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Model-Free Adaptive Control for Nonlinear Multi-Agent Systems Under Constrained Bit Rate: An encoding-decoding mechanism

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2025年

作者： Zheng, Shanshan Liu, Shuai Wang, Licheng Univ Shanghai Sci & Technol Coll Sci Shanghai Peoples R China Shanghai Univ Elect Power Coll Automat Engn Shanghai Peoples R China

In this article, the model-free-based consensus tracking control problem is investigated for nonlinear discrete-time multi-agent systems (MASs) subject to constrained bit rate. A dynamic linearization technology is employed, by which the original unknown nonlinear system is equivalently transformed into a dynamic linearized model. An encoding-decoding mechanism (EDM) is applied to encode the measurement outputs into the binary codewords with fewer occupations of the network bandwidth. On this basis, a distributed model-free adaptive control (MFAC) scheme is developed, while sufficient conditions are presented to ensure that the close-loop MAS achieves the expected consensus performance. The proposed scheme is completely data-driven without relying on any information from the system model or structure. Meanwhile, the inherent relationship between bit rate constraints and decoding accuracy is revealed. Finally, simulation results are presented to demonstrate the validity of the provided approach.

关键词： consensus constrained bit rate encoding-decoding mechanism model-free adaptive control multi-agent systems recoverability

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Outlier-resistant state estimation for complex networks with random false data injection attacks under encoding-decoding mechanism

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INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING 2024年第8期38卷 2833-2854页

作者： Liu, Yufeng Hu, Jun Jia, Chaoqing Chen, Cai Chi, Kun Harbin Univ Sci & Technol Sch Automat Harbin 150080 Peoples R China Harbin Univ Sci & Technol Heilongjiang Prov Key Lab Optimizat Control & Inte Harbin Peoples R China Harbin Univ Sci & Technol Dept Appl Math Harbin 150080 Peoples R China

This article focuses on the outlier-resistant state estimation problem for discrete time-varying complex networks (TVCNs) affected by random false data injection attacks (FDIAs) under an encoding-decoding mechanism (EDM). From the perspective of information security, a uniform-quantization-based EDM is employed to encrypt the transmitted data. During the data transmission process, a set of independent random variables governed by Bernoulli distribution is introduced to characterize the occurrence of random FDIAs. For the purpose of alleviating the passive impact of potential measurement outliers, a saturation structure is adopted during the estimator design. The gain matrix is given by minimizing the upper bound of estimation error covariance. According to the stochastic analysis method, it is shown that the state estimation error is bounded exponentially in mean-square sense by providing new sufficient condition. It should be noted that we make the first attempt to develop new outlier-resistant state estimation method with performance evolution criterion in the time-varying perspective for TVCNs with random FDIAs under EDM. Finally, a simulation example with comparative experiment is presented to illustrate the effectiveness of the newly presented outlier-resistant estimation algorithm.

关键词： complex networks encoding-decoding mechanism exponential boundedness in mean-square sense random false data injection attacks state estimation

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Finite-Time Fault Detection for T-S Fuzzy Systems Under encoding-decoding mechanism 3

Finite-Time Fault Detection for T-S Fuzzy Systems Under Enco...

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3rd Conference on Fully Actuated System Theory and Applications (FASTA)

作者： Ma, Siteng Hu, Jun Wu, Zhihui Yu, Hui Yu, Tianze Harbin Univ Sci & Technol Dept Appl Math Harbin 150080 Peoples R China Harbin Univ Sci & Technol Sch Automat Harbin 150080 Peoples R China

ISBN: (纸本)9798350373707;9798350373691

This paper is concerned with the problem of finite-time fault detection (FD) for T-S fuzzy systems under the encoding-decoding mechanism (EDM). In order to relieve the communication burden and enhance the transmission reliability, the EDM is introduced in the sensor-to-filter channel. The sufficient conditions are derived to guarantee the finite-time boundedness (FTB) with prescribed H-infinity performance of the filtering error system (FES). Moreover, the parameters of the FD filter are determined by means of the linear matrix inequality method. Finally, the feasibility of the developed FD filtering algorithm is illustrated by a numerical simulation with comparative experiments.

关键词： encoding-decoding mechanism Finite-Time Fault Detection T-S Fuzzy Systems

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Data-driven iterative learning control using a uniform quantizer with an encoding-decoding mechanism

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2022年第7期32卷 4336-4354页

作者： Zhang, Huimin Chi, Ronghu Hou, Zhongsheng Huang, Biao Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 266061 Peoples R China Qingdao Univ Sch Automat Qingdao Peoples R China Univ Alberta Dept Chem & Mat Engn Edmonton AB Canada

This work explores the problem of uniform quantization of iterative learning control (ILC) for nonlinear nonaffine systems under a data-driven design and analysis framework. First, to deal with the strong nonlinearity and nonaffine structure of the systems, an iterative linear data model (iLDM) utilizing more additional parameter information is developed consequently bypassing modeling process. The iLDM only serves for the controller design and analysis without any mechanistic interpretation. Then, an encoding-decoding mechanism (E-DM) is employed to deal with the bounded tracking performance caused by the uniform quantizer. Using the iLDM, an E-DM based quantized data-driven ILC (E-D QDDILC) method is developed with a quantized learning control law and a quantized parameter estimation law, both of which only utilize the quantized output estimations obtained from the E-DM. The quantized parameter estimation law enhances the robustness of the proposed E-D QDDILC as an adaptive mechanism to tune the learning gain in real-time. A mathematical induction approach and the contraction mapping principle are introduced for the convergence analysis as the basic tools. When the scaling function is bounded, one shows the tracking error is bounded convergent. When the scaling function approaches zero iteratively, a zero convergence can be guaranteed in the iteration domain. The main results are verified through simulation examples.

关键词： convergence analysis data-driven ILC encoding-decoding mechanism nonlinear nonaffine systems uniform quantizer

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Proportional-integral-type estimator design for delayed recurrent neural networks under encoding-decoding mechanism

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2022年第13期53卷 2729-2741页

作者： Yang, Fan Li, Jiahui Dong, Hongli Shen, Yuxuan Northeast Petr Univ Artificial Intelligence Energy Res Inst Daqing 163318 Peoples R China Northeast Petr Univ Sanya Offshore Oil & Gas Res Inst Sanya 572025 Peoples R China Northeast Petr Univ Heilongjiang Prov Key Lab Networking & Intelligen Daqing 163318 Peoples R China

In this paper, the proportional-integral-type estimator design problem is studied for recurrent neural networks under the encoding-decoding communication mechanism. In the process of the measurement data transmission, an encoding-decoding mechanism is introduced to improve the security of the network by encrypting the measurement data. The purpose of this paper is to design a proportional-integral-type estimation algorithm such that the estimation error dynamics is exponentially ultimately bounded in mean square. First, a sufficient condition is obtained for the existence of the desired estimator. Then, the parameters of the estimator are obtained by solving certain matrix inequality. Finally, a simulation example is given to verify the effectiveness of the designed estimation algorithm.

关键词： Recurrent neural networks proportional-integral-type estimator encoding-decoding mechanism uniform quantiser

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Model-Free Adaptive Control for Nonlinear Multi-Agent Systems With encoding-decoding mechanism

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2022年 8卷 489-498页

作者： Zhang, Shuhua Ma, Lifeng Yi, Xiaojian Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China Beijing Inst Technol Sch Mechatron Engn Beijing 100081 Peoples R China Beijing Inst Technol Jiaxing 314000 Peoples R China

This paper is concerned with the consensus tracking problem for a class of nonlinear discrete-time multi-agent sy stems (MASs). The dynamic linearization method is used to approximate the nonlinear dynamics of the addressed MASs, resulting in an equivalent linear time-varying data model. With the purpose of mitigating the effects from limited communication bandwidth, a uniform-quantization-haled encoding-decoding mechanism is exploited. A model-free adaptive distributed control protocol is put forward to deal with the tracking problem, which is totally data-driven without any requirement of model information except for I/O data. Finally, two illustrative simulation examples are utilized to demonstrate the effectiveness of the proposed control scheme.

关键词： encoding-decoding mechanism model-free adaptive control multi-agent systems uniform quantizer

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Gradient-Based Iterative Learning Control for Signal Quantization with encoding-decoding mechanism 12

Gradient-Based Iterative Learning Control for Signal Quantiz...

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IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)

作者： Tao, Yujuan Huang, Yande Tao, Hongfeng Chen, Yiyang Jiangnan Univ Minist Educ Key Lab Adv Proc Control Light Ind Wuxi 214122 Jiangsu Peoples R China Soochow Univ Sch Mech & Elect Engn Suzhou 215137 Peoples R China

ISBN: (纸本)9798350321050

This paper addresses the optimization problem of quantized iterative learning control (ILC) for networked control systems (NCSs) with limited bandwidth. For linear time-invariant systems with quantized input signals, a mathematical cost function is constructed to obtain a gradient-based ILC law that rests with the system model, and the learning gain is updated in the trial domain. By combining the infinite logarithmic quantizer with the encoding and decoding mechanism to encode and decode the signals, the quantization accuracy is enhanced and the system tracking capability is improved. Compared with the traditional gradient descent method with fixed learning gain, the gradient-based ILC law can obtain faster error convergence. Simulation based on industrial robot system is given to substantiate the suggested method.

关键词： Quantized Iterative Learning Control Networked Control Systems encoding-decoding mechanism Gradient Descent Method

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Linear-fitting-based recursive filtering for nonlinear systems under encoding-decoding mechanism

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Science China(Information Sciences) 2024年第5期67卷 183-196页

作者： Bo JIANG Hongli DONG Zhiwei GAO Yuxuan SHEN Fan YANG Artificial Intelligence Energy Research Institute Northeast Petroleum University Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Sanya Offshore Oil & Gas Research Institute Northeast Petroleum University School of Electrical & Information Engineering Northeast Petroleum University

This paper deals with a recursive filtering problem for a class of discrete time-varying nonlinear networked systems with the encoding-decoding mechanism. The linear fitting method is introduced to handle the nonlinearity. An encoding-decoding mechanism is constructed to describe the data transmission process in wireless communication networks(WCNs). To be specific, the measurement outputs are mapped by a quantizer to unique codewords for transmission in WCNs. Then, the codewords are decoded by the decoder to recover the measurement outputs which are sent to the filter. The processing/encoding delay and network delay have been considered. Firstly, on the premise that the upper bound of the filtering error covariance is minimum, the appropriate filtering gain is calculated. Then, the mean square exponential boundedness of the filtering error is analyzed. Finally, two simulation examples are presented to verify the effectiveness of the proposed algorithm.

关键词： encoding-decoding mechanism uniform quantizer linear fitting networked systems recursive filtering

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Probability-guaranteed encoding-decoding-based state estimation for delayed memristive neutral networks with event-triggered mechanism

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INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING 2024年第8期38卷 2750-2770页

作者： Hu, Chen Zhang, Shuhua Zhao, Hongyuan Ma, Lifeng Guo, Jian Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China

This article handles the probability-guaranteed state estimation problem for a class of nonlinear memristive neural networks (MNNs) by using an event-triggered mechanism. Both time-varying delays and incomplete measurements are considered in the MNNs dynamics. To mitigate the impact of limited communication bandwidth, a communication protocol is proposed that incorporates an encoding-decoding technique in addition to an event-triggered scheme. The aim is to devise a state estimator that can estimate the states of MNNs, ensuring that the state estimation error falls within the required ellipsoidal area with a desired chance. We obtain sufficient conditions for the feasibility of the addressed problem, where the requested gains can be found iteratively by solving certain convex optimization problems. On the basis of the proposed framework, some issues are further presented to determine locally optimal estimator parameters according to different specifications. Finally, we utilize an illustrative numerical example to show the validity of our provided theoretical results.

关键词： encoding-decoding mechanism event-triggered technique memristive neural networks probability-guaranteed estimation

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encoding-decoding-Based Quantized Learning Control Using Spherical Polar Coordinates

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IEEE TRANSACTIONS ON CYBERNETICS 2025年第2期55卷 812-825页

作者： Huo, Niu Shen, Dong Ho, Daniel W. C. Renmin Univ China Sch Math Beijing 100872 Peoples R China City Univ Hong Kong Dept Math Hong Kong Peoples R China

This study investigates the performance of discrete-time systems under quantized iterative learning control. An encoding-decoding mechanism is combined with a spherical polar coordinate-based quantizer to process the signals transmitted through a control network, which introduces a quantization operation to the encoding process. A scenario involving encoding and decoding of the system output is explored before discussing the general scenario involving encoding and decoding of both the system output and control input. Unlike existing schemes, the two scenarios require no additional scaling parameter in the encoder and decoder. The radius of the support sphere is designed to vary over the iterations, and the learning control scheme is based on the output of the decoder. The results indicate that the control method enables error-free tracking performance of a system. The theoretical conclusions are verified in tests of a permanent magnet synchronous motor.

关键词： encoding-decoding mechanism quantized iterative learning control spherical polar coordinates

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