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Parameter Estimation and Tracking control of MIMO Linear systems Without Prior Knowledge of control Signs and Parameter Bounds

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IEEE Transactions on Automatic control 2024年第6期70卷 3695-3710页

作者： Xu, Yuchun Zhang, Yanjun Zhang, Ji-Feng Chinese Academy of Sciences Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Beijing100190 China Beijing Institute of Technology School of Automation Beijing100081 China Beijing Institute of Technology State Key Lab of Automation Intelligent Unmanned Systems Beijing100081 China University of Chinese Academy of Sciences School of Mathematics Sciences Beijing100149 China Zhongyuan University of Technology School of Automation and Electrical Engineering Zhengzhou450007 China Chinese Academy of Sciences Academy of Mathematics and Systems Science Beijing100190 China

Dealing with the uncertain high-frequency gain matrix, denoted as Kp, is a fundamental problem in multivariable adaptive control systems. In this paper, we propose a new solution for parameter estimation and adaptive control for a general class of multi-input multi-output discrete-time linear time-invariant systems. The proposed scheme does not require any prior knowledge of the sign or bound information of Kp, and thus, significantly relaxes the design conditions in traditional multivariable adaptive control systems. Compared with the commonly used Nussbaum gain or multi-model techniques for addressing the unknown signs of Kp, the proposed scheme does not rely on any additional design conditions or any switching mechanism, while still ensuring closed-loop stability and asymptotic output tracking. Specifically, an output feedback adaptive control law is developed based on a matrix decomposition technique, which leads to derivation of a modified estimation error model. Subsequently, a gradient-based parameter update law is formulated only relying on the non-zero condition of the leading principle minors of Kp. Through designing gain functions and stable filters, the controller is always non-singular and does not involve any causal contradiction problem. Simulation study showcases the design process and demonstrates the effectiveness of the proposed scheme. © 1963-2012 IEEE.

关键词： MIMO systems

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Combine deep learning and Bayesian analysis to separate overlapping gravitational wave signals

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science China(Physics,Mechanics & Astronomy) 2025年第5期68卷 205-219页

作者： Cunliang Ma Weiguang Zhou Zhoujian Cao Mingzhen Jia Jiangxi Provincial Key Laboratory of Multidimensional Intelligent Perception and Control Jiangxi University of Science and TechnologyGanzhou 341000China Institute of Applied Mathematics Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Fundamental Physics and Mathematical Sciences Hangzhou Institute for Advanced StudyUCASHangzhou 310024China

Future gravitational wave(GW)observatories,such as the Einstein Telescope,are anticipated to encounter overlapping GW signals,presenting considerable obstacles to GW data processing techniques,including signal identification and parameter *** this letter,we propose a scheme of combining deep learning and Bayesian analysis to disentangle overlapping GW *** deep learning part takes a data-driven approach that employs an encoder-separation-decoder framework which is powerful enough to extract the shape of the signal even when the GW signals completely *** Bayesian analysis part takes the matched filtering technique to extract the amplitude of the GW *** scheme can facilitate the utilization of existing GW detection and parameter estimation methods for future instances of overlapping *** methodology effectively reduces biases in parameter estimation when handling multiple intertwined ***,this marks the first known instance where deep learning has been successfully utilized to disentangle overlapping GW signals.

关键词： gravitational wave separation machine learning Einstein Telescope parameter estimation

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Convergence of Adaptive Identification for Phase Retrieval Problem 63

Convergence of Adaptive Identification for Phase Retrieval P...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Liu, Yujing Liu, Zhixin Guo, Lei University of Chinese Academy of Sciences Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematical Sciences Beijing China

ISBN: (纸本)9798350316339

The phase retrieval problem is of fundamental importance in various fields including computer science, physics, and engineering, where only the magnitude measurements are variable. For this NP-hard problem, previous work has mainly focused on the noiseless case, off-line algorithms, i.i.d. standard Gaussian assumptions on the regressor and convergence results in a high probability sense. To overcome these limitations, we consider the phase retrieval problem in the presence of noise disturbances. Based on the maximum likelihood (ML) criterion, we propose a novel adaptive identification algorithm to estimate the true signal vector. For the first time, we establish the global convergence and convergence rate results for the proposed algorithm without relying on the i.i.d. assumption on the regressor. Finally, we provide a numerical example to illustrate the effectiveness of our adaptive algorithm. © 2024 IEEE.

关键词： Consensus algorithm

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Cardiorespiratory Coupling Degree Affects the Efficiency of Cardiopulmonary System 43

Cardiorespiratory Coupling Degree Affects the Efficiency of ...

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43rd Chinese control Conference, CCC 2024

作者： Zhang, Jiajun Han, Jing University of Chanese Academy of Sciences Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematical Sciences Beijing100049 China

ISBN: (纸本)9789887581581

The human body is a complex system composed of many interacting subsystems, and the coupling degree among subsystems determines the physiological function and efficiency. The cardiac and respiratory systems are strongly coupled and modulated by the autonomic nervous system (ANS). In this paper, we explore the relationship between the degree of cardiorespiratory coupling represented by the Heart-Breath Coherence (H B C) metric and its physiological value from the perspective of oxygen uptake per unit of cardiac work. We collected the heart rate and respiratory data of 29 volunteers in multi-scenario experiments (contain arithmetic task, listening to sounds, emotional evocations, smelling odors and watching videos) and calculate their H B C values to obtain different degrees of cardiorespiratory coupling. Then, we calculate the volume of oxygen uptake and cardiac work by a gas exchange model, and its reliability is verified by the significantly positive correlation between oxygen saturation computed by the model and measured in the experiment. Calculation results of experimental data show that: higher cardiorespiratory coupling degree (H B C value) will increase the efficiency of cardiopulmonary system (oxygen intake per unit cardiac work);stressful tasks (low cardiorespiratory coupling degree) require more oxygen and the heart does more work, but the efficiency is lower than relaxation. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： cardiopulmonary system Cardiorespiratory coupling efficiency of oxygen intake per unit cardiac work Heart-Breath Coherence (HBC) life complex system

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Stability and Tracking Performance of PID controlled Nonlinear Uncertain systems 41

Stability and Tracking Performance of PID Controlled Nonline...

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第41届中国控制会议

作者： Cheng Zhao Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of Sciences

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper considers the global stability and tracking problem for a class of MIMO nonlinear uncertain systems controlled by the well-known classical PID *** contrary to most of the existing literature on PID controlled nonlinear uncertain systems,the reference signal is not assumed to be a constant or have a constant ***,we will show that the PID parameters can be chosen to ensure the global stability of the closed-loop control systems,as long as some prior knowledge about the partial derivatives of the nonlinear functions and the control gain matrix are *** addition,the tracking error is proved to be bounded in the whole time interval,with an ultimate upper bound proportional to the change rate of the reference *** implies that the ultimate bound of the tracking error will be small,if the reference signal is slowly ***,even if the reference signal is not slowly varying,we will show that the tracking error can still be made arbitrarily small by choosing the PID parameters suitably large.

关键词： PID control non-affine systems nonlinear uncertain system global stabilization tracking

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A Memory Enhancement Adjustment Method Based on Stochastic Gradients 41

A Memory Enhancement Adjustment Method Based on Stochastic G...

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第41届中国控制会议

作者： Gan Li Key Laboratory of Systems and Control Institute of Systems ScienceAcademy of Mathematics and Systems ScienceChinese Academy of Sciences

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Stochastic gradient descent methods and its variants have been widely used to learn the parameters of a neural network by solving an associated non-convex minimization *** propose a new momentum method and adaptive method(MEA/AdaMEA)based on memory enhancement adjustment,which is different from the traditional momentum methods and adaptive *** the theoretical level,we prove the convergence of the MEA method for solving a non-convex minimization problem,and then analyze the generalization error of the MEA method from the perspective of consistent stability,showing that the MEA method can improve the stability of the learned model and enhance the generalization *** the experimental level,we compare the empirical results of the MEA/AdaMEA method and common optimizers for deep learning on the datasets CIFAR10 and CIFAR100 under the network architectures of the convolutional neural networks ResNetl8 and *** demonstrate the effectiveness of our proposed MEA/AdaMEA.

关键词： Stochastic Gradient Algorithms Momentum Adaptive Methods Deep Learning

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An Inverse Problem for Adaptive Linear Quadratic Stochastic Differential Games 63

An Inverse Problem for Adaptive Linear Quadratic Stochastic ...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Chen, Zhixing Guo, Lei Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences School of Mathematical Science Beijing100049 China

ISBN: (纸本)9798350316339

In this paper, we consider an inverse problem for adaptive two-player stochastic linear quadratic differential games where the cost functions of players are unknown to each other, which arise in many practical situations but have rarely been explored theoretically. We will introduce a new feedback pattern where Player 2 chooses the optimal actions given the actions of Player 1, while Player 1 uses an adaptive learning algorithm to recover Player 2's cost function and chooses an adaptive strategy by solving the corresponding coupled Riccati equations. Under some suitable assumptions on the system matrices, it is shown that estimates for the cost function parameters are strongly consistent, and the adaptive game systems asymptotically reach the Nash equilibrium. © 2024 IEEE.

关键词： Riccati equations

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Convergence of Online Learning Algorithm for a Mixture of Multiple Linear Regressions 41

Convergence of Online Learning Algorithm for a Mixture of Mu...

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41st International Conference on Machine Learning, ICML 2024

作者： Liu, Yujing Liu, Zhixin Guo, Lei The Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China

Mixed linear regression (MLR) is a powerful model to characterize nonlinear relationships among observed data while still being simple and computationally efficient. This paper investigates the online learning and data clustering problem for MLR model with an arbitrary number of sub-models and arbitrary mixing weights. Previous investigations mainly focus on offline learning algorithms, and the convergence results are established under the independent and identically distributed (i.i.d.) input data assumption. To overcome these fundamental limitations, we propose a novel online learning algorithm for parameter estimation based on the EM principle. By using Ljung's ODE method and Lyapunov stability theorem, we first establish the almost sure convergence results of the proposed algorithm without the traditional i.i.d. assumption on the input data. Furthermore, by using the stochastic Lyapunov function method, we also provide its convergence rate analysis for the first time. Finally, we analyze the performance of online data clustering based on the parameter estimates, which is asymptotically the same as that in the case of known parameters. Copyright 2024 by the author(s)

关键词： Multiple linear regression

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Learning-Based Predictive control of Switched Nonlinear systems via Koopman Modeling Approach

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Journal of systems science & Complexity 2025年第2期38卷 691-716页

作者： NING Zepeng FANG Xu LI Yibei XIE Lihua School of Electrical and Electronic Engineering Nanyang Technological University Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences

This paper proposes a data-driven learning-based approach to predictive control for switched nonlinear systems subject to state and control constraints and external stochastic disturbances. A switched Koopman modeling framework is developed, where a multi-mode neural network for state lifting is trained simultaneously with Koopman operators and state reconstruction matrices for all *** framework facilitates the construction of the switched linear Koopman model in a transformed space and effectively captures the dynamics of the original nonlinear system. A switched predictive control strategy is then designed to regulate the switched Koopman model with constrained states and control inputs against both the stochastic disturbances and the uncertainties introduced by the lifting neural network. The proposed control scheme ensures mean-square stability and guarantees boundedness during the online phase. Furthermore, boundedness analysis is performed to determine the bounded set of the original system state across all admissible switching sequences. The effectiveness of the proposed methodology is demonstrated through a case study of a gene regulatory network.

关键词： Data-driven machine learning model predictive control stochastic systems switched Koopman model switched nonlinear systems

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On tuning of ADRC with competing design indices:a quantitative study

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control Theory and Technology 2023年第1期21卷 16-33页

作者： Yutao Zhao Yi Huang Zhiqiang Gao Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China Department of Electrical Engineering and Computer Science Center for Advanced Control TechnologiesCleveland State UniversityClevelandOH 44115USA

controller optimization has mostly been done by minimizing a certain single cost *** practice,however,engineers must contend with multiple and conflicting considerations,denoted as design indices(DIs)in this *** to account for such complexity and nuances is detrimental to the applications of any advanced control *** paper addresses this challenge heads on,in the context of active disturbance rejection controller(ADRC)and with four competing DIs:stability margins,tracking,disturbance rejection,and noise *** this end,the lower bound for the bandwidth of the extended state observer is first established for guaranteed closed-loop ***,one by one,the mathematical formula is meticulously derived,connecting each DI to the set of controller *** our best knowledge,this has not been done in the context of *** formulas allow engineers to see quantitatively how the change of each tuning parameter would impact all of the DIs,thus making the guesswork *** example is given to show how such analytical methods can help engineers quickly determine controller parameters in a practical scenario.

关键词： Uncertain systems Active disturbance rejection control(ADRC) Design indices(DIs) Extended state observer(ESO)

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