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input-output data based tracking control under DoS attacks

INTERNATIONAL JOURNAL OF CONTROL 2024年第7期97卷 1627-1637页

作者： Yin, Liyuan Xu, Lezhong Zhu, Hongming Zhu, Yupeng Wu, Chengwei Harbin Inst Technol Dept Control Sci & Engn Harbin Peoples R China Northeast Forestry Univ Coll Mech & Elect Engn Harbin Peoples R China Harbin Inst Technol Dept Control Sci & Engn Harbin 150001 Peoples R China

This paper investigates the secure optimal tracking control problem for cyber-physical systems, in which the controller-actuator channel is jammed by denial-of-service attacks. Without leveraging model knowledge, learning-based tracking control algorithms are proposed by utilizing the measured input-output data. The impact of malicious attacks on tracking performance is discussed. More specifically, an augmented system consisted of the system model and the reference model is derived. Then the states of the augmented system are rewritten by utilizing the input, output, and reference trajectory, following which both the Bellman equation and algebraic Riccati equation are given. The conditions of existence and uniqueness of the solution to the algebraic Riccati equation are derived. Furthermore, both policy iteration and value iteration learning-based tracking control algorithms are provided for cyber-physical systems against denial-of-service attacks, and the convergence of the algorithms is also proved. Finally, a DC motor system and a numerical example are given to illustrate the effectiveness of the proposed control algorithms.

关键词： Cyber-physical systems denial-of-service attacks input-output data reinforcement learning control

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input-output data-Based output Antisynchronization Control of Multiagent Systems Using Reinforcement Learning Approach

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2021年第11期17卷 7359-7367页

作者： Peng, Zhinan Zhao, Yiyi Hu, Jiangping Luo, Rui Ghosh, Bijoy Kumar Nguang, Sing Kiong Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China Southwestern Univ Finance & Econ Sch Business Adm Chengdu 611130 Peoples R China Texas Tech Univ Dept Math & Stat Lubbock TX 79409 USA Univ Auckland Dept Elect Comp & Software Engn Auckland 1142 New Zealand

This article investigates an output antisynchronization problem of multiagent systems by using an input-output data-based reinforcement learning approach. Till now, most of the existing results on antisynchronization problems required full-state information and exact system dynamics in the controller design, which is always invalid in practical scenarios. To address this issue, a new system representation is constructed by using just the available input/output data from the multiagent system. Then, a novel value iteration algorithm is proposed to compute the optimal control laws for the agents;moreover, a convergence analysis is presented for the proposed algorithm. In the implementation of the data-based controllers, an actor-critic network structure is established to learn the optimal control laws without the requirement of information of the agent dynamics. An incremental weight updating rule is proposed to improve the learning performance. Finally, simulation results are presented to demonstrate the effectiveness of the proposed antisynchronization control strategy.

关键词： Multi-agent systems Informatics Synchronization Artificial neural networks Optimal control Network topology Heuristic algorithms Incremental actor-critic (AC) network input-output data optimal antisynchronization partially observable multiagent systems reinforcement learning (RL)

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Application of genetic programming to system modeling from input-output data

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 1998年第5期E81A卷 924-930页

作者： Uatrongjit, S Fujii, N Chiang Mai Univ Fac Engn Chiang Mai 50000 Thailand Tokyo Inst Technol Fac Engn Tokyo 1528552 Japan

A new approach for generating a system model from its input-output data is presented. The model is approximated as a linear combination of simple basis functions. The number of basis functions is kept as small as possible to prevent over-fitting and to make the model efficiently computable. Based on these conditions, genetic programming is employed for the generation and selection of the appropriate basis. Since the obtained model can be expressed in simple mathematical expressions, it is suitable for using the model as a macro or behavior model in system level simulation. Experimental results are shown.

关键词： system modeling input-output data behavior model genetic programming

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SYSTEM-IDENTIFICATION OF LOSSLESS LAYERED MEDIA FROM input-output data

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SIGNAL PROCESSING 1991年第3期22卷 269-285页

作者： VANDERWOUDE, JW [a]Faculty of Technical Mathematics Informatics Delft University of Technology P.O. Box 356 2600 AJ Delft The Netherlands

In this paper we model the interior of the earth as a lossless layered medium, and we are concerned with the reconstruction of the parameters of such a medium from the input an output (input-output data). In the first part of the paper we assume that the input-output data is noise free, and we derive a so-called layer stripping algorithm for the reconstruction of the parameters of the medium. The algorithm is just one of the many versions of layer stripping algorithms that are available in the literature. In the second and major part of the paper we assume that both the input and the output are corrupted by noise, and we derive two new methods for the estimation of the parameters of the medium using the noisy input-output data. The methods are based on the estimation of the parameters of an ARX-representation of a lossless layered medium. The distinction between the two methods is that in one method the ARX parameters are free, while in the other method the ARX parameters have to correspond to parameters of a lossless layered medium that are physically realistic. We illustrate the methods by means of a numerical experiment.

关键词： LOSSLESS LAYERED MEDIUM REFLECTION COEFFICIENTS PARAMETER ESTIMATION input-output data ARX-REPRESENTATION

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Inverse Q-Learning Using input-output data

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IEEE TRANSACTIONS ON CYBERNETICS 2024年第2期54卷 728-738页

作者： Lian, Bosen Xue, Wenqian Lewis, Frank L. Davoudi, Ali Auburn Univ Elect & Comp Engn Dept Auburn AL 36849 USA Univ Florida Elect & Comp Engn Dept Gainesville FL 32603 USA Univ Texas Arlington UTA Res Inst Arlington TX 76019 USA

This article addresses the problem of learning the objective function of linear discrete-time systems that use static output-feedback (OPFB) control by designing inverse reinforcement learning (RL) algorithms. Most of the existing inverse RL methods require the availability of states and state-feedback control from the expert or demonstrated system. In contrast, this article considers inverse RL in a more general case where the demonstrated system uses static OPFB control with only input-output measurements available. We first develop a model-based inverse RL algorithm to reconstruct an input-output objective function of a demonstrated discrete-time system using its system dynamics and the OPFB gain. This objective function infers the demonstrations and OPFB gain of the demonstrated system. Then, an input-output Q -function is built for the inverse RL problem upon the state reconstruction technique. Given demonstrated inputs and outputs, a data-driven inverse Q -learning algorithm reconstructs the objective function without the knowledge of the demonstrated system dynamics or the OPFB gain. This algorithm yields unbiased solutions even though exploration noises exist. Convergence properties and the nonunique solution nature of the proposed algorithms are studied. Numerical simulation examples verify the effectiveness of the proposed methods.

关键词： Linear programming Heuristic algorithms System dynamics Optimal control Q-learning Discrete-time systems Trajectory data-driven control input-output data inverse reinforcement learning (RL) static output feedback

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Will using newer input-output data for general equilibrium modeling provide a better estimate for the CO2mitigation cost?

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ECONOMIC SYSTEMS RESEARCH 2021年第2期33卷 157-170页

作者： Hong, Wei-Hong Chai, Hui-Chih Chen, Y-H Henry Reilly, John M. Paltsev, Sergey Inst Nucl Energy Res Ctr Energy Econ & Strategy Res Touyuan Taiwan MIT Joint Program Sci & Policy Global Change Cambridge MA 02139 USA

We provide a critical evaluation about how updating the input-output data of a computable generation equilibrium model can affect policy results, an assessment that is rarely done in existing literature. Specifically, we explore how datasets with different fossil energy cost shares alter results of policy simulations that aim at reducing CO(2)emissions. We prove analytically that a sudden fossil fuel price surge, which provides little time for adjustment through input substitution, can lead to a higher CO(2)mitigation cost. The finding is demonstrated empirically in a full-scale economy-wide model for a base year with lower fossil fuel prices, contrasted with results from a base year when fossil fuel prices spiked. We then propose an adjustment to resolve the issues of using input-output data that embed abrupt fossil fuel price hikes.

关键词： input-output data energy-economic modeling emissions mitigation costs

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A direct PID gains tuning method for DC motor control using an input-output data generated by disturbance response

A direct PID gains tuning method for DC motor control using ...

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IEEE International Conference on Control Applications (CCA)

作者： Masuda, Shiro Tokyo Metropolitan Univ Fac Elect Engn Math & Comp Sci Tokyo 158 Japan

ISBN: (纸本)9781457710636

The disturbance attenuation FRIT method for regulator problems tunes the control parameters so that the disturbance response follows the disturbance reference model output. We have recently proposed such a disturbance attenuation FRIT method using input-output data generated by disturbances. This paper applies the disturbance attenuation FRIT method to a PID control gain tuning for a speed and position control for DC motor. The proposed method focuses on the step-type disturbance, e. g. load change or additive step signal at input signal, which generates an initial one-shot input and output data for PID gains tuning. The efficiency of the proposed method is shown through the experimental results of a speed and position regulation control for DC motor against the step-type disturbances.

关键词： Attenuation DC motor DC motor control DC motors DC motors data models Mathematical model Position control Transfer functions Tuning control system synthesis direct PID gains tuning method disturbance attenuation FRIT method disturbance response input-output data machine control position control position control position regulation control regulator problems speed regulation control step type disturbance three-term control velocity control

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Estimation of international demand bahaviour for use with input-output based data

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Economic Systems Research 2004年第4期16卷 347-366页

作者： Reimer, Jeffrey J. Hertel, Thomas W. Food System Research Group Dept. of Agricultural/Applied Econ. University of Wisconsin Madison WI 53706 427 Lorch Street United States Center for Global Trade Analysis Dept. of Agricultural Economics Purdue University West Lafayette IN United States

The making of projections often requires an economy-wide perspective, and the estimation of consumer demands at the international level. In this paper, an implicit, directly additive demand system (AIDADS) is estimated using cross-country data on consumer expenditures from two different sources: the International Comparison Programme (ICP), and the Global Trade Analysis Project (GTAP). The two data sets are found to produce results that are quite consistent despite their differing origins, and the fact that the former is based on consumer goods that embody wholesale/retail margins, while margin demands are treated separately in GTAP. Given the similarity of the results, the estimation based on GTAP data is favoured for economy-wide projection purposes because it can be readily matched to input-output based production and trade data. An additional benefit of the GTAP-based estimates is that they provide direct evidence concerning how aggregate margin expenditures vary with per capita income. © 2004 The International input-output Association.

关键词： Demand system estimation input-output data Wholesale retail margins

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A NON-INTRUSIVE METHOD TO INFERRING LINEAR PORT-HAMILTONIAN REALIZATIONS USING TIME-DOMAIN data

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ELECTRONIC TRANSACTIONS ON NUMERICAL ANALYSIS 2022年 56卷 102-116页

作者： Cherifi, Karim Goyal, Pawan Benner, Peter Max Planck Inst Dynam Complex Tech Syst Sandtorstr 1 D-39106 Magdeburg Germany Otto von Guericke Univ Fac Math Univ Pl 2 D-39106 Magdeburg Germany

Port-Hamiltonian systems have gained a lot of attention in recent years due to their inherent valuable properties in modeling and control. In this paper, we are interested in constructing linear port-Hamiltonian systems from time-domain input-output data. We discuss a non-intrusive methodology that is comprised of two main ingredients-(a) inferring frequency response data from time-domain data and (b) constructing an underlying portHamiltonian realization using the inferred frequency response data. We illustrate the proposed methodology by means of two numerical examples and also compare it with two other system identification methods to infer the frequency response from the input-output data.

关键词： System identification port-Hamiltonian systems Loewner approach transfer function input-output data

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Recursive coupled projection algorithms for multivariable output-error-like systems with coloured noises

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IET SIGNAL PROCESSING 2020年第7期14卷 455-466页

作者： Pan, Jian Ma, Hao Zhang, Xiao Liu, Qinyao Ding, Feng Chang, Yufang Sheng, Jie Hubei Univ Technol Sch Elect & Elect Engn Hubei Key Lab High Efficiency Utilizat Solar Ener Wuhan 430068 Peoples R China Jiangnan Univ Sch Internet Things Engn Wuxi 214122 Jiangsu Peoples R China Univ Washington Sch Engn & Technol Tacoma WA 98402 USA

By combining the coupling identification concept with the gradient search, this study develops a partially coupled generalised extended projection algorithm and a partially coupled generalised extended stochastic gradient algorithm to estimate the parameters of a multivariable output-error-like system with autoregressive moving average noise from input-output data. The key is to divide the identification model into several submodels based on the hierarchical identification principle and to establish the parameter estimation algorithm by using the coupled relationship between these submodels. The simulation test results indicate that the proposed algorithms are effective.

关键词： identification parameter estimation least squares approximations gradient methods autoregressive moving average processes state-space methods stochastic processes multivariable control systems recursive coupled projection algorithms multivariable output-error-like system coloured noises coupling identification concept gradient search projection algorithm stochastic gradient algorithm autoregressive moving average noise input-output data identification model hierarchical identification principle parameter estimation algorithm coupled relationship

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