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A novel strategy to solve communication constraints for formation control of multi-AUVs

Science China(Information Sciences) 2021年第7期64卷 253-255页

作者： Zhenyu GAO Ge GUO Department of Automation Dalian Maritime University School of Control Engineering Northeastern University Qinhuangdao Key Laboratory of Synthetical Automation for Process Industries Northeastern University

Dear editor,Interest in terms of research on formation control of multiple autonomous underwater vehicles (multi-AUVs) has gained considerable attention in recent years [1–5], owing to its extensive applications in d... 详细信息

关键词： Information Systems and Communication Service

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Consensus Robustness under PI Protocols with Undirected Graphs

Consensus Robustness under PI Protocols with Undirected Grap...

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American control Conference (ACC)

作者： Qi Mao Dan Ma Yanling Ding Hui Peng Jie Chen Department of Electrical Engineering City University of Hong Kong Hong Kong China College of Information Science and Engineering State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China School of Automation Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control Guangdong University of Technology Guangzhou China

This paper concerns robust consensus problems for continuous-time first-order multi-agent systems with respect to uncertain gain and phase of the agent’s frequency response varying within certain ranges. We consider dynamic output feedback control protocol in the form of proportional-integral (PI) control. The agents can in general be nonminimum phase and unstable systems, which are interconnected by an undirected graph network. We seek to determine the largest ranges of gain and phase variations, referred to as the gain consensus margin (GCM) and the phase consensus margin (PCM), respectively, so that consensus can be achieved robustly within these ranges. Our main results consist of explicit analytical expressions of the GCM and PCM, which demonstrate how the agent’s unstable pole and nonminimum phase zero, as well as the network connectivity may fundamentally confine the gain and phase variation ranges so that consensus can or cannot be maintained.

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Novel Cooperative Particle Swarm Optimization with Interactive Learning Strategy 1

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8th International Conference on Life System Modeling and Simulation, LSMS 2024 and 8th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Wang, Qiliang Liu, Yiheng Xu, Hao Zhang, Pinggai Wu, Qinlin Wei, Lisheng Anhui Key Laboratory of Electric Drive and Control School of Electrical Engineering Anhui Polytechnic University Anhui Wuhu241000 China Industrial Process Control Optimization and Automation Engineering Research Center School of Electronic Engineering Chaohu University Anhui Chaohu238024 China School of Computer Science and Artificial Intelligence Chaohu University Anhui Chaohu238024 China

ISBN: (数字)9789819603138

ISBN: (纸本)9789819603121

Particle Swarm Optimization (PSO) is an optimization method grounded in swarm intelligence, designed to mimic the social behaviors of bird flocks during hunting. By sharing information about their positions, the entire swarm can explore and search diverse areas within a given space. This method features rapid convergence, minimal parameters, and straightforward implementation, making it a potent and efficient metaheuristic technique. Nevertheless, conventional PSO algorithms, despite their simplicity and efficiency, often become trapped in local optima when tackling complex multimodal problems, which limits their global search capability. To mitigate these limitations, a new Cooperative Particle Swarm Optimization algorithm with Interactive Learning (CPSOIL) is introduced. This approach incorporates an interactive learning strategy, allowing particles to learn from their neighbors by perceiving the surrounding particles during updates. Moreover, a mechanism has been added to modify the particle velocity update when the algorithm encounters local optima, thereby preserving population diversity, enhancing the likelihood of escaping local optima, and improving algorithm accuracy. Comparisons with other leading algorithms on the CEC2005 benchmark functions indicate that, in most cases, the enhanced algorithm outperforms its counterparts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Particle swarm optimization (PSO)

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A Norm Optimal Iterative Learning control Approach With Efficient Computation

A Norm Optimal Iterative Learning Control Approach With Effi...

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Data Driven control and Learning Systems (DDCLS)

作者： Luyuan Gao Zhihe Zhuang Hongfeng Tao Yiyang Chen Wojciech Paszke Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University Wuxi PR China School of Mechanical and Electrical Engineering Soochow University Suzhou PR China Institute of Automation University of Zielona Gora Zielona Gora Poland

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Iterative learning control (ILC) is excellently applied to output tracking for systems that perform repetitive tasks. Classical norm optimal ILC (NOILC) approach yields optimal control inputs by minimizing a quadratic cost function. However, this approach requires the introduction of supervectors, i.e., lifting technique, for system description, which raises a high computational load problem. The contribution of this paper is to propose a non-lifted NOILC (N-NOILC) approach by redesigning a cost function that does not involve the lifting technique, achieving the goal of efficient computation. Therefore, the proposed approach is particularly suitable for large tracking tasks with long trial duration and high sampling frequency. Moreover, considering the model uncertainty in practice, robust convergence is analyzed. Numerical simulations are shown to demonstrate the properties and advantages of the proposed approach.

关键词： Uncertain systems Uncertainty Optimal control Cost function Numerical simulation Computational efficiency Numerical models

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Demand Response Optimization for the Enhancement of the Distribution System's Operation

Demand Response Optimization for the Enhancement of the Dist...

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International Conference on Smart Energy Systems and Technologies (SEST)

作者： Maria Fotopoulou George Tsekouras Dimitrios Rakopoulos Vassiliki Kontargyri Department of Electrical and Electronics Engineering University of West Attica Athens Greece Chemical Process and Energy Resources Institute Centre for Research and Technology Hellas Athens Greece Control Systems and Industrial Automation Laboratory National and Kapodistrian University of Athens Athens Greece

ISBN: (数字)9798350386493

ISBN: (纸本)9798350386509

Following the latest energy market trends, active distribution systems incorporate an ascending mix of flexible loads, which can participate in Demand Response (DR) strategies. The purpose of this research paper is to present a DR strategy for the Distribution System Operator (DSO) in order to enhance the system's operation and mitigate power quality issues, such as voltage drops, and congestion. The proposed algorithm aims at smoothing the load curve, taking into consideration the available flexible load and the limitations of the system. The algorithm is demonstrated on a model of the distribution system of Skiathos island, Greece, in the context of ENFLATE, a Horizon Europe project, and future scenarios with photovoltaic (PV) penetration are studied as well. According to the results, the proposed DR algorithm manages to reduce the voltage drop, eliminate the number of events where the voltage drop exceeds the limits and reduce the losses of the distribution system up to 8%-9%, depending on the PV penetration.

关键词： Electric potential Smoothing methods Power quality Europe Voltage Artificial neural networks Transformers Demand response Load modeling Context modeling

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Active Fault Diagnosis Method Based on Deep Forest and Its Industrial Application

Active Fault Diagnosis Method Based on Deep Forest and Its I...

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Artificial Intelligence Technology (ACAIT), Asian Conference on

作者： Dong Li Kaixun He Lili Tao Hanlong Liu College of Electrical and Automation Engineering Shandong University of Science and Technology Qingdao China Key Laboratory of Smart Manufacturing in Energy Chemical Process of Ministry of Education East China University of Science and Technology China College of Intelligent Manufacturing and Control Engineering Shanghai Second University of Technology China

ISBN: (数字)9798331517090

ISBN: (纸本)9798331517106

Wind turbine equipment, as a key component in the process production control of thermal power plants, directly affects the power generation efficiency of the plant. Due to the non-linear and non-stationary nature of the signals acquired from wind turbine sensors, this paper presents a novel active fault diagnosis approach for wind turbine systems based on deep forest techniques. Initially, multi-dimensional time-domain features are extracted from the raw fault data. Simultaneously, important features are selected from multiple angles by integrating Pearson correlation coefficient and Borutashap algorithm, following the principles of active fault diagnosis. Additionally, the whale optimization algorithm (WOA) is utilised to search the optimal sinusoidal auxiliary signal parameter, thereby highlighting specific fault manifestations and improving the model’s diagnostic capability. The deep forest, with its strong classification ability and noise resistance, overcomes the overfitting issue in shallow machine learning algorithms and the complex parameter tuning in deep learning. Then, the designed sinusoidal auxiliary signal is used as an additional input to the deep forest model, enabling the final fault classification outcomes. Finally, experiments are conducted using a wind turbine power transmission system fault simulator. The experimental results demonstrate an improvement in fault diagnosis accuracy, confirming the effectiveness of the proposed method.

关键词： Fault diagnosis Deep learning Machine learning algorithms Feature extraction Whale optimization algorithms Regression analysis Wind turbines Random forests Tuning Power generation

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Extended NNs-Based Adaptive Event-Triggered Asymptotic control for Nonlinear Systems Against Actuator Faults

Extended NNs-Based Adaptive Event-Triggered Asymptotic Contr...

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International Symposium on Autonomous Systems (ISAS)

作者： Shanlin Liu Yingwei Zhang Xudong Zhao Hesheng Wang College of Information Science and Engineering Northeastern University Shenyang China State Laboratory of Synthesis Automation of Process Industry Northeastern University Shenyang China Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of IntelligentControl and Management Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350363173

ISBN: (纸本)9798350363180

In this article, an adaptive event-triggered asymp¬totically fault-tolerant control (FTC) issue for nonlinear systems with actuator faults is investigated. An extended neural networks (NNs) technique is introduced to estimate unknown functions of the system under consideration. Compared with traditional NNs that ignore approximation errors, the proposed extended NNs further processes approximation errors, reducing the conservatism of the proposed algorithm. Then, within the framework of backstepping method, a new adaptive control strategy is developed by combining event-triggered mechanism (ETM) and bound estimation (BE) approach, which ensures the asymptotic convergence of system states while also reducing the transmission of unnecessary information, thus improving the utilization of communication resources. At the same time, the designed controller eliminates the adverse effects of actuator failures and system nonlinear terms. Finally, a simulation example of a single-link robot system is provided to demonstrate that the developed control strategy can ensure that all signals are bounded and system states are asymptotically convergent.

关键词： Actuators Backstepping Fault tolerant systems process control Artificial neural networks control systems Approximation error

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Passivity-Based Online Reinforcement Learning for Real Time Model-Free Overhead Crane System control

Passivity-Based Online Reinforcement Learning for Real Time ...

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Chinese control and Decision Conference, CCDC

作者： Haoran Zhang Chunhui Zhao Jinliang Ding State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

ISBN: (纸本)9781665478977

In this paper, a novel model-free online Reinforcement Learning (RL) control method is proposed for the real-time overhead crane control problem. The crane control problem is first formulated as an optimal regulation problem with a user-specified objective function. Two neural-networks, namely Actor-Critic networks, are employed to approximate the objective function and the optimal control policy respectively. Then, an improved network updating rule with an additional passivity-based stabilization term is developed to remove the requirement of the initial stabilizing control policy. Unlike other crane control approaches, the proposed online RL algorithm does not rely on prior knowledge of the overhead crane mathematical model. Finally, simulation studies are carried out to demonstrate the effectiveness of the proposed method in the presence of system parameter variations when compared to LQR control.

关键词： Adaptation models Cranes System dynamics Reinforcement learning Linear programming Mathematical models Real-time systems

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Distributed Optimization by Network Flows with Spatio-Temporal Compression

arXiv

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arXiv 2024年

作者： Ren, Zihao Wang, Lei Yi, Xinlei Wang, Xi Yuan, Deming Yang, Tao Wu, Zhengguang Shi, Guodong State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China College of Electronics and Information Engineering Tongji University China School of Electrical Engineering Telecommunications University of New South Wales Sydney Australia School of Automation Nanjing University of Science and Technology China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University China Australian Centre for Robotics School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney SydneyNSW2006 Australia

Several data compressors have been proposed in distributed optimization frameworks of network systems to reduce communication overhead in large-scale applications. In this paper, we demonstrate that effective information compression may occur over time or space during sequences of node communications in distributed algorithms, leading to the concept of spatio-temporal compressors. This abstraction classifies existing compressors as spatio-temporal compressors, with their effectiveness described by constructive stability criteria from nonlinear system theory. Subsequently, we apply these spatio-temporal compressors to standard continuous-time consensus flows and distributed prime-dual flows, establishing conditions ensuring convergence. Additionally, we introduce a novel observer-based distributed primal-dual continuous flow integrated with spatio-temporal compressors, which provides broader convergence conditions. These continuous flows achieve exponential convergence to the global optimum when the objective function is strongly convex and can be discretized using Euler approximations. Finally, numerical simulations illustrate the versatility of the proposed spatio-temporal compressors and verify the convergence of algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Stability criteria

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Adaptive Pseudo Inverse control for a Class of Nonlinear Asymmetric and Saturated Nonlinear Hysteretic Systems

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IEEE/CAA Journal of Automatica Sinica 2021年第4期8卷 916-928页

作者： Xiuyu Zhang Ruijing Jing Zhiwei Li Zhi Li Xinkai Chen Chun-Yi Su School of Automation Engineering Northeast Electric Power Universityand also with Jilin Province International Research Center of Precision Drive and Intelligent ControlJilin 132012China State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China Department of Electronic and Information Systems Shibaura Institute of TechnologySaitama 337-8570Japan Department of Mechanical and Industrial Engineering Concordia UniversityQCMontreal H3B 1R6Canada

This paper aims at eliminating the asymmetric and saturated hysteresis nonlinearities by designing hysteresis pseudo inverse compensator and robust adaptive dynamic surface control(DSC)***"pseudo inverse"means that an on-line calculation mechanism of approximate control signal is developed by applying a searching method to the designed temporary control signal where the true control signal is *** main contributions are summarized as:1)to our best knowledge,it is the first time to compensate the asymmetric and saturated hysteresis by using hysteresis pseudo inverse compensator because the construction of the true saturated-type hysteresis inverse model is very difficult;2)by designing the saturated-type hysteresis pseudo inverse compensator,the construction of true explicit hysteresis inverse and the identifications of its corresponding unknown parameters are not required when dealing with the saturated-type hysteresis;3)by combining DSC technique with the tracking error transformed function,the"explosion of complexity"problem in backstepping method is overcome and the prespecified tracking performance is *** of stability and experimental results on the hardware-inloop platform illustrate the effectiveness of the proposed adaptive pseudo inverse control scheme.

关键词： Adaptive dynamic surface control adaptive pseudo inverse control asymmetric and saturated hysteresis robust control

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