximate dynamic programming

在线全文

cnki会议

学校读者我要写书评

暂无评论

Online and Model-Free Supplementary Learning Control Based o...

第26届中国控制与决策会议

作者： Wentao Guo Feng Liu Jennie Si Shengwei Mei State Key Laboratory of Power Systems Department of Electrical EngineeringTsinghua University Department of Electrical Engineering Arizona State University

An approximate dynamic programming（ADP） based supplementary learning control method is developed to online improve the performance of existing controllers. The proposed supplementary learning structure can make full use of the prior knowledge of the pre-designed controller and endow the controller with learning ability. Moreover, by introducing the action dependent value function for policy evaluation, the supplementary learning control can work in a model-free manner. The policy iteration algorithm is employed to train the actor-critic structure of the ADP supplementary controller. Simulation studies are carried out on the cart-pole system to validate the optimization and the adaptation capability of the proposed methodology.

关键词： approximate dynamic programming Model-Free Online Supplementary Control

Discounted Iterative Adaptive Critic Designs With Novel Stability Analysis for Tracking Control

在线全文

cnki会议

学校读者我要写书评

暂无评论

IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1262-1272页

作者： Mingming Ha Ding Wang Derong Liu School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083China Faculty of Information Technology the Beijing Key Laboratory of Computational Intelligence and Intelligent Systemthe Beijing Laboratory of Smart Environmental Protectionand the Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China Department of Electrical and Computer Engineering University of Illinois at ChicagoChicago IL 60607 USA IEEE

The core task of tracking control is to make the controlled plant track a desired trajectory.The traditional performance index used in previous studies cannot eliminate completely the tracking error as the number of time steps increases.In this paper,a new cost function is introduced to develop the value-iteration-based adaptive critic framework to solve the tracking control problem.Unlike the regulator problem,the iterative value function of tracking control problem cannot be regarded as a Lyapunov function.A novel stability analysis method is developed to guarantee that the tracking error converges to zero.The discounted iterative scheme under the new cost function for the special case of linear systems is elaborated.Finally,the tracking performance of the present scheme is demonstrated by numerical results and compared with those of the traditional approaches.

关键词： Adaptive critic design adaptive dynamic programming(ADP) approximate dynamic programming discrete-time nonlinear systems reinforcement learning stability analysis tracking control value iteration(VI)