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Trajectory tracking control of discrete non-affine MIMO iterative systems with unknown models: a neural-network-based data-driven algorithm

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APPLIED INTELLIGENCE 2024年第20期54卷 9936-9955页

作者： Shi, Qingyu Huang, Xia Wang, Zhen Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao 266590 Peoples R China

This paper devises a neural-network-based data-driven (NN-DD) algorithm to address the trajectory tracking control (TC) of discrete non-affine MIMO systems with unknown models and repetitive operation patterns. data-driven control no longer relies on the precise model of the controlled system, thereby breaking free from the limitations of model-based control strategies. Inspired by this, the primary objective of the algorithm is to ensure that the tracking error of the system is uniformly ultimately bounded through a data-driven approach. The algorithm is comprised of a DD modeling approach based on an enhanced stochastic configuration network (ESCN), and a control input solving approach based on radial basis function neural networks (RBFNNs). The numerical simulations indicate that the proposed algorithm achieves a decrease in the modeling error to 3.29e-7\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$3.29e-7$$\end{document} and the tracking error to 1e-8\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1e-8$$\end{document} after just 20 iterations. In addition, the numerical simulations also demonstrate that the modeling algorithm based on an ESCN reduces the modeling errors by 48.11%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$48.11\%$$\end{document} and 99.95%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage

关键词： MIMO systems data-driven algorithm Trajectory tracking control Enhanced stochastic configuration network RBFNNs

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A novel data-driven algorithm to reveal and track the ribosome heterogeneity in single molecule studies

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BIOPHYSICAL CHEMISTRY 2015年 199卷 39-45页

作者： Yang, Haopeng Xiao, Ming Wang, Yuhong Univ Houston Houston TX 77214 USA

The unique advantage of the single molecule approach is to reveal the inhomogeneous subpopulations in an ensemble. For example, smFRET (single molecule fluorescence resonance energy transfer) can identify multiple subpopulations based on the FRET efficiency histograms. However, identifying multiple FRET states with overlapping average values remains challenging. Here, we report a new concept and method to analyze the single molecule FRET data of a ribosome system. The main results are as follows: 1. based on a hierarchit concept, multiple ribosome subpopulations are identified. 2. The subpopulations are self-identified via the cross-correlation analysis of the FRET histogram profiles. The dynamic heterogeneity is tracked after 2 min intervals on the same ribosomes individually. 3. The major ribosome subpopulations exchange with each other with a certain pattern, indicating some correlations among the motions of the tRNAs and the ribosomal components. Experiments under the conditions of 20% glycerol or I mM viomycin supported this conclusion. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Ribosome conformational dynamics Single molecule FRET data-driven algorithm Dynamic heterogeneity

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Optimizing the Empirical Parameters of the data-driven algorithm for SIF Retrieval for SIFIS Onboard TECIS-1 Satellite

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SENSORS 2021年第10期21卷 3482页

作者： Zou, Chu Du, Shanshan Liu, Xinjie Liu, Liangyun Wang, Yuyang Li, Zhen Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci Beijing 100094 Peoples R China Univ Chinese Acad Sci Coll Resources & Environm Beijing 100049 Peoples R China China Acad Space Technol Inst Remote Sensing Satellite Beijing 100095 Peoples R China

Space-based solar-induced chlorophyll fluorescence (SIF) has been widely demonstrated as a great proxy for monitoring terrestrial photosynthesis and has been successfully retrieved from satellite-based hyperspectral observations using a data-driven algorithm. As a semi-empirical algorithm, the data-driven algorithm is strongly affected by the empirical parameters in the model. Here, the influence of the data-driven algorithm's empirical parameters, including the polynomial order (n(p)), the number of feature vectors (n(SV)), the fluorescence emission spectrum function, and the fitting window used in the retrieval model, were quantitatively investigated based on the simulations of the SIF Imaging Spectrometer (SIFIS) onboard the First Terrestrial Ecosystem Carbon Inventory Satellite (TECIS-1). The results showed that the fitting window, n(p), and n(SV) were the three main factors that influenced the accuracy of retrieval. The retrieval accuracy was relatively higher for a wider fitting window;the root mean square error (RMSE) was lower than 0.7 mW m(-2) sr(-1) nm(-1) with fitting windows wider than 735-758 nm and 682-691 nm for the far-red band and the red band, respectively. The RMSE decreased first and then increased with increases in n(p) range from 1 to 5 and increased in n(SV) range from 2 to 20. According to the specifications of SIFIS onboard TECIS-1, a fitting window of 735-758 nm, a second-order polynomial, and four feature vectors are the optimal parameters for far-red SIF retrieval, resulting in an RMSE of 0.63 mW m(-2) sr(-1) nm(-1). As for red SIF retrieval, using second-order polynomial and seven feature vectors in the fitting window of 682-697 nm was the optimal choice and resulted in an RMSE of 0.53 mW m(-2) sr(-1) nm(-1). The optimized parameters of the data-driven algorithm can guide the retrieval of satellite-based SIF and are valuable for generating an accurate SIF product of the TECIS-1 satellite after its launch.

关键词： solar-induced chlorophyll fluorescence (SIF) data-driven algorithm Terrestrial Ecosystem Carbon Inventory Satellite (TECIS-1) parameter optimization

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A data-driven Combined algorithm for Abnormal Power Loss Detection in the Distribution Network

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IEEE ACCESS 2020年 8卷 24675-24686页

作者： Long, Huan Chen, Chang Gu, Wei Xie, Jihua Wang, Zheng Li, Guodong Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China Jiangsu Key Lab Smart Grid Technol & Equipment Nanjing 210096 Peoples R China Tianjin Elect Power Res Inst Tianjin 300041 Peoples R China

Power loss, consisting of technical loss (TL) and non-technical loss (NTL), reflects the effective utilization rate of energy and the management level of power grids. This paper proposes a data-driven combined algorithm to systematically identify anomalies of power loss in the distribution network, including the abnormal type, time, and position. The detection process contains three stages: abnormal feeder detection, abnormal time detection, and abnormal position detection. The suspected abnormal feeders are first detected from all feeders in the distribution network by the data-driven algorithm based on the daily power supply and electricity sales data. Then, the control chart is employed to further monitor the fluctuation of the power loss of each suspected abnormal feeder and discover its abnormal time. Based on the detected abnormal time, its abnormal position is finally located through the risk assessment technology. Numerous experiments based on the real data show that the proposed data-driven combined algorithm can effectively detect and analyze abnormal power loss in the distribution network.

关键词： Power loss abnormal detection data-driven algorithm control chart risk assessment

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Hybrid data-driven Parameters Estimation for Communication-Less WPT System With Reduced Primary Sampling data

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IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 2025年第1期11卷 2435-2443页

作者： Cheng, Bing He, Liangzong Liu, Houxuan Xiamen Univ Xiamen 361005 Fujian Peoples R China

Wireless power transfer (WPT) systems advantaging environment-friendly and highly efficient, and accurate parameter estimation is a premise for precise control and a guarantee for system safety. However, the conventional parameter estimation method, which is based on the primary impedance angle is easily affected by the components delay, current distortion, and other issues during the detection process. Meanwhile, conventional data-driven parameter estimation requires a large number of data and dual-side commutation, resulting in higher execution costs. To address those issues, an online/offline hybrid data-driven parameters estimation method utilizing the backpropagation neural network (BPNN) is proposed for the communication-less WPT system. The primary dc input voltage and current are taken as the input variables of the BPNN model to reduce the noise of training data. Further, to overcome the difficulty of a large account of training data collection, an approximate BPNN model is built first by the offline training under a small sampling set. Then, online data-driven parameter estimation by monitoring the primary resonant current is implemented to optimize the built BPNN model constantly, resulting in enhancing estimation accuracy and avoiding complex wireless communication. Finally, an experimental setup is constructed to verify the feasibility of the proposed data-driven estimation method.

关键词： Parameter estimation Accuracy Impedance Estimation Mathematical models Inductance Training data Backpropagation neural network (BPNN) data-driven algorithm parameters estimation primary information wireless power transfer (WPT)

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Partially observed linear quadratic stochastic optimal control problem in infinite horizon: A data-driven approach

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SYSTEMS & CONTROL LETTERS 2025年 198卷

作者： Li, Xun Wang, Guangchen Xiong, Jie Zhang, Heng Hong Kong Polytech Univ Dept Appl Math Hong Kong Peoples R China Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China Southern Univ Sci & Technol Dept Math Shenzhen 518055 Peoples R China Southern Univ Sci & Technol SUSTech Int Ctr Math Shenzhen 518055 Peoples R China

This paper develops a data-driven algorithm to solve an infinite-horizon partially observed linear quadratic stochastic optimal control problem. The optimal control of this problem is related to an algebraic Riccati equation (ARE) and a filtering equation. First, we prove that the solution of a Riccati-type ordinary differential equation (ODE) converges to the unique positive semidefinite solution of the ARE. Next, we establish some data-based relationships among the system input, the system state and certain matrices that appear in the Riccati-type ODE and the filtering equation. Then, using these relationships, we design a data-driven algorithm to approximate the positive semidefinite solution of the ARE and the optimal control. The main feature of this algorithm is that it does not need the information of two system coefficients. Finally, we prove the convergence of the obtained algorithm and demonstrate its effectiveness by simulating a concrete example.

关键词： Partially observed state Linear quadratic stochastic optimal control problem Infinite horizon Algebraic Riccati equation (ARE) data-driven algorithm

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A Novel data-driven DRNN-SMC Model for Redundant Manipulators

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年第6期55卷 4322-4333页

作者： Yang, Min Guo, Yuxin Zhu, Siying Tan, Ning Liao, Bolin Zhang, Hui Hunan Univ Sch Robot Changsha 410082 Peoples R China Hunan Univ Natl Engn Res Ctr Robot Visual Percept & Control T Changsha 410082 Peoples R China Southeast Univ Sch Automat Nanjing 210096 Jiangsu Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Jishou Univ Coll Comp Sci & Engn Jishou 416000 Peoples R China

The robot industry is developing rapidly, and how to control the redundant manipulators precisely and effectively has become a new hot topic in industry's development. In recent years, many scholars in the industry have also proposed various control methods. However, most of these methods are proposed assuming that the Jacobian matrix is known. Actually, in practical applications, the detailed information of Jacobian matrix is often not precisely known. Therefore, this article develops a novel data-driven recurrent neural network (RNN) model that can update the Jacobian matrix and joint angles. By defining two dynamic error functions, two RNN designed formulas are used to obtain a continuous RNN (CRNN) model. Subsequently, the CRNN model is discretized by using Euler forward formula, and a discrete RNN (DRNN) model is generated. Then, a classic sliding mode control (SMC) algorithm is introduced, and DRNN-SMC model is further proposed. Moreover, the corresponding rigorous mathematical derivation and proof are carried out. In addition, simulation tests are carried out by using the Kinova Gen2 manipulator, comparing the DRNN model and PD controller, as well as the DRNN-SMC model and DRNN model, validating the precision of the DRNN-SMC model. Additionally, practical experiments using the Kinova Gen3 manipulator are performed to showcase the applicability and versatility of the DRNN-SMC model.

关键词： Manipulators Mathematical models Jacobian matrices End effectors Recurrent neural networks Adaptation models Heuristic algorithms Manipulator dynamics Lyapunov methods Joints data-driven algorithm DRNN DRNN-SMC motion control redundant manipulator SMC

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data-driven structural health monitoring for early corrosion detection in the oil and gas pipelines: enhancing sensitivity with unsupervised dimensionality reduction

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2025年

作者： de Aguiar, Filipe Moreira Correa, Lucio de Abreu de Oliveira, Henrique Tormen Haan Clarke, Thomas Gabriel Rosauro Ramos, Julio Endress Fed Univ Rio Grande Do Sul UFRGS Postgrad Program Min Met & Mat Engn PPGEM Av Bento Goncalves 9500 BR-90040060 Porto Alegre Brazil Aix Marseille Univ CNRS Cent Med LMA Marseille France Petrobras SA Ctr Pesquisas & Desenvolvimento Leopoldo Americo M Rio De Janeiro Brazil

Corrosion presents significant threats to structural integrity within the oil and gas industry, increasing maintenance demands and associated costs. However, traditional inspection techniques often face limitations in detecting early-stage corrosion, particularly under challenging environmental and operational conditions. This study investigates structural health monitoring using low computational cost data-driven approaches, specifically principal component analysis, t-distributed stochastic neighbor embedding, and locally linear embedding to enable early and autonomous corrosion detection capabilities in a scalable manner for embedded systems. Corrosion was induced in a steel pipe using an ionic solution, with nearly 400 GB of guided wave signal data recorded across well-defined corrosion stages via an array of piezoelectric transducers. The results demonstrate that the proposed approach enables the detection of corrosion with a sensitivity of up to 0.39% reduction in the cross-sectional area. Furthermore, the results validate the feasibility of establishing autonomous damage detection thresholds, mitigating the need for periodic inspections. The methodology proved robust, effectively isolating environmental effects without the need for environmental and operational condition compensation techniques. Unsupervised dimensionality reduction effectively detects early structural changes, such as corrosion and transduction loss, with guided waves and optimized parameters, offering strong support for traditional inspection methods.

关键词： SHM data-driven algorithm dimensionality reduction guided waves corrosion test pipelines

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data-driven estimation of the inertia moment of wind turbines: A new ice-detection algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING 2013年第6期227卷 552-555页

作者： Stotsky, Alexander Egardt, Bo Chalmers Dept Signals & Syst SE-41296 Gothenburg Sweden

Turbine blades accumulate ice under certain atmospheric conditions, such as low temperature and high humidity. This implies additional loads that might damage the turbine. A reliable ice-detection algorithm is required to shut down the turbine and prevent damages. A simple sensorless technique is proposed in this article for detection of both icing and ice shedding events. The technique is based on the estimation of the turbine inertia moment using generator speed measurements, turbine model and robust and fast convergent data-driven algorithms. Estimated inertia moment can be used for both ice detection and adaptation of the parameters of the control system. Implementation of this technique allows a stable turbine operation during hazardous ice conditions via adjustment of the control system parameters or turbine shut down in the extreme icing conditions.

关键词： Wind turbine icing and shedding events estimation of the inertia moment data-driven algorithm

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data-driven optimal tracking control of discrete-time multi-agent systems with two-stage policy iteration algorithm

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INFORMATION SCIENCES 2019年 481卷 189-202页

作者： Peng, Zhinan Zhao, Yiyi Hu, Jiangping Ghosh, Bijoy Kumar Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Sichuan Peoples R China Southwestern Univ Finance & Econ Sch Business Adm Chengdu 611130 Sichuan Peoples R China Texas Tech Univ Dept Math & Stat Lubbock TX 79409 USA

Herein, a novel adaptive dynamic programming (ADP) algorithm is developed to solve the optimal tracking control problem of discrete-time multi-agent systems. Compared to the classical policy iteration ADP algorithm with two components, policy evaluation, and policy improvement, a two-stage policy iteration algorithm is proposed to obtain the iterative control laws and the iterative performance index functions. The proposed algorithm contains a sub-iteration procedure to calculate the iterative performance index functions at the policy evaluation. The convergence proof for the iterative performance index functions and the iterative control laws are provided. Subsequently, the stability of the closed-loop error system is also provided. Further, an actor-critic neural network (NN) is used to approximate both the iterative control laws and the iterative performance index functions. The actor-critic NN can implement the developed algorithm online without knowledge of the system dynamics. Finally, simulation results are provided to illustrate the performance of our method. (C) 2018 Elsevier Inc. All rights reserved.

关键词： Optimal tracking control Multi-agent systems Two-stage policy iteration Actor-critic networks data-driven algorithm

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