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dynamic Event-Triggered Consensus Control for Multi-Agent Systems Using adaptive dynamic programming

ieee ACCESS 2022年 10卷 110285-110293页

作者： Zhang, Qi Yang, Yang Xie, Xiaoran Xu, Chunming Yang, Han Changchun Univ Sci & Technol Sch Elect Informat Engn Changchun 130013 Peoples R China Changchun Shikai Sci & Technol Ind Co Ltd Changchnu 130015 Peoples R China Beijing Engn Res Ctr Emergency Survival Secur Beijing 102206 Peoples R China

The consensus optimal control problem for a class of linear multi-agent systems with directed communication networks is studied in this paper using adaptive dynamic programming. To overcome the restrictions of the agent's low processing capability and to extend the actuator's lifetime, consider the event-triggered. In the beginning, a dynamic event-triggered is provided, with several existing static event-triggered serving as special examples. When using the dynamic event-triggered, a longer interval can be shown between any two consecutive event-triggered. Designing a dynamic event-triggered control law becomes more challenging when implemented in directed networks. In addition, on the basis of dynamic event-triggered, a novel adaptive dynamic programming is used to construct a suitable dynamic event-triggered control law, which employs just the interaction information between agents and does not need model dynamics, overcoming the difficulty of solving the algebraic Riccati equation. Finally, the effectiveness of the proposed method is verified by simulation results, and no agent exhibits Zeno behavior.

关键词： Multi-agent systems dynamic programming Behavioral sciences Optimal control Protocols Decentralized control Convergence Multi-agent systems event-triggered adaptive dynamic programming reinforcement learning

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Broad learning With reinforcement learning Signal Feedback: Theory and Applications

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ieee TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2022年第7期33卷 2952-2964页

作者： Mao, Ruiqi Cui, Rongxin Chen, C. L. Philip Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China Univ Macao Fac Sci & Technol Dept Comp & Informat Sci Macau 99999 Peoples R China

Broad learning systems (BLSs) have attracted considerable attention due to their powerful ability in efficient discriminative learning. In this article, a modified BLS with reinforcement learning signal feedback (BLRLF) is proposed as an efficient method for improving the performance of standard BLS. The main differences between our research and BLS are as follows. First, we add weight optimization after adding additional nodes or new training samples. Motivated by the weight iterative optimization in the convolution neural network (CNN), we use the output of the network as feedback while employing value iteration (VI)-based adaptive dynamic programming (ADP) to facilitate calculation of near-optimal increments of connection weights. Second, different from the homogeneous incremental algorithms in standard BLS, we integrate those broad expansion methods, and the heuristic search method is used to enable the proposed BLRLF to optimize the network structure autonomously. Although the training time is affected to a certain extent compared with BLS, the newly proposed BLRLF still retains a fast computational nature. Finally, the proposed BLRLF is evaluated using popular benchmarks from the UC Irvine Machine learning Repository and many other challenging data sets. These results show that BLRLF outperforms many state-of-the-art deep learning algorithms and shallow networks proposed in recent years.

关键词： Training Standards learning systems Games Heuristic algorithms Face recognition reinforcement learning adaptive dynamic programming (ADP) broad learning system (BLS) feedback incremental nodes reinforcement learning (RL) value iteration (VI) weight optimization

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Degradation adaptive Energy Management Strategy for FCHEV Based on the Rule-DDPG Method: Tailored to the Current SOH of the Powertrain

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ieee TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 2025年第1期11卷 993-1003页

作者： Tang, Xingwang Shi, Lei Zhang, Yujia Li, Bingbing Xu, Sichuan Song, Ziyou Tongji Univ Sch Automot Studies Shanghai 201804 Peoples R China UCL Dept Elect & Elect Engn London WC1E 6BT England Natl Univ Singapore Dept Mech Engn Singapore 117575 Singapore

To develop an energy management strategy (EMS) that adapts to the state-of-health (SOH) of the fuel cell stack (FCS) to keep the output of the powertrain of fuel cell hybrid electric vehicles (FCHEVs) optimal all the time, a degradation adaptive EMS is proposed. The proposed EMS is based on a hierarchical framework, where the upper layer involves a degradation rate model to estimate the SOH of the FCS online and acquire performance characteristics at different SOH levels. In the lower layer, an EMS using the Rule-DDPG algorithm is developed. The results prove that the proposed method outperforms existing mainstream EMSs and aligns closely with the dynamic programming approach regarding economic and durability indicators. Compared to nonadaptive EMSs across various driving cycles, the proposed degradation adaptive EMS can consistently achieve the lowest energy consumption and optimal durability performance, tailored to the current SOH of the powertrain. In addition, a comparison between offline simulation and hard-in-the-loop testing validates the computational efficiency and real-time applicability of the proposed strategy in practical environments. Specifically, the calculation time for each sample during the WLTC cycle is shown to be less than 213.214 ms, with an average calculation time of 168.637 ms and a minimum calculation time of 150.618 ms.

关键词： Degradation Energy management Fuel cells Voltage Batteries Mechanical power transmission Medical services Deep reinforcement learning degradation adaption energy management strategy (EMS) fuel cell durability fuel cell hybrid electric vehicle (FCHEVs)

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Cooperative Ecological adaptive Cruise Control for Plug-In Hybrid Electric Vehicle Based on Approximate dynamic programming

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ieee TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第3期72卷 3132-3145页

作者： Li, Jie Liu, Yonggang Fotouhi, Abbas Wang, Xiangyu Chen, Zheng Zhang, Yuanjian Li, Liang Chongqing Univ State Key Lab Mech Transmiss Chongqing 400044 Peoples R China Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400044 Peoples R China Cranfield Univ Adv Vehicle Engn Ctr Sch Aerosp Transport & Mfg Cranfield MK43 0AL England Tsinghua Univ State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China Kunming Univ Sci & Technol Fac Transportat Engn Kunming 650500 Peoples R China Loughborough Univ Dept Aeronaut & Automot Engn Loughborough LE11 3TU Leics England

Eco-driving control generates significant energy-saving potential in car-following scenarios. However, the influence of preceding vehicle may impose unnecessary velocity waves and deteriorate fuel economy. In this research, a learning-based method is exploited to achieve satisfied fuel economy for connected plug-in hybrid electric vehicles (PHEVs) with the advantage of vehicle-to-vehicle communication system. A data-driven energy consumption model is leveraged to generate reinforcement signals for approximate dynamic programming (ADP) with the consideration of nonlinear efficiency characteristics of hybrid powertrain system. An advanced ADP scheme is designed for connected PHEVs driving in car-following scenarios. In addition, the cooperative information is incorporated to further improve the fuel economy of the vehicle under the premise of driving safety. The proposed method is mode-free and showcases acceptable computational efficiency as well as adaptability. The simulation results demonstrate that the fuel economy during car-following processes is remarkably improved through cooperative driving information, thereby partially paving the theoretical basis for energy-saving transportation.

关键词： Approximate dynamic programming cooperative adaptive cruise control eco-driving plug-in hybrid electric vehicle velocity optimization

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reinforcement learning-Based Time-Synchronized Optimized Control for Affine Systems

IEEE Transactions on Artificial Intelligence

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ieee Transactions on Artificial Intelligence 2024年第10期5卷 5216-5231页

作者： Zhang, Yuxiang Liang, Xiaoling Li, Dongyu Ge, Shuzhi Sam Gao, Bingzhao Chen, Hong Lee, Tong Heng National University of Singapore Department of Electrical and Computer Engineering 117576 Singapore Beihang University School of Cyber Science and Technology Beijing100191 China Tianmushan Laboratory Hangzhou310023 China Tongji University Clean Energy Automotive Engineering Center Shanghai201804 China

The approach of (fixed-) time-synchronized control (FTSC) aims at attaining the outcome where all the system state-variables converge to the origin simultaneously/synchronously. This type of outcome can be the highly essential performance desired in various real-world high-precision control applications. Toward this objective, this article proposes and investigates the development of a time-synchronized reinforcement learning algorithm (TSRL) applicable to a particular class of first- and second-order affine nonlinear systems. The approach developed here appropriately incorporates the norm-normalized sign function into the optimal system control design, leveraging on the special properties of this norm-normalized sign function in attaining time-synchronized stability and control. Concurrently, the actor–critic framework in reinforcement learning (RL) is invoked, and the dual quantities of system control and gradient term of the cost function are decomposed with appropriate time-synchronized control items and unknown actor/critic part and to be learned independently. By additionally employing the adaptive dynamic programming technique, the solution of the Hamilton–Jacobi–Bellman equation is iteratively approximated under this actor–critic framework. As an outcome, the proposed TSRL method optimizes the system control while attaining the notable time-synchronized convergence property. The performance and effectiveness of the proposed method are demonstrated to be effectively applicable via detailed numerical studies and on an autonomous vehicle nonlinear system motion control problem. © 2020 ieee.

关键词： Synchronization

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Actor-Critic Optimal Control for Semi-Markovian Jump Systems With Time Delay

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ieee TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2024年第4期71卷 2164-2168页

作者： Zhang, Lulu Zhang, Huaguang Yue, Xiaohui Wang, Tianbiao Northeastern Univ Coll Informat Sci & Engn Shenyang 110004 Liaoning Peoples R China Northeastern Univ State Key Lab Synthet Automation Proc Ind Shenyang 110004 Liaoning Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110004 Liaoning Peoples R China

In this brief, an actor-critic neural network (NN) framework is constructed to address the optimal regulation problem of the nonlinear semi-Markovian jump systems with state and input delays. Firstly, a specialized cost function associated with semi-Markovian jump parameters (s-MJPs) is defined. Then, the resolution of the Hamilton-Jacobian-Bellman equation is ascertained based on the application of actor-critic NN, in which updating laws for both the actor and critic NNs are devised to concomitantly execute the reinforcement learning strategy. Wherein the temporal difference error takes into account state and input delays. An actor-critic NN scheme is designed to approximate the unknown dynamic for time-delay systems with s-MJPs. Utilizing the Lyapunov method, this brief successfully demonstrates the ultimate uniform boundedness of the considered system. Finally, the practical simulation results presented serve to validate the efficacy of the proposed algorithm.

关键词： adaptive dynamic programming time delay semi-Markovian jump systems optimal control

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reinforcement learning-Based Structural Control of Floating Wind Turbines

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ieee TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第3期52卷 1603-1613页

作者： Zhang, Jincheng Zhao, Xiaowei Wei, Xing Univ Warwick Sch Engn Coventry CV4 7AL W Midlands England

The structural control of floating wind turbines using active tuned mass damper is investigated in this article. To our knowledge, this is for the first time that reinforcement learning-based control approach is employed to this type of application. Specifically, an adaptive dynamic programming (ADP) algorithm is used to derive the optimal control law based on the nonlinear structural dynamics, and the large-scale machine learning platform Tensorflow is employed for the design and implementation of the neural network (NN) structure. Three fully connected NNs, i.e., a plant network, a critic network, and an action network, are included in the proposed NN structure. Their training requires the gradient information flowing through the whole network, which is tackled by automatic differentiation, a popular technique for deriving the gradients of complex networks automatically. While to our knowledge, the network structures in the existing literature are rather simple and the training of the hidden layer is usually ignored. This allows their gradients to be derived analytically, which is infeasible with complex network structures. Thus, automatic differentiation greatly improves the employed ADP algorithm's ability in solving complex problems. The simulation results of structural control of floating wind turbines show that ADP controller performs very well in both normal and extreme conditions, with the standard deviation of the platform pitch displacement being reduced by around 40%. A clear advantage of ADP controllers over the H-infinity controller is observed, especially in extreme conditions. Moreover, our design considers the tradeoff between the control performance and power consumption.

关键词： Wind turbines Resists Artificial neural networks Training Vibrations dynamic programming Shock absorbers Active structural control adaptive dynamic programming (ADP) floating wind turbine neural networks (NNs) reinforcement learning

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Latency and Energy Minimization in NOMA-Assisted MEC Network: A Federated Deep reinforcement learning Approach 29

Latency and Energy Minimization in NOMA-Assisted MEC Network...

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29th ieee symposium on Computers and Communications (ieee ISCC)

作者： Ahmadi, Arian Host-Madsen, Anders Xiong, Zixiang Univ Hawaii Manoa Dept Elect Engn Honolulu HI 96822 USA Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA

ISBN: (纸本)9798350354249;9798350354232

Multi-access edge computing (MEC) is seen as a vital component of forthcoming 6G wireless networks, aiming to support emerging applications that demand high service reliability and low latency. However, ensuring the ultra-reliable and low-latency performance of MEC networks poses a significant challenge due to uncertainties associated with wireless links, constraints imposed by communication and computing resources, and the dynamic nature of network traffic. Enabling ultra-reliable and low-latency MEC mandates efficient load balancing jointly with resource allocation. In this paper, we investigate the joint optimization problem of offloading decisions, computation and communication resource allocation to minimize the expected weighted sum of delivery latency and energy consumption in a non-orthogonal multiple access (NOMA)-assisted MEC network. Given the formulated problem is a mixed-integer non-linear programming (MINLP), a new multi-agent federated deep reinforcement learning (FDRL) solution based on double deep Q-network (DDQN) is developed to efficiently optimize the offloading strategies across the MEC network while accelerating the learning process of the Internet-of-Thing (IoT) devices. Simulation results show that the proposed FDRL scheme can effectively reduce the weighted sum of delivery latency and energy consumption of IoT devices in the MEC network and outperform the baseline approaches.

关键词： Federated learning

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Approximate Optimal Indirect Regulation of an Unknown Agent With a Lyapunov-Based Deep Neural Network

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ieee CONTROL SYSTEMS LETTERS 2023年 7卷 2773-2778页

作者： Makumi, Wanjiku A. Bell, Zachary I. Dixon, Warren E. Univ Florida Dept Mech & Aerosp Engn Gainesville FL 32611 USA Air Force Res Lab Munit Directorate Eglin AFB FL 32542 USA

An approximate optimal policy is developed for a pursuing agent to indirectly regulate an evading agent coupled by an unknown interaction dynamic. Approximate dynamic programming is used to design a controller for the pursuing agent to optimally influence the evading agent to a goal location. Since the interaction dynamic between the agents is unknown, integral concurrent learning is used to update a Lyapunov-based deep neural network to facilitate sustained learning and system identification. A Lyapunov-based stability analysis is used to show uniformly ultimately bounded convergence. Simulation results demonstrate the performance of the developed method.

关键词： Deep learning System identification Function approximation Real-time systems Optimal control Artificial neural networks Aerodynamics Deep neural networks reinforcement learning adaptive control Lyapunov methods nonlinear control systems

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DRL-ECMS: An adaptive Hierarchical Equivalent Consumption Minimization Strategy Based on Deep reinforcement learning

DRL-ECMS: An Adaptive Hierarchical Equivalent Consumption Mi...

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33rd ieee Intelligent Vehicles symposium (ieee IV)

作者： Lin, Yang Chu, Liang Hu, Jincheng Zhang, Yuanjian Hou, Zhuoran Jilin Univ State Key Lab Automot Dynam Simulat & Control Changchun Peoples R China Univ Glasgow Sch Comp Sci Glasgow Lanark Scotland Queens Univ Belfast W Tech Ctr Belfast Antrim North Ireland

ISBN: (纸本)9781665488211

With the rise of machine learning, reinforcement learning (RL) is gradually applied to the energy management strategy (EMS) of plug-in hybrid electric vehicle (PHEV). Some old algorithms have also achieved better results by combining with reinforcement learning. In order to learn from the advantages of previous algorithms and explore the application potential of reinforcement learning algorithm, this paper proposes an adaptive hierarchical management strategy combining equivalent consumption minimization strategy (ECMS) knowledge with proximal policy optimization (PPO). This system is an advanced data-driven RL algorithm at present. For a more comprehensive comparison, this paper compares the proposed EMS with dynamic programming (DP), ECMS with constant equivalence factor and q-learning. The results show that the fuel consumption of the proposed control strategy is very close to that of the DP-based control strategy and the performance is better than the other two strategies. It shows that deep reinforcement learning can help ECMS solve the problem of dynamic factor planning and DRL-ECMS has the potential of deployment in real-time system.

关键词： reinforcement learning energy management equivalent consumption minimization strategy

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