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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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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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An efficient energy management strategy based on heuristic dynamic programming specialized for hybrid electric unmanned delivery aerial vehicles

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JOURNAL OF CLEANER PRODUCTION 2024年 453卷

作者： Yang, Chao Liang, Buyuan Wang, Weida Wang, Hongwei Yang, Liuquan Chen, Yincong Wang, Muyao Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China Beijing Inst Technol Yangtze Delta Reg Acad Jiaxing 314000 Peoples R China

Large-sized unmanned delivery aerial vehicles (UDAVs) stand out as prominent vertical take-off and landing aircraft, benefiting from integrating a hybrid electric propulsion system (HEPS) to enhance the power-to-weight ratio. Despite this advancement, UDAVs encounter challenges regarding range limitations and the need for swift computational solutions. Addressing these issues necessitates a computationally efficient energy management strategy (EMS) that optimizes power distribution to properly plan engine working points, ultimately aimed at enhancing fuel economy to increase flight range. This study proposes a heuristic dynamic programming (HDP)based EMS tailored for the HEPS of a large-sized UDAV. In this strategy, the HDP framework is adopted to improve computational efficiency by iteratively updating control variables. To account for the specific flight characteristics, a flight state identifier incorporating selected flight command flags applicable to UDAVs is designed. This identifier aids in predicting velocity changes for different flight states by amalgamating corresponding prediction networks into an integrated velocity prediction network (IVPN). Substituting the model network with IVPN in the HDP framework significantly enhances prediction accuracy and guides power distribution towards optimal results, signifying an efficient EMS specialized for UDAVs. The proposed strategy is evaluated against baseline strategies in designed flight scenarios, highlighting a remarkable 51.37% improvement in prediction accuracy, a noteworthy 6.04% reduction in equivalent fuel consumption, and an impressive 95.81% reduction in global computation time. Finally, the actual applicability of the proposed strategy is validated in a hardware-in-loop test. Consequently, the proposed strategy can provide theoretical support for the efficient energy management of diverse large-sized UDAVs.

关键词： Unmanned delivery aerial vehicle Hybrid electric propulsion system Energy management strategy heuristic dynamic programming Fuel economy

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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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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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Bio-inspired heuristic dynamic programming for high-precision real-time flow control in a multi-tributary river system

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KNOWLEDGE-BASED SYSTEMS 2021年 230卷 107381-107381页

作者： Liu, Bao Yang, Jinying Gao, Lei Nazari, Asef Thiruvady, Dhananjay China Univ Petr East China Coll Control Sci & Engn Qingdao 266580 Peoples R China CSIRO Waite Campus Urrbrae SA 5064 Australia Deakin Univ Sch Informat Technol Geelong Vic Australia

Rivers are a type of time-varying, highly nonlinear systems, especially with multiple tributaries. To address low precision and poor real-time flow control of multi-tributary river systems, a heuristic dynamic programming algorithm inspired by regulation mechanisms in the human body (Bio-HDP) is proposed. Bio-HDP consists of several control units including the model network (MNCU), the action network (ANCU), the critic network (CNCU), and the central coordination (CCCU), all of which are developed based on the interactions and mechanisms in human's NEI system (nervous, endocrine, and immune systems). The MNCU approximates the actual system through learning actual data on the control object. The ANCU calculates the control variable according to the error between the current state of the system acquired from the MNCU and the set value. The CNCU is used to evaluate whether the current control variable obtained by the ANCU can meet the control requirements. Finally, the CCCU conducts coordinated regulation of the above three control units in real-time according to the current state of the system, and makes the control mechanism faster and more accurate. By incorporating a biological feedback mechanism into the MNCU, the ANCU, and the CNCU, we adopt a neural network with multi-level feedback for the control mechanism. We select the Chongyang River system in China as the control object to verify the effects of the Bio-HDP control algorithm. The results show that the Bio-HDP algorithm improves speed, adaptability, and accuracy of the algorithm in comparison to other algorithms, and exhibits timely and accurate control effects. (C) 2021 Elsevier B.V. All rights reserved.

关键词： heuristic dynamic programming Biological network Multi-level feedback Water resource Flow control Real-time control River system Biologically inspired

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Deep Stackelberg heuristic dynamic programming for frequency regulation of interconnected power systems considering flexible energy sources

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 106卷 104508-104508页

作者： Yin, Linfei Wu, Yunzhi Guangxi Univ Coll Elect Engn Nanning 530004 Guangxi Peoples R China Guangxi Univ Inst Artificial Intelligence Nanning 530004 Guangxi Peoples R China

The flexible energy sources (FESs) could refuse to execute the generation commands (GCs) if the benefits fell short of their expectations when applying traditional automatic generation control methods, this problem leads to frequency instability and the waste of scheduling resources. Accordingly, a deep Stackelberg heuristic dynamic programming (DSHDP) is proposed for the frequency regulation and power dispatch of smart generation control (SGC). Firstly, the heuristic deep dynamic programming, whose networks are replaced by three deep neural networks, can be trained offline by historical data to fit the optimal performance index. Then, the GCs can be optimized through an online updating strategy for stabilizing the frequency of interconnected power systems. To maximize the benefits of FESs further, an improved SGC framework based on the Stackelberg game is built to let FESs play games with conventional power plants in a fair environment. Finally, the optimal GCs can be distributed by the Nash equilibrium to obtain higher profits than traditional methods, and the control accuracy is increased indirectly. Two cases, i.e., IEEE two-area power system and six-area power system based on China Southern Power Grid (CSPG), are provided to verify the superiority of the DSHDP. The results indicate that the excellent flexibility and stability of the proposed algorithm in complex power systems containing renewable energy and FESs. Totally, the frequency deviation obtained by DSHDP is reduced by up to 59.38%, while the total profit of FESs rises by up to 67.47% when compared with the other seven representative algorithms.

关键词： heuristic dynamic programming Stackelberg game Smart generation control Flexible energy sources Deep neural networks

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An Improved Reinforcement Learning Based heuristic dynamic programming Algorithm for Model-Free Optimal Control 29th

An Improved Reinforcement Learning Based Heuristic Dynamic P...

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29th International Conference on Artificial Neural Networks (ICANN)

作者： Li, Jia Yuan, Zhaolin Ban, Xiaojuan Univ Sci & Technol Beijing Artificial Intelligence & 3D Visualizat Lab Beijing Peoples R China

ISBN: (纸本)9783030616168;9783030616151

For complicated processing industrial area, model-free adaptive control in data-driven schema is a classic problem. This paper proposes an improved reinforcement learning (RL) based heuristic dynamic programming algorithm for optimal tracking control in industrial system. The proposed method designs a double neural networks framework and employs a gradient-based optimization schema to present the optimal control law. Inspired by the experience replay buffer in deep RL learning, historical system trajectories in short-term are also considered in the training phase which achieves the stabilization of network learning. An experimental study based on an simulated industrial device shows that the proposed method is superior to other algorithms in terms of time consumption and control accuracy.

关键词： heuristic dynamic programming Optimal tracking control Model-free Reinforcement learning

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