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Research on Optimal Electrical Energy Management Method of Smart Grid Based on Value Iteration

IFAC-PapersOnLine 2020年第5期53卷 681-685页

作者： Chunsheng Yan Yongjian Zheng Xun Dai Baiyu Gao Liguang Zhong Shi Xing Ruizhuo Song Liaoning Electric Power Development Stock Co. LtdShenyang 110179China State Grid Liaoning Electric Power Supply Co. LtdShenyang 110006China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing 100083 China

Based on the smart grid residential load data and energy storage terminal data, this paper expands a new optimal control method of smart grid supply and demand matching and *** paper describes the operation principle and defines the required optimization parameters of the control object—electric energy storage management system. Aiming at the characteristics of discrete non linearity of the smart grid, a value iteration algorithm is selected to establish a control strategy,and an adaptive value iteration optimization scheme is *** use the BP neural network to export the control strategy of the electric energy management system by reducing the back propagation error. Finally,we use the day-ahead electricity price data to achieve numerical *** result shows that the adaptive value iteration algorithm methods have higher efficiency and superior adaptability compared with the traditional BP neural network control method.

关键词： adaptive dynamic programming optimal control value iteration smart grid BP neural network

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Event-Triggered adaptive Control for Discrete-Time Zero-Sum Games

Event-Triggered Adaptive Control for Discrete-Time Zero-Sum ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Wang, Ziyang Wei, Qinglai Liu, Derong Luo, Yanhong Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Guangdong Univ Technol Sch Automat Guangzhou Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China

ISBN: (纸本)9781728119854

In this paper, an event-triggered adaptive dynamic programming (ADP) method is developed for the discrete-time nonlinear two-player zero-sum games. First, an event-triggered ADP algorithm is presented to solve the Hamilton-Jacobi-Isaacs (HJI) equation. Then, a novel double event-triggered scheme is designed, the control inputs and the disturbance inputs will be updated only when the triggering conditions are satisfied. Therefore, the computational burden and the communication cost can be reduced. The algorithm is implemented by two neural networks, and the stability of the two-player system is proved. Finally, an example is employed to illustrate the effectiveness of the developed method.

关键词： adaptive dynamic programming neural network event-triggered control zero-sum game

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Incremental Model-Based Global Dual Heuristic programming for Flight Control 13th

Incremental Model-Based Global Dual Heuristic Programming fo...

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13th International-Federation-of-Automatic-Control (IFAC) Workshop on adaptive and Learning Control Systems (ALCOS)

作者： Sun, Bo van Kampen, Erik-Jan Delft Univ Technol Dept Control & Operat NL-2629 HS Delft Netherlands

This paper proposes a novel adaptive dynamic programming method, called Incremental model-based Global Dual Heuristic programming, to generate a self-learning adaptive controller, in the absence of sufficient prior knowledge of system dynamics. An incremental technique is employed for online model identification, instead of the artificial neural networks commonly used in conventional Global Dual Heuristic programming. The incremental model has the capability of tackling nonlinearity and uncertainty of the plant, but can also guarantee high precision of online identification without the requirement of offline training. On the basis of the identified model, two neural networks are adopted to facilitate the implementation of the self-learning controller, by approximating the cost-to-go and its derivatives and the control policy, respectively. Both methods are applied to a tracking control problem of a nonlinear aerospace system and the results show that the proposed method outperforms conventional Global Dual Heuristic programming in online learning speed, tracking precision and robustness to variation of initial system states and network weights. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： adaptive dynamic programming adaptive control incremental technique global dual heuristic programming artificial neural network

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ADP-based active optimal fault-tolerant control for modular manipulator with multiple sensor failure

ADP-based active optimal fault-tolerant control for modular ...

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Chinese Automation Congress (CAC)

作者： Li, Bing Fang, Tao Zhou, Fan Dong, Bo Li, Yuanchun Changchun Univ Technol Dept Control Sci & Engn Changchun Peoples R China Chanchun Univ Sci & Techonlgy Dept Elect Informat Engn Changchun Peoples R China

ISBN: (纸本)9781728140940

In this paper, we propose a novel active fault-tolerant control (AFTC) method for modular manipulators with multiple sensor failure, which is based on adaptive dynamic programming (ADP). Combining the principle of diffeomorphism, a first-order filter is introduced to convert sensor faults into pseudo-actuator faults. Combine the sensor faults estimated by the adaptive sensor fault observer to establish the Hamilton-Jacobi-Bellman (HJB) equation. Based on the ADP, the online policy iteration (PI) algorithm is applied to solve the HJB equation by establishing a critic neural network. It is proved that the closed-loop modular manipulators system is asymptotic stability according to the Lyapunov theory. Simulation results of 2-DOF modular manipulator systems with two configurations demonstrate the validity of the proposed algorithm finally.

关键词： adaptive dynamic programming fault-tolerant control modular manipulators PI algorithm critic neural network

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Block-Decentralized Model-Free Reinforcement Learning Control of Two Time-Scale Networks

Block-Decentralized Model-Free Reinforcement Learning Contro...

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American Control Conference (ACC)

作者： Mukherjee, Sayak Bai, He Chakrabortty, Aranya North Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA Oklahoma State Univ Sch Mech & Aerosp Engn Stillwater OK 74078 USA

ISBN: (纸本)9781538679265

In this paper, we present a cluster-wise decentralized model-free reinforcement learning (RL) based control design for a linear time-invariant consensus network. We assume that the fast dynamics of the network is stable and design the control to shape the slow dynamics. The design exploits timescale separation properties inherent in the slow dynamics of the clusters and the weak couplings between the clusters. The aggregated slow variable from each cluster is used for feedback and decentralized controllers are learned for each cluster. Using singular perturbation theory, we show the sub-optimality of the learned controller and provide closed-loop stability conditions. We prove that this decentralized learning design will produce close-to-optimal performance if the clustering is strong with weak inter-cluster couplings. This design reduces the learning time and the amount of communication links required. The effectiveness of the design is demonstrated using a numerical example.

关键词： Clustered network Reinforcement learning adaptive dynamic programming Block-decentralized control Two time-scale

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Critic-Identifier Structure ADP Based Near-optimal Decentralized Tracking Control of Modular and Reconfigurable Robots 34

Critic-Identifier Structure ADP Based Near-optimal Decentral...

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34th Youth Academic Annual Conference of Chinese-Association-of-Automation (YAC)

作者： Xia, Hongbing Xue, Daiwei Wang, Yanchun Qiao, Aimin Zhao, Bo Bengbu Univ Dept Elect & Elect Engn Bengbu Peoples R China Beijing Normal Univ Sch Syst Sci Beijing Peoples R China

ISBN: (纸本)9781728139364

This paper investigates a near-optimal decentralized tracking control (DTC) scheme for modular and reconfigurable robots (MRRs) via a novel critic-identifier (CI) structure-based adaptive dynamic programming (ADP) algorithm. The DTC problem of MRRs is transformed into an optimal control issue, which consists of local namely controller, local optimal feedback controller and robust compensator. By using desired states of coupled subsystems to substitute their corresponding actual states, the strict norm-boundedness assumption of interconnections can be avoided. By using self-learning ability of neural network (NN), an identifier is constructed to approximate the subsystem dynamics. Then, the local desired control law can be obtained based on identified dynamics. A critic NN is constructed to solve Hamiltonian-Jacobi-Bellman (HJB) equation, and the local tracking feedback control policy is derived. To remove the overall error caused by the substitution, identification and approximation of critic NN, a robust term is added to guarantee the reliable performance of MRR system. The stability of the closed-loop system is ensured to be asymptotically stable by using the Lyapunov's direct methods. Finally, simulation studies show the effectiveness of the proposed scheme.

关键词： adaptive dynamic programming Modular and reconfigurable robots Decentralized tracking control Critic-identifier structure

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Parallel adaptive Critic Designs of Optimal Control for Ice-Storage Air Conditioning Systems

Parallel Adaptive Critic Designs of Optimal Control for Ice-...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Liao, Zehua Wei, Qinglai Song, Ruizhuo Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Peoples R China

ISBN: (纸本)9781728124858

As a kind of distributed energy storage system, ice-storage air conditioning (IAC) system is an effective way for transferring cooling load. A parallel adaptive critic design method is constructed for solving the optimal control problem of IAC systems. adaptive critic design is also called adaptive dynamic programming (ADP). The initial value function of the parallel ADP method is obtained by using particle swarm optimization (PSO) method to pre-train the weights of the neural networks. Another PSO algorithm is utilized in each iteration to achieve the target control, which avoids solving a nonlinear equation in traditional ADP algorithms. Finally, the efficiency of the parallel ADP algorithm will be illustrated through the experiment results.

关键词： Parallel adaptive critic design adaptive dynamic programming particle swarm optimization ice-storage air conditioning

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Nearly Optimal Consensus Control of Discrete Time Multiagent Systems with Time Delays

Nearly Optimal Consensus Control of Discrete Time Multiagent...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Zhang, Yao Mu, Chaoxu Zhao, Qian Wang, Ke Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

ISBN: (纸本)9781728124858

This paper researches the optimal consensus control of discrete time multiagent systems with time delays by applying the heuristic dynamic programming (HDP) iteration algorithm. It is known that the agent can only accept the message from itself and its neighboring agents. The communication among the agents is modeled by using the communication structure graph. The objective is to make all the followers track the desired leader. First, the discrete time multiagent systems with time delays are transformed into systems without time delays. Then the HDP algorithm is used to solve the multiagent consensus control problem. Neural networks are applied to approximate the performance index and the optimal control policy. At last, a simulation study is presented to verify the theoretical conclusion.

关键词： Multiagent systems neural networks consensus control adaptive dynamic programming

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Local Tracking Control for Unknown Interconnected Systems via Neuro-dynamic programming 25th

Local Tracking Control for Unknown Interconnected Systems vi...

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25th International Conference on Neural Information Processing (ICONIP)

作者： Zhao, Bo Liu, Derong Ha, Mingming Wang, Ding Xu, Yancai Wei, Qinglai Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China

ISBN: (纸本)9783030042394;9783030042387

This paper develops a neuro-dynamic programming based local tracking control (LTC) scheme for unknown interconnected systems. By using the local input-output data and the desired states of coupling subsystems, a local neural network (NN) identifier is established to obtain the local input gain matrix online. By introducing a modified local cost function, the Hamilton-Jacobi-Bellman equation is solved by a local critic NN with asymptotically convergent weight vector, which is obtained by nested update law, and the LTC can be derived with the desired state aided augmented subsystem. The stability of the closed-loop system is shown by Lyapunov's direct method. The simulation on the parallel inverted pendulum system illustrates that the developed LTC scheme is effective.

关键词： adaptive dynamic programming Neuro-dynamic programming Local tracking control Optimal control Unknown interconnected systems

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Optimal Trading Strategies in Continuous Double Auctions for Transactive Energy 51

Optimal Trading Strategies in Continuous Double Auctions for...

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51st North American Power Symposium (NAPS)

作者： Liang, Junkai Tang, Wenyuan North Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA

ISBN: (纸本)9781728104072

The current power distribution system is undergoing a transition from centralized markets to decentralized markets. However, the existing economic mechanism for decentralized markets has many disadvantages such as the ignorance of incomplete information, the lack of negotiation process, and the requirement of a high degree of synchronization between the prosumers. In this paper, we propose a novel economic mechanism based on continuous double auction. With respect to the incomplete information, an adaptive dynamic programming based strategy is used to adjust sequential strategies according to the market trends. Several structural results are established, which are then verified by sensitivity analysis.

关键词： adaptive dynamic programming continuous double auction decentralized energy trading renewable energy

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