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An improved heuristic dynamic programming framework for multi-source frequency regulation

APPLIED ENERGY 2025年 387卷

作者： Zhao, Zeyu Yang, Xiyun Chen, Han Chen, He North China Elect Power Univ Sch Control & Comp Engn Beijing Peoples R China

To address the frequency deviation resulting from the growing integration of renewable energy sources into the grid, a game-based regulation strategy involving multiple sources is proposed within the improved heuristic dynamic programming (HDP) framework. This strategy aims to enhance the precision of frequency regulation commands and improve the efficiency of the multi-source system by leveraging the synergistic effects of sourceload uncertainty prediction and the zonal game among the multiple frequency regulation sources. First, to mitigate the impact of source-load uncertainty, an HDP optimization framework is proposed to consider the temporal fluctuation characteristics of load and renewable energy sources. Secondly, to coordinate the power of multiple frequency regulation sources, enhance the frequency restoration speed and overall economy, a zonal game frequency regulation method that considers the characteristics of multiple sources is proposed. Finally, the coordination effect of the strategy is validated through simulation studies. The method put forward is capable of preserving the system's frequency stability as the renewable energy penetration rate increases, and it can reduce the frequency regulation cost and response time.

关键词： Frequency regulation Multi-source power system Automatic generation control Stackelberg game heuristic dynamic programming

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Solving dynamic facility layout problem using a hybridized heuristic dynamic programming approach

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2025年第6期76卷 1066-1086页

作者： Salimpour, Saeideh Pourvaziri, Hani Azab, Ahmed Univ Windsor Windsor ON Canada KFUPM Interdisciplinary Res Ctr Smart Mobil & Logist Dept Ind & Syst Engn Dhahran Saudi Arabia

Today's market volatility shortens product lifecycles and drives constant changes in product mix and demand. Changes in product mix and demand necessitate, in turn, changes to the shopfloor layout. The dynamic facility layout problem (DFLP) addresses layout development over multiple periods, with rearrangements from one period to another. To optimize the DFLP using dynamic programming (DP), the DP state space should be restricted as the problem is NP-hard. Therefore, a two-phased hybridized solution algorithm is being proposed and developed in this article. In the first phase, a heuristic approach is used to determine the set of layouts to be considered in each period. In the second phase, a metaheuristic approach is used to solve the recursive formulation of DP. A genetic algorithm (GA) searches for the best subsets of layouts, each represented by one chromosome. Notably, the GA incorporates a heuristic selection operator guided by a deep neural network algorithm. The best subset of layouts that results in the best multi-period layout plan is found throughout the different GA generations. The proposed method's efficiency is statistically validated through rigorous statistical tests, affirming its superior performance, particularly for large-sized instances of the problem, and showcasing more efficient solutions.

关键词： dynamic facility layout problem reconfigurable manufacturing system heuristic dynamic programming metaheuristics genetic algorithms

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heuristic dynamic programming Based Online Energy Management Strategy for Plug-In Hybrid Electric Vehicles

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2019年第5期68卷 4479-4493页

作者： Liu, Jichao Chen, Yangzhou Zhan, Jingyuan Shang, Fei Beijing Univ Technol Beijing Key Lab Traff Engn Coll Artificial Intelligence & Automat Beijing 100124 Peoples R China

For the online energy optimization problem of plug-in hybrid electric vehicles (P-HEVs), this paper proposes a heuristic dynamic programming (HDP) based online energy management strategy, to minimize the fuel consumption of the P-HEV. First of all, considering the uncertain nonlinear dynamic process of a vehicle in the actual traffic environment, we adopt the back propagation neural network (BPNN) to construct the dynamic model of the P-HEV. Then, on this basis, we utilize the HDP to establish an energy management controller with the aim of minimizing energy consumption of the P-HEV. Moreover, the energy management controller is implemented by an online energy management strategy algorithm. To verify the effect of the controller, we employ a practical route in Beijing road network to simulate the BPNN model of the P-HEV and the proposed energy management strategy. The experimental results show several advantages of our strategy. First, compared to the analytic model, the BPNN model can reflect the real dynamic process of the P-HEV with a higher precision. Second, the assigned torques by the strategy can effectively make the vehicle track the desired vehicle-speeds, and the tracking accuracy of the vehicle-speed is higher than 98%. Besides, on the premise of ensuring the real-time performance, the proposed strategy can further reduce the fuel consumption and emissions of the P-HEV when compared with the existing online energy management strategies, although its fuel consumption is more than that of the offline global optimization energy management strategy by 4% approximately.

关键词： Plug-in hybrid electric vehicle on-line energy optimization heuristic dynamic programming back propagation neural network

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heuristic dynamic programming for Adaptive Virtual Synchronous Generators 51

Heuristic Dynamic Programming for Adaptive Virtual Synchrono...

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51st North American Power Symposium (NAPS)

作者： Saadatmand, Sepehr Nia, Mohamad Saleh Sanjari Shamsi, Pourya Ferdowsi, Mehdi Wunsch, Donald C. Missouri Univ Sci & Technol Dept Elect & Comp Engn Rolla MO 65409 USA

ISBN: (纸本)9781728104072

In this paper a neural network heuristic dynamic programing (HDP) is used for optimal control of the virtual inertia-based control of grid connected three-phase inverters. It is shown that the conventional virtual inertia controllers are not suited for non-inductive grids. A neural network based controller is proposed to adapt to any impedance angle. Applying an adaptive dynamic programming controller instead of a supervised controlled method enables the system to adjust itself to different conditions. The proposed HDP consists of two subnetworks: critic network and action network. These networks can be trained during the same training cycle to decrease the training time. The simulation results confirm that the proposed neural network HDP controller performs better than the traditional direct-fed voltage and/or reactive power controllers in virtual inertia control schemes.

关键词： grid connected inverter heuristic dynamic programming neural network virtual synchronous generator

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heuristic dynamic programming Iterative Algorithm Design Based on BP Neural Network

Heuristic Dynamic Programming Iterative Algorithm Design Bas...

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International Forum on Materials Analysis and Testing Technology (IFMATT 2013)

作者： Zhao, Yu Yang, Jiye Jiangsu Food & Pharmaceut Sci Coll Dept Math Huaian 223000 Peoples R China

ISBN: (纸本)9783037859537

The time-delay delay phenomenon is a kind of widespread physical and biological phenomenon. The existence of time-delay not only give the stability of system analysis and controller design brings great difficulties but also usually make the systems unstable and even cause the system performance deteriorated. We use the adaptive dynamic iterative algorithm to solve this equation. By using the neural network to achieve the iterative algorithm, get the optimal control law of the systems with time delay. The simulation results show that the adaptive dynamic programming method to solve the optimal control of the nonlinear system is effective.

关键词： heuristic dynamic programming Neural Network Iterative Algorithm

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heuristic dynamic programming for Neural Network Vector Control of a Grid-Connected Converter

Heuristic Dynamic Programming for Neural Network Vector Cont...

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IEEE PES General Meeting

作者： Fu, Xingang Li, Shuihui Univ Alabama Dept Elect & Comp Engn Tuscaloosa AL 35487 USA

ISBN: (纸本)9781479964154

This paper analyzes optimal control of a grid-connected converter (GCC) based on the adaptive critic designs (ACDs), especially on heuristic dynamic programming (HDP). Instead of using a trained model neural network to identify the dynamics of the plant, the paper uses exact GCC plant mathematical model to reflect the system dynamics accurately. Thus, the HDP for GCC only contains a critic neural network and an action neural network, which simplifies the control design. The training cycles of the critic and action networks in our HDP design are combined into one training cycle, which reduces the calculation time and makes it more suitable to online training. Results show that the RNN controller based on the designed HDP exhibits good tracking ability. The weights of the critic and action networks are adjusted online to adapt the changing GCC system automatically.

关键词： heuristic dynamic programming adaptive critic designs recurrent neural network grid-connected converter

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heuristic dynamic programming strategy with eligibility traces

Heuristic dynamic programming strategy with eligibility trac...

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American Control Conference 2008

作者： Li, Tao Zhao, Dongbin Yi, Jianqiang Chinese Acad Sci Inst Automat Lab Complex Syst & Intelligence Sci Beijing 100080 Peoples R China

ISBN: (纸本)9781424420780

In traditional Adaptive dynamic programming (ADP), only one step estimate is considered for training process, Thus, learning efficiency is lower. If more steps estimates are included, learning process will be speed up. Eligibility traces record the past and current gradients of estimation. It can be used to work with ADP for speeding up learning. In this paper, heuristic dynamic programming (HDP) which is a typical structure of ADP is considered. An algorithm, HDP(A), integrating HDP with eligibility traces is presented. The algorithm is illustrated from both forward view and back view for clear comprehension. Equivalency of two views is analyzed. Furthermore, differences between HDP and HDP(A) are considered from both aspects of theoretic analysis and simulation results. The problem of balancing a pendulum robot (pendubot) is adopted as a benchmark. The results indicate that compared to HDP, HDP(A) shows higher convergence rate and training efficiency.

关键词： heuristic dynamic programming adaptive dynamic programming eligibility trace pendulum robot

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Ecological Adaptive Cruise Control and Energy Management Strategy for Hybrid Electric Vehicles Based on heuristic dynamic programming

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2019年第9期20卷 3526-3535页

作者： Li, Guoqiang Goerges, Daniel Univ Kaiserslautern Dept Elect & Comp Engn D-67663 Kaiserslautern Germany

In this paper, an ecological adaptive cruise controller (ECO-ACC) for parallel hybrid electric vehicles (HEVs) in a car-following scenario is presented to improve the fuel economy and to maintain a desired inter-vehicle distance from the preceding vehicle. An ACC based on action dependent heuristic dynamic programming (ADHDP) is proposed to obtain an ecological velocity profile and realize an active distance control in normal driving situations. ADHDP is able to adapt internal parameters online and can thus deal with systems with disturbances. Furthermore, an adaptive energy management strategy for HEVs is introduced to control the gear shift and power split for fuel consumption optimization. The gear shift command is designed by enumeration, and the power distribution between the engine and the electric motor is performed by ADHDP. The developed ACC and energy management strategy are finally combined to an ECO-ACC to achieve a multi-objective optimization. Only the current velocity and acceleration of the preceding vehicle are used while knowledge about the future velocity is not needed. The simulations of different driving cycles indicate that the ECO-ACC can lead to near-optimal fuel economy and comfortable driving.

关键词： Adaptive cruise control energy management strategy heuristic dynamic programming

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Virtual guide automatic berthing control of marine ships based on heuristic dynamic programming iteration method q

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NEUROCOMPUTING 2021年 437卷 289-299页

作者： Liu, Qi Li, Tieshan Shan, Qihe Yu, Renhai Gao, Xiaoyang Dalian Maritime Univ Nav Coll Dalian 116026 Liaoning Peoples R China Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China

This paper addresses the berthing control problem for automatic ships by using a virtual guide system based on heuristic dynamic programming (HDP) method. Firstly, by introducing an automatic virtual guide system, the berthing control problem can be transformed into a tracking control problem, and then can be further transformed into an optimal regulation problem. Secondly, the HDP method is used to solve the optimal regulation control problem of the marine surface ship with unknown ship model. Then, it is proven that the tracking error, the adaptation laws and the control inputs are uniformly bounded on the basis of the Lyapunov theory. Finally, simulations are carried out on an automatic model ship and the HDP method is compared with the backstepping control to verify the effectiveness of the developed control scheme. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Berthing control Virtual guide control Unknown ship model heuristic dynamic programming

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Zero-sum game optimal control for the nonlinear switched systems based on heuristic dynamic programming

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OPTIMAL CONTROL APPLICATIONS & METHODS 2023年第5期44卷 2821-2837页

作者： Fu, Xingjian Li, Zizheng Beijing Informat Sci & Technol Univ Sch Automat Beijing Peoples R China Beijing Informat Sci & Technol Univ Sch Automat Beijing 100192 Peoples R China

In this article, the zero-sum game problem of control and disturbance is studied for discrete affine nonlinear switched systems with unknown dynamical models. A two-level heuristic dynamic programming iterative algorithm for solving the zero-sum game problem is proposed, which can be used to solve the Hamilton-Jacobi-Isaacs equation associated with the optimal regulation control problem. The convergence analysis based on the value function and control strategy is given. For algorithm implementation, neural networks are used to approximate the control strategy, disturbance strategy, and value function of the game dynamic programming, respectively. A model network is used to approximate the system states with an unknown dynamical model. The iterative algorithm steps are given. Finally, the validity of the method is verified by simulation examples.

关键词： heuristic dynamic programming neural networks optimal control switched system zero-sum game

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