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Hybrid Iteration ADP Algorithm to Solve Cooperative, Optimal Output Regulation Problem for Continuous-Time, Linear, Multiagent systems: Theory and Application in Islanded Modern Microgrids With IBRs

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ieee TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2024年第1期71卷 834-845页

作者： Qasem, Omar Davari, Masoud Gao, Weinan Kirk, Daniel R. Chai, Tianyou Florida Inst Technol Coll Engn & Sci Melbourne FL 32901 USA Georgia Southern Univ Dept Elect & Comp Engn Statesboro Campus Statesboro GA 30460 USA Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

In this article, we propose a novel adaptive dynamic programming (ADP) algorithm, named hybrid iteration (HI), to solve the cooperative, optimal output regulation problem ((CORP)-R-2) for continuous-time, linear, multi-agent systems. Unlike the traditional ADP algorithms, i.e., policy iteration (PI) and value iteration (VI), HI does not need an initial stabilizing control policy required by PI. At the same time, it maintains a faster convergence rate compared with VI. First, a model-based HI algorithm is proposed to solve the (CORP)-R-2. Based on the proposed HI algorithm, a data-driven, adaptive, optimal controller is developed to solve the cooperative, adaptive, and optimal output regulation problem without using any information about the physics of the system. Instead, the states/input information collected along the trajectories of the dynamic system is employed. The proposed data-driven HI is applied to the adaptive, optimal secondary voltage control (also known as voltage restoration control) of an islanded modern microgrid based on the inverter-based resources. Compared with the VI and PI algorithms, comparative simulation results demonstrate that the proposed HI approach is significantly able to save the convergence time of the central processing unit (also known as CPU) deployed, reduce the number of learning iterations, and remove the requirement of the initial stabilizing control policy. Comparative experiments reveal the practicality and superiority of the proposed methodology.

关键词： Regulation Convergence Heuristic algorithms Symmetric matrices Optimal control Multi-agent systems Microgrids Adaptive dynamic programming (ADP) continuous-time cooperative linear multiagent systems (MASs) optimal output regulation reinforcement learning

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FedAWS: A Federated Tuning Approach with Adaptive Weight Shrinking for Pre-trained Foundation Models 13

FedAWS: A Federated Tuning Approach with Adaptive Weight Shr...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Cheng, Yubin Song, Zhihuan Jiang, Xiaoyu Yao, Le Zhejiang University State Key Laboratory of Industrial Control Technology Institute of Industrial Process Control College of Control Science and Engineering Hangzhou310027 China School of Mathematics Hangzhou Normal University Hangzhou China

ISBN: (纸本)9798350361674

In recent years, pre-trained foundation models have developed rapidly and have been applied to various downstream tasks through fine-tuning methods. However, due to the existence of data silos, the lack of data for fine-tuning has become a vital issue preventing the application of the foundation model to industry. In addition, the high complexity of the network makes the foundation model prone to overfitting when used for data-constrained industrial tasks. Therefore, a federated tuning approach with adaptive weight shrinking, named FedAWS, is proposed for collaborative fine-tuning between factories. FedAWS adaptively selects the weight-shrinking factor to reduce overfitting and improve the generalization of the global model without relying on additional data sets or human experience. The case study on an industrial dataset demonstrates the effectiveness of FedAWS. This successful validation provides a new way for inter-factory collaboration to apply the foundation model to industry, thus facilitating the efficient utilization of industrial data. © 2024 ieee.

关键词： Federated learning Fine-tuning Foundation Model Weight Shrinking

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Aerodynamic Parameter Identification of Aircraft Based on GA-BP Algorithm 13

Aerodynamic Parameter Identification of Aircraft Based on GA...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Zhao, Yu Wang, Shihui Sun, You Chen, Si Lan, Tianpeng Zhang, Yingbin Beijing Aerospace Automatic Control Institute China Academy of Launch Vehicle Technology Beijing100039 China

ISBN: (纸本)9798350361674

Aiming at the problem of aerodynamic parameter identification of a certain type aircraft, the GA-BP algorithm is used to identify the coefficients of aerodynamic force. Firstly, the coefficients are solved by using flight trajectory data obtained from wind tunnel simulation experiments. Then, the relationships between aerodynamic force coefficients and Mach, angle of attack, rudder pendulum angle, etc. are fitted by BP neural network. The thresholds and weights between neurons are optimized by genetic algorithm during the training of neural network, so that the optimal structure of neural network can be obtained through training of multiple sets of flight trajectory data under different flight conditions. Simulation results show that the aerodynamic force coefficients obtained by the GA-BP algorithm have higher accuracy compared with traditionally used engineering methods. © 2024 ieee.

关键词： Flight paths

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Fixed-Time Observer Based Adaptive Prescribed Performance control for Full State Feedback System with Disturbance 13

Fixed-Time Observer Based Adaptive Prescribed Performance Co...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Sun, Guofa Liang, Boyang School of Information and Control Engineering Qingdao University of Technology Qingdao266520 China

ISBN: (纸本)9798350361674

This article proposes a fixed-time adaptive controller based on disturbance observer to optimize the disturbance resistance and dynamic performance of full state feedback system. Firstly, a fixed-time prescribed performance (PP) function is used to transform the error function, and a new control law is proposed to ensure that the tracking error converges within a fixed-time and improve the dynamic performance of considered system;Secondly, a fixed-time disturbance observer (FT-DO) is used to observe disturbance signals and compensate to improve the performance of the system;Besides, considering the uncertainty of the system, a new update law is constructed by combining neural network (NN) and fixed-time PP function;As a result, a fixed-time control scheme is obtained, to increase the robustness of the system, where commander filter is adopted to decrease complexity;Finally, the effectiveness of the method was further verified through simulation. © 2024 ieee.

关键词： State feedback

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A Compact Constraint Incremental Method for Random Weight Networks and Its Application

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ieee TRANSACTIONS ON NEURAL NETWORKS AND learning systems 2024年第11期35卷 16936-16944页

作者： Wang, Qianjin Dai, Wei Zhang, Chunfu Zhu, Jiaji Ma, Xiaoping Yancheng Inst Technol Sch Elect Engn Yancheng 224051 Peoples R China China Univ Min & Technol Sch Informat & Control Engn Engn Res Ctr Intelligent Control Underground Spac Minist Educ Xuzhou 221116 Jiangsu Peoples R China

Incremental random weight networks (IRWNs) face the issues of weak generalization and complicated network structure. There is an important reason: the learning parameters of IRWNs are determined in a random fashion without guidance, which may increase many redundant hidden nodes, and thereby leading to inferior performance. To resolve this issue, a novel IRWN with compact constraint that guides the assignment of random learning parameters (CCIRWN) is developed in this brief. Using the iteration method of Greville, a compact constraint that simultaneously assures the quality of generated hidden nodes and the convergence of the CCIRWN is built to perform learning parameter configuration. Meanwhile, the output weights of the CCIRWN are assessed analytically. Two types of learning methods for constructing the CCIRWN are proposed. Finally, the performance evaluation of the proposed CCIRWN is undertaken on the 1-D nonlinear function approximation, several real-world datasets, and data-driven estimation based on the industrial data. Numerical and industrial examples indicate that the proposed CCIRWN with compact structure can achieve favorable generalization ability.

关键词： Computational modeling learning systems Task analysis Convergence Training Computational efficiency Optimization Compact constraint compact structure favorable generalization Greville's method incremental random weight networks (IRWNs)

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Dense-Descriptor Transporter Networks for Multidimensional Deformable Object Manipulation 13

Dense-Descriptor Transporter Networks for Multidimensional D...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Cao, Yang Gong, Daoxiong Yu, Jianjun Beijing University of Technology Faculty of Information Technology Beijing100124 China

ISBN: (纸本)9798350361674

With the continuous development of robotics, there are emerging new trends in robotic manipulation of deformable objects, calling for improved adaptability and flexibility to better cope with complex environments and task requirements. Due to the high-dimensional state space and complex dynamics of deformable objects during deformation, it is challenging for neural networks to directly extract object features such as shape, texture and color from images. In this paper, we propose an imitation learning approach that utilizes dense descriptor modules to improve its perception of different deformable objects and deformations. The learned descriptors can extract the features of deformable objects from RGB-D images and map them to the descriptor space, making the representation of objects more compact and robust. Furthermore, our proposed approach demonstrates better competitiveness in simulation environments featuring fabrics, ropes, or bags when compared to the baseline methods. © 2024 ieee.

关键词： Contrastive learning

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Fixed-Time Inter-Axis Synchronization control of Manipulators based on Active Disturbance Rejection Method 13

Fixed-Time Inter-Axis Synchronization Control of Manipulator...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Zhao, Hanpu Liu, Yang Seventh Research Division Beijing100191 China

ISBN: (纸本)9798350361674

This paper investigates the fixed-time inter-axis synchronization tracking control for n-degree of freedom (DOF) uncertain manipulators. First, a dynamic model for the manipulator is presented, and a fixed-time disturbance observer is designed to compensate for system uncertainties and external disturbances. Then, a non-singular fixed-time active disturbance rejection inter-axis synchronization controller is designed, which not only avoids the singularity problem in traditional terminal sliding mode control, but also realizes the active suppression of disturbances. It is proved that the tracking error of each joint of the uncertain manipulator converges synchronously to zero within a fixed time. Finally, simulations are conducted with a 2-DOF manipulator, and results demonstrate that the method can achieve fixed-time inter-axis synchronization control and possesses robustness against the lumped disturbances. © 2024 ieee.

关键词： Disturbance rejection

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Short-Term Load Prediction Using CNN-LSTM Network and Sparrow Search Algorithm for Combined Cooling Heating and Power systems 13

Short-Term Load Prediction Using CNN-LSTM Network and Sparro...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Zhou, Yaxin Li, Feng Chen, Yonghui School of Electrical and Information Engineering Jiangsu University of Technology Changzhou213001 China

ISBN: (纸本)9798350361674

In combined cooling heating and power (CCHP) systems, the interaction and impact between cooling, heating and electrical energy play a vital role, and accurate load forecasting is the key to economic dispatch and efficient operation of CCHP systems. In this paper, based on convolutional neural networks-long short-term memory network (CNN-LSTM) model and sparrow search algorithm (SSA), the short-term multiple load prediction of CCHP system is studied. First, the spatio-temporal features of the load data are extracted using CNN and LSTM respectively, and the CNN-LSTM network model is constructed. Subsequently, the SSA is used to optimize the learning parameters of the CNN-LSTM network model, namely, learning rate, training times and number of neurons of the LSTM. Simulation results show that compared with other existing prediction techniques, the proposed method can achieve better prediction accuracy in cooling, heating and electrical energy, and mean absolute error and mean absolute percentage error are reduced, which testify the effectiveness and feasibility of the proposed method. © 2024 ieee.

关键词： Cooling systems

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learning Hierarchical control For Multi-Agent Capacity-Constrained systems 63

Learning Hierarchical Control For Multi-Agent Capacity-Const...

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63rd ieee conference on Decision and control, CDC 2024

作者： Vallon, Charlott Pinto, Alessandro Stellato, Bartolomeo Borrelli, Francesco University of California Berkeley Department of Mechanical Engineering BerkeleyCA94720 United States California Institute of Technology Nasa Jet Propulsion Laboratory PasadenaCA91109 United States Princeton University Department of Operations Research and Financial Engineering PrincetonNJ08544 United States

ISBN: (纸本)9798350316339

This paper introduces a data-driven hierarchical control scheme for a fleet of nonlinear, capacity-constrained autonomous agents in an iterative environment. The proposed control framework consists of a high-level dynamic task assignment and routing layer and low-level motion planning and tracking layer. Each layer uses a data-driven Model Predictive control (MPC) policy for efficient calculation of new task assignments and actuation. We use collected data to iteratively refine estimates of agent capacity usage, and update MPC policy parameters accordingly. We leverage tools from iterative learning control to integrate learning at both hierarchy levels, and coordinate learning between levels to maintain closed-loop feasibility and performance improvement at each iteration. © 2024 ieee.

关键词： Predictive control systems

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Swarm learning for Secure and Effective Industrial Federated Big data Analytics

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ieee TRANSACTIONS ON RELIABILITY 2025年第2期74卷 2893-2903页

作者： Jiang, Xiaoyu Cheng, Yubin Song, Zhihuan Ma, Xiaoguang Ye, Lingjian Ge, Zhiqiang Guangdong Univ Petrochem Technol Sch Automat Maoming 525000 Peoples R China Zhejiang Univ Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Huzhou Univ Sch Engn Huzhou Key Lab Intelligent Sensing & Optimal Contr Huzhou 313000 Peoples R China Southeast Univ Sch Math Nanjing 210096 Peoples R China

Industrial intelligent systems (IIS) play a huge role in modern industry, and their intelligent models of IIS enable diagnosis of faults, key performance indicator (KPI) prediction, and other important industrial process analysis in a data-driven way. However, the performance of intelligent models is limited by the quantity and quality of local data in specific factories. At the same time, the privacy information and security concerns contained by industrial data lead to the problem of information silos in industry. This hinders data sharing and cross-factory collaborations. To address these issues, this article makes the following contributions. First, for the first time, we empower industrial federated big data analytics (IFBDA) of IIS with swarm learning, and propose a hyperledger fabric-based IFBDA blockchain (IFBDAchain) for multifactory information sharing and collaborative modeling. Second, in the IFBDAchain, we further consider potential dishonest behaviors among federated members, and design verification and privacy protection mechanisms to ensure trustworthiness of analytics. Third, we validate the IFBDAchain with two real industrial cases. The results demonstrate the effectiveness of the IFBDAchain in fault classification and KPI prediction tasks in industry. Compared to the average values of local learning, our method increases the classification accuracy by 27.6%, 69.4%, and 33.1% under Independent and identically distributed (IID), non-IID, and unbalanced conditions, respectively. Furthermore, the root-mean-square error of the KPI prediction decreases by 33.3%, 49%, and 45.7% for the IID, non-IID, and unbalanced conditions, respectively, indicating its significant potential as a generic backbone for industrial federated Big data analytics.

关键词： data models Blockchains Servers Analytical models Federated learning Smart contracts Industries Blockchain federated learning industrial Big data analytics industrial intelligent systems (IIS) swarm learning

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