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Control Strategies for Digital Twin Systems

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 170-180页

作者： Guo-Ping Liu IEEE the Center for Control science and technology Southern University of Science and TechnologyShenzhen 518055China

With the continuous breakthrough in information technology and its integration into practical applications, industrial digital twins are expected to accelerate their development in the near future. This paper studies various control strategies for digital twin systems from the viewpoint of practical applications.To make full use of advantages of digital twins for control systems, an architecture of digital twin control systems, adaptive model tracking scheme, performance prediction scheme, performance retention scheme, and fault tolerant control scheme are proposed. Those schemes are detailed to deal with different issues on model tracking, performance prediction, performance retention, and fault tolerant control of digital twin systems. Also, the stability of digital twin control systems is analysed. The proposed schemes for digital twin control systems are illustrated by examples.

关键词： Digital twin control systems fault tolerant control model tracking performance prediction performance retention

Reducing idleness in financial cloud services via multi-objective evolutionary reinforcement learning based load balancer

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science China(Information sciences) 2024年第2期67卷 20-40页

作者： Peng YANG Laoming ZHANG Haifeng LIU Guiying LI Department of Statistics and Data science Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and EngineeringSouthern University of Science and Technology Academy for Advanced Interdisciplinary Studies Southern University of Science and Technology Guangdong OPPO Mobile Telecommunications Corp. Ltd Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology

In recent years, various companies have started to shift their data services from traditional data centers to the cloud. One of the major motivations is to save on operational costs with the aid of cloud elasticity. This paper discusses an emerging need from financial services to reduce the incidence of idle servers retaining very few user connections, without disconnecting them from the server side. This paper considers this need as a bi-objective online load balancing problem. A neural network based scalable policy is designed to route user requests to varied numbers of servers for the required elasticity. An evolutionary multi-objective training framework is proposed to optimize the weights of the policy. Not only is the new objective of idleness reduced by over 130% more than traditional industrial solutions, but the original load balancing objective itself is also slightly improved. Extensive simulations with both synthetic and real-world data help reveal the detailed applicability of the proposed method to the emergent problem of reducing idleness in financial services.

关键词： evolutionary reinforcement learning evolutionary multi-objective optimization load balance cloud computing

Tracking Control of Multi-Agent Systems Using a Networked Predictive PID Tracking Scheme

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IEEE/CAA Journal of Automatica Sinica 2023年第1期10卷 216-225页

作者： Guo-Ping Liu IEEE the Center for Control science and technology Southern University of Science and TechnologyShenzhen 518055China

With the rapid development of network technology and control technology,a networked multi-agent control system is a key direction of modern industrial control systems,such as industrial Internet systems.This paper studies the tracking control problem of networked multi-agent systems with communication constraints,where each agent has no information on the dynamics of other agents except their outputs.A networked predictive proportional integral derivative(PPID)tracking scheme is proposed to achieve the desired tracking performance,compensate actively for communication delays,and simplify implementation in a distributed manner.This scheme combines the past,present and predictive information of neighbour agents to form a tracking error signal for each agent,and applies the proportional,integral,and derivative of the agent tracking error signal to control each individual agent.The criteria of the stability and output tracking consensus of multi-agent systems with the networked PPID tracking scheme are derived through detailed analysis on the closed-loop systems.The effectiveness of the networked PPID tracking scheme is illustrated via an example.

关键词： Coordinative tracking control networked multiagent systems PID control predictive control

Ionospheric disturbance analysis of the January 15,2022 Tonga eruption based on GPS data

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science China Earth sciences 2023年第8期66卷 1798-1813页

作者： Jiafeng LI Kejie CHEN Haishan CHAI Jian LIN Zhiyuan ZHOU Hai ZHU Mingzhe LYU Department of Earth and Space sciences Southern University of Science and TechnologyShenzhen 518055China Department of Ocean science and Engineering Southern University of Science and TechnologyShenzhen 518055China

Hunga Tonga-Hunga Ha’apai climactic eruption on January 15,2022,released enormous energy that affected the ionosphere over the Pacific Rim.We analyzed ionospheric disturbance following volcanic eruptions using near-field(<1000 km),regional(1000–5000 km),and far-field(5000–12000 km) global positioning system(GPS) observations.The results indicate that the near-field ionospheric perturbation that occurred 8–15 min after the cataclysmic eruption was mainly derived from the shock wave(~1000 m/s) generated by the blast,while the low-frequency branch with long-distance propagation characteristics over the regional and the far-field was mainly associated with atmospheric Lamb waves(~330 m/s).Moreover,the amplitude of disturbance and background total electron content(TEC) are related proportionally.The intensity of the volcanic eruption and the background ionospheric conditions determine the magnitude of ionospheric responses.TEC perturbations were invisible on the reference days.Furthermore,the source location and onset time were calculated using the ray tracing technique,which confirms that the Tonga event triggered the ionospheric anomaly beyond the crater.Finally,the change in the frequency of the perturbations coincided with the arrival of the initial tsunami,implying the generation of a meteotsunami.

关键词： GPS Tonga volcano Traveling ionospheric disturbances Raytracing Meteotsunami

A FAS approach for stabilization of generalized chained forms: part 1. Discontinuous control laws

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science China(Information sciences) 2024年第2期67卷 118-143页

作者： Guang-Ren DUAN Center for Control Theory and Guidance technology Harbin Institute of Technology Center for Control science and technology Southern University of Science and Technology

In this paper, a type of general nonholonomic systems is proposed, which contains both the Brockett’s two example systems, and their extended n-dimensional chained forms, as special cases. For the stabilization of such systems, a stabilizing controller is proposed based on the fully actuated system(FAS)approach, which is discontinuous at the origin but time-invariant when the open-loop system is time-invariant,and drives the feasible trajectories of the system to the origin exponentially. Furthermore, the proposed FAS approach is also extended to the sub-normal system case and the time-delay system case.

关键词： nonholonomic systems feedback stabilization exponential convergence sub-fully actuated systems time-delay systems

Non-affinity:The emergence of networks from amorphous planar graphs

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science China(Physics,Mechanics & Astronomy) 2023年第6期66卷 118-138页

作者： Xiangying Shen Guimei Zhu Baowen Li Department of Materials science and Engineering Southern University of Science and TechnologyShenzhen 51855China School of Microelectronics Southern University of Science and TechnologyShenzhen 518055China Department of Physics Southern University of Science and TechnologyShenzhen 518055China International Quantum Academy Shenzhen 518048China

The primary research of physics is to reveal the underlying law of the physical world.However,for a complex system consisting of multiple components,even if we know every details of each component,we still cannot predict the collective behavior due to the emergence phenomenon.The amorphous networks of mass points hinged by springs belong to such a complex system.Owing to the non-affinity,one of the emergence phenomena of the network,the displacements field of the internal mass points under the external load tends to be chaotic,and there is no well-established theoretical framework to describe these points’collective behavior analytically.The non-affine mechanical responses of the amorphous networks are very common,whereas the affine response is rare and it occurs only in those lattices with one site per unit cell.The network’s non-affinity prevents us from further investigating the relationship between its intrinsic properties(such as the contact number,local structure,and topological characteristics)and the mechanical behaviors.As a result,it is very complicated and difficult to predict the responses of an amorphous network to an imposed strain.Interestingly,a sort of amorphous network derived from the jammed particles is reported to have almost perfect affine mechanical behavior,in a stark contrast with the general perception.These findings may shed light on uncovering the structural factors that affect the network’s affinity.This article will give a short review of the latest advances in this area.

关键词： complex systems mechanical properties amorphous solids structure

Privacy-Preserving Consensus-Based Distributed Economic Dispatch of Smart Grids via State Decomposition

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1250-1261页

作者： Wei Chen Guo-Ping Liu The authors are with the Center for Control science and technology Southern University of Science and TechnologyShenzhen 518055China

This paper studies the privacy-preserving distributed economic dispatch(DED)problem of smart grids.An autonomous consensus-based algorithm is developed via local data exchange with neighboring nodes,which covers both the islanded mode and the grid-connected mode of smart grids.To prevent power-sensitive information from being disclosed,a privacy-preserving mechanism is integrated into the proposed DED algorithm by randomly decomposing the state into two parts,where only partial data is transmitted.Our objective is to develop a privacy-preserving DED algorithm to achieve optimal power dispatch with the lowest generation cost under physical constraints while preventing sensitive information from being eavesdropped.To this end,a comprehensive analysis framework is established to ensure that the proposed algorithm can converge to the optimal solution of the concerned optimization problem by means of the consensus theory and the eigenvalue perturbation approach.In particular,the proposed autonomous algorithm can achieve a smooth transition between the islanded mode and the grid-connected mode.Furthermore,rigorous analysis is given to show privacy-preserving performance against internal and external eavesdroppers.Finally,case studies illustrate the feasibility and validity of the developed algorithm.

关键词： preserving autonomous mode

Bayesian Deep Learning for Dynamic Power System State Prediction Considering Renewable Energy Uncertainty

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Journal of Modern Power Systems and Clean Energy 2022年第4期10卷 913-922页

作者： Shiyao Zhang James J.Q.Yu Academy for Advanced Interdisciplinary Studies Southern University of Science and TechnologyShenzhen 518055China Department of Computer science and Engineering Southern University of Science and TechnologyShenzhen 518055China

Modern power systems are incorporated with distributed energy sources to be environmental-friendly and costeffective.However,due to the uncertainties of the system integrated with renewable energy sources,effective strategies need to be adopted to stabilize the entire power systems.Hence,the system operators need accurate prediction tools to forecast the dynamic system states effectively.In this paper,we propose a Bayesian deep learning approach to predict the dynamic system state in a general power system.First,the input system dataset with multiple system features requires the data pre-processing stage.Second,we obtain the dynamic state matrix of a general power system through the Newton-Raphson power flow model.Third,by incorporating the state matrix with the system features,we propose a Bayesian long short-term memory(BLSTM)network to predict the dynamic system state variables accurately.Simulation results show that the accurate prediction can be achieved at different scales of power systems through the proposed Bayesian deep learning approach.

关键词： Bayesian deep learning data analytics NewtonRaphson power flow renewable energy source system state

Predictive sliding-mode control of networked high-order fully actuated systems under random deception attacks

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science China(Information sciences) 2023年第9期66卷 50-63页

作者： Da-Wei ZHANG Guo-Ping LIU Center for Control Theory and Guidance technology Harbin Institute of Technology Center for Control science and technology Southern University of Science and Technology

This research addresses the output tracking of networked high-order fully actuated(NHOFA)systems under random deception attacks in the sensor to networked controller(SNC) and networked controller to actuator(NCA) communication channels, where a Bernoulli process represents the launching success rate of random deception attacks. A successful attack involves the output and control signals being tampered with by the injection of false data. Herein, a predictive sliding-mode control scheme is proposed to achieve the security tracking. In this scheme, a sliding-mode variable is introduced to defend the random deception attacks by enhancing the robustness of closed-loop systems. An incremental HOFA(IHOFA) prediction model is developed using a Diophantine equation as an alternative to the traditional reduced-order prediction model. Through this IHOFA prediction model, the multistep ahead predictions of the sliding-mode variable are constructed to optimize the tracking performance and defense of random deception attacks. By utilizing the Lyapunov function and linear matrix inequality(LMI) approach, a sufficient and necessary condition is proposed to ensure stability and high tracking performance of closed-loop NHOFA systems. Herein, an experiment on the tracking control of an air-bearing spacecraft(ABS) simulator is performed to demonstrate the effectiveness and practicability of the predictive sliding-mode control scheme.

关键词： NHOFA systems random deception attacks predictive sliding-mode control stability and tracking performance tracking control of ABS simulator