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Parallel System Based Quantitative Assessment and Self-evolution for Artificial Intelligence of Active Power Corrective control

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 13-28页

作者： Tianyun Zhang Jun Zhang Feiyue Wang Peidong Xu Tianlu Gao Haoran Zhang Ruiqi Si School of Electrical Engineering and automation Wuhan UniversityWuhan 430072HubeiChina State Key Laboratory for Management and control of Complex System Institute of AutomationChinese Academy of SciencesBeijing 100190China

In artificial intelligence(AI)based-complex power system management and control technology,one of the urgent tasks is to evaluate AI intelligence and invent a way of autonomous intelligence evolution.However,there is,currently,nearly no standard technical framework for objective and quantitative intelligence evaluation.In this article,based on a parallel system framework,a method is established to objectively and quantitatively assess the intelligence level of an AI agent for active power corrective control of modern power systems,by resorting to human intelligence evaluation theories.On this basis,this article puts forward an AI self-evolution method based on intelligence assessment through embedding a quantitative intelligence assessment method into automated reinforcement learning(AutoRL)systems.A parallel system based quantitative assessment and self-evolution(PLASE)system for power grid corrective control AI is thereby constructed,taking Bayesian Optimization as the measure of AI evolution to fulfill autonomous evolution of AI under guidance of their intelligence assessment results.Experiment results exemplified in the power grid corrective control AI agent show the PLASE system can reliably and quantitatively assess the intelligence level of the power grid corrective control agent,and it could promote evolution of the power grid corrective control agent under guidance of intelligence assessment results,effectively,as well as intuitively improving its intelligence level through selfevolution.

关键词： AI quantitative intelligence assessment and self-evolution automated reinforcement learning Bayesian optimization corrective control parallel system

Fixed-Time Anti-Disturbance Average-Tracking for Multi-Agent Systems Without Velocity Measurements

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Journal of Systems Science & Complexity 2024年第3期37卷 985-1002页

作者： LI Yuling LIU Chenglin ZHANG Ya CHEN Yangyang Key Laboratory of Advanced Process control for Light Industry(Ministry of Education) Institute of AutomationJiangnan UniversityWuci 214122China School of automation Southeast UniversityNanjing 210096China

In this paper,the authors tackle a fixed-time average-tracking problem for multi-agent systems which do not require velocity measurements and are affected by external mismatched and matched disturbances.The objective requires the considered agents with second-order dynamics to follow the average value of several time-varying reference signals,each of which is acquired by only one agent.First of all,the average value is estimated by using a novel distributed average estimator in a fixed time.Second,an original observer is created to provide estimated values in a fixed time for external mismatched and matched disturbances as well as unmeasured velocities of agents.Then,the authors propose a novel fixed-time anti-disturbance average-tracking protocol with the aid of the observer and a sliding mode surface.In the end,the feasibility of theoretical analysis is illustrated by some simulation experiments.

关键词： Average-tracking control fixed-time algorithm fixed-time observer mismatched distur-bances multi-agent systems velocity measurements

A model reduction approach for discrete-time linear time-variant systems with delayed inputs

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Science China(Information Sciences) 2024年第4期67卷 197-217页

作者： Ai-Guo WU Guang-Ren DUAN Yu WANG Jie ZHANG School of Mechanical Engineering and automation Harbin Institute of Technology(Shenzhen) HIT Wuhu Robot Technology Research institute Center for control Science and Technology Southern University of Science and Technology

A model reduction approach is presented for discrete-time linear time-variant input-delayed systems. According to this proposed approach, a dynamical variable is constructed by taking advantage of the current state and historical information of input. It is revealed that the behavior of this dynamical variable is governed by a discrete-time linear delay-free system. It is worth noting that the presented variable transformation does not require the system matrix to be invertible. Based on the reduced delay-free models,stabilizing control laws can be easily obtained for the original delayed system. For the case with a single input delay, the constructed variable is an exact prediction for the future state, and thus the stabilizing control law could be designed by replacing the future state with its prediction. Finally, three discrete-time periodic systems with delayed input are employed to illustrate how to utilize the presented model reduction approaches.

关键词： reduction approach predictor-based feedback time-varying systems delayed input periodic system

Aerial target threat assessment based on gated recurrent unit and self-attention mechanism

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Journal of Systems Engineering and Electronics 2024年第2期35卷 361-373页

作者： CHEN Chen QUAN Wei SHAO Zhuang School of automation Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Intelligent control and Decision of Complex Systems Beijing 100081China

Aerial threat assessment is a crucial link in modern air combat, whose result counts a great deal for commanders to make decisions. With the consideration that the existing threat assessment methods have difficulties in dealing with high dimensional time series target data, a threat assessment method based on self-attention mechanism and gated recurrent unit(SAGRU) is proposed. Firstly, a threat feature system including air combat situations and capability features is established. Moreover, a data augmentation process based on fractional Fourier transform(FRFT) is applied to extract more valuable information from time series situation features. Furthermore, aiming to capture key characteristics of battlefield evolution, a bidirectional GRU and SA mechanisms are designed for enhanced features.Subsequently, after the concatenation of the processed air combat situation and capability features, the target threat level will be predicted by fully connected neural layers and the softmax classifier. Finally, in order to validate this model, an air combat dataset generated by a combat simulation system is introduced for model training and testing. The comparison experiments show the proposed model has structural rationality and can perform threat assessment faster and more accurately than the other existing models based on deep learning.

关键词： target threat assessment gated recurrent unit(GRU) self-attention(SA) fractional Fourier transform(FRFT)

A Fractional-Order Ultra-Local Model-Based Adaptive Neural Network Sliding Mode control of n-DOF Upper-Limb Exoskeleton With Input Deadzone

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IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 760-781页

作者： Dingxin He HaoPing Wang Yang Tian Yida Guo the School of automation Nanjing University of Science and TechnologyNanjing 210094China IEEE Norinco Group institute of Navigation and control Technology Beijing 100089China

This paper proposes an adaptive neural network sliding mode control based on fractional-order ultra-local model for n-DOF upper-limb exoskeleton in presence of uncertainties,external disturbances and input deadzone.Considering the model complexity and input deadzone,a fractional-order ultra-local model is proposed to formulate the original dynamic system for simple controller design.Firstly,the control gain of ultra-local model is considered as a constant.The fractional-order sliding mode technique is designed to stabilize the closed-loop system,while fractional-order time-delay estimation is combined with neural network to estimate the lumped disturbance.Correspondingly,a fractional-order ultra-local model-based neural network sliding mode controller(FO-NNSMC) is proposed.Secondly,to avoid disadvantageous effect of improper gain selection on the control performance,the control gain of ultra-local model is considered as an unknown parameter.Then,the Nussbaum technique is introduced into the FO-NNSMC to deal with the stability problem with unknown gain.Correspondingly,a fractional-order ultra-local model-based adaptive neural network sliding mode controller(FO-ANNSMC) is proposed.Moreover,the stability analysis of the closed-loop system with the proposed method is presented by using the Lyapunov theory.Finally,with the co-simulations on virtual prototype of 7-DOF iReHave upper-limb exoskeleton and experiments on 2-DOF upper-limb exoskeleton,the obtained compared results illustrate the effectiveness and superiority of the proposed method.

关键词： Adaptive control input deadzone model-free control n-DOF upper-limb exoskeleton neural network

Differential Game Model of Resource Extraction with Continuous and Dynamic Updating

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Journal of the Operations Research Society of China 2024年第1期12卷 51-75页

作者： Ovanes Petrosian Tihomirov Denis Jiang-Jing Zhou Hong-Wei Gao School of Mathematics Harbin Institute of TechnologyHarbin150001HeilongjiangChina School of automation Qingdao UniversityQingdao266071ShandongChina Faculty of Applied Mathematics and control processes Saint-Petersburg State UniversitySaint PetersburgRussia National Research University"High School of Economics" Saint-PetersburgRussia School of Mathematics and Statistics Qingdao UniversityQingdao266071ShandongChina

This paper is devoted to a new class of differential games with continuous and dynamic updating.The direct application of resource extraction in a case of dynamic and continuous updating is considered.It is proved that the optimal control(cooperative strategies)and feedback Nash equilibrium strategies uniformly converge to the corresponding strategies in the game model with continuous updating as the number of updating instants converges to infinity.Similar results are presented for an optimal trajectory(cooperative trajectory),equilibrium trajectory and corresponding payoffs.

关键词： Differential games Dynamic updating Continuous updating Optimal control Feedback Nash equilibrium

Attitude-adjusting dynamical behavior of cubic rover on low-gravity testbed

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Astrodynamics 2024年第1期8卷 149-159页

作者： Bowen Jiang Muhammad Talha Hussain Xiangyuan Zeng School of automation Beijing Institute of TechnologyBeijing100081China

Cubic rovers that traverse by hopping systems are promising in low-gravity environments.Although several analyses of the control methods and mobility of the cubic rover are available,investigations of its attitude-adjusting behavior are still limited.This study derives the dynamic equations of the two attitude-adjusting modes of the cubic rover,referred to as walking and twisting.The relationships between the speed threshold and rotation angle of the cubic rover were investigated in both rigid and regolith environments using a self-designed low-gravity testbed.Comparative studies were conducted by considering the experimental and simulated outputs.The results of this study can be interesting for roving mission planning when exploring planetary moons and small celestial bodies.

关键词： low-gravitytestbed attitude-adjusting motion cubic rover experimental study

Preliminary study of the electrospray DPE peculiarities from the liquid surface in the presence of the CSWs

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Plasma Science and Technology 2024年第5期26卷 110-120页

作者：贺伟国霍卫杰韩罗峰朱康武王锋王泽坤 School of Mechatronic Engineering and automation Shanghai UniversityShanghai 200444People’s Republic of China Shanghai Aerospace Electronic Technology institute Shanghai 201109People’s Republic of China Shanghai Aerospace control Technology institute Shanghai 201109People’s Republic of China

The Ultrasonic Electric Propulsion(UEP)system is a cutting-edge propulsion technology that is mostly used on platforms for small satellites(less than 10 kg).The characteristics of droplet partial emissions(DPEs)in the UEP system are investigated using a high-speed imaging technique(an ultra-high speed camera(NAC HX-6)and a long-distance microscope)in this work.The experiments demonstrate that there are a few partial emission modes,including left-side emission,double-side emission,and right-side emission,that are present in the droplet emission process of the UEP system.These modes are primarily caused by the partial formation of capillary standing waves(CSWs)on the emission surface of the ultrasonic nozzle.The emission rate for single-and double-sided emissions varies at different times,indicating that there are different CSWs engaged in droplet emission due to variations in the liquid film thickness and charge state of the liquid cones.Additionally,as the droplets emit continuously,a raised area on the emission surface appears,with several droplets emitting there as a result of charge accumulation.Additionally,photos of the CSWs with emitting droplets are obtained,which highlights the CSWs'distinctive wave morphology.

关键词： ultrasonic electric propulsion system microsatellite high-speed imaging technique droplet partial emissions electric field

Deep Reinforcement Learning or Lyapunov Analysis?A Preliminary Comparative Study on Event-Triggered Optimal control

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1702-1704页

作者： Jingwei Lu Lefei Li Qinglai Wei Fei-Yue Wang IEEE the Department of Industrial Engineering Tsinghua UniversityBeijing 100084China The State Key Laboratory for Management and control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Dear Editor,This letter develops a novel method to implement event-triggered optimal control(ETOC) for discrete-time nonlinear systems using parallel control and deep reinforcement learning(DRL), referred to as Deep-ETOC. The developed Deep-ETOC method introduces the communication cost into the performance index through parallel control, so that the developed method enables control systems to learn ETOC policies directly without triggering conditions.

关键词： Deep letter enable