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system identification and Parameter Self-Tuning Controller on Deep-Sea Mining Vehicle

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China Ocean Engineering 2023年第1期37卷 53-61页

作者： WENG Qi-wang YANG Jian-min LIANG Qiong-wen MAO Jing-hang GUO Xiao-xian SJTU Yazhou Bay Institute of Deepsea SCI-TECH Sanya 572000China State Key Laboratory of Ocean Engineering Shanghai Jiao Tong UniversityShanghai 200240China Institute of Marine Equipment Shanghai Jiao Tong UniversityShanghai 200240China

system identification is a quintessential measure for real-time analysis on kinematic characteristics for deep-sea mining vehicle, and thus to enhance the control performance and testing efficiency. In this study, the system identification algorithm, recursive least square method with instrumental variables(IV-RLS), is tailored to model ‘Pioneer I’, a deep-sea mining vehicle which recently completed a 1305-meter-deep sea trial in the Xisha area of the South China Sea in August, 2021. The algorithm operates on the sensor data collected from the trial to obtain the vehicle’s kinematic model and accordingly design the parameter self-tuning controller. The performances demonstrate the accuracy of the model, and prove its generalization capability. With this model, the optimal controller has been designed, the control parameters have been self-tuned, and the response time and robustness of the system have been optimized,which validates the high efficiency on digital modelling for precision control of deep-sea mining vehicles.

关键词： deep-sea mining system identification parameter self-tuning controller digital modeling

system identification with binary-valued observations under both denial-of-service attacks and data tampering attacks:the optimality of attack strategy

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Control Theory and Technology 2022年第1期20卷 127-138页

作者： Jin Guo Xuebin Wang Yanling Zhang Wenchao Xue Yanlong Zhao School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing100083China Key Laboratory of Knowledge Automation for Industrial Processes Ministry of EducationBeijing100083China Key Laboratory of systems and Control Institute of Systems ScienceAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing100190China

With the development of wireless communication technology,cyber physical systems are applied in various fields such as industrial production and infrastructure,where lots of information exchange brings cyber security threats to the systems.From the perspective of system identification with binary-valued observations,we study the optimal attack problem when the system is subject to both denial of service attacks and data tampering attacks.The packet loss rate and the data tampering rate caused by the attack is given,and the estimation error is derived.Then the optimal attack strategy to maximize the identification error with the least energy is described as a min–max optimization problem with constraints.The explicit expression of the optimal attack strategy is obtained.Simulation examples are presented to verify the effectiveness of the main conclusions.

关键词： system identification Binary-valued observations Denial-of-service attacks Data tampering attacks

system identification under saturated precise or set-valued measurements

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Science China(Information Sciences) 2023年第1期66卷 224-243页

作者： Yanlong ZHAO Hang ZHANG Ting WANG Guolian KANG Key Laboratory of systems and Control Academy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences Institute of Artificial Intelligence University of Science and Technology Beijing Department of Biostatistics St.Jude Children's Research Hospital

This paper considers the system identification problem based on saturated precise or set-valued measurements, which is widely used in various fields and has essential difficulties. Since existing methodologies cannot make full use of the mixed data, this paper is aiming to fill the gap and build a unified framework in dealing with such problems rigorously and comprehensively. New algorithms are introduced and their properties are established. Most significantly, the Cramécr-Rao(CR) lower bound based on the measurements is established, which consists of two parts with respect to the precise data and set-valued data, respectively. This prompts the idea of designing an estimation algorithm by grouping and combining the estimations under two classifications of data. As a result, a CR lower bound-based algorithm(CRBA) is constructed. The convergence properties are theoretically analyzed in terms of consistency and asymptotic efficiency under periodic inputs. For general inputs, an algorithm based on the CRBA that combines the expectation maximization(EM) algorithm for set-valued subsystems and the gradient descent algorithm for precise subsystems is proposed. Numerical simulations validate the superiority of the proposed algorithms.

关键词： system identification Cramér-Rao lower bound truncated data precise measurement set-valued measurement

Data-driven discovery of linear dynamical systems from noisy data

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Science China(Technological Sciences) 2024年第1期67卷 121-129页

作者： WANG YaSen YUAN Ye FANG HuaZhen DING Han School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China State Key Lab of Digital Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan 430074China School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China Department of Mechanical Engineering University of KansasLawrence 66045USA

In modern science and engineering disciplines,data-driven discovery methods play a fundamental role in system modeling,as data serve as the external representations of the intrinsic mechanisms within systems.However,empirical data contaminated by process and measurement noise remain a significant obstacle for this type of modeling.In this study,we have developed a data-driven method capable of directly uncovering linear dynamical systems from noisy data.This method combines the Kalman smoothing and sparse Bayesian learning to decouple process and measurement noise under the expectation-maximization framework,presenting an analytical method for alternate state estimation and system identification.Furthermore,the discovered model explicitly characterizes the probability distribution of process and measurement noise,as they are essential for filtering,smoothing,and stochastic control.We have successfully applied the proposed algorithm to several simulation systems.Experimental results demonstrate its potential to enable linear dynamical system discovery in practical applications where noise-free data are intractable to capture.

关键词： system identification sparse Bayesian learning Kalman smoothing process and measurement noise

Threshold Selection and Resource Allocation for Quantized identification

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Journal of systems Science & Complexity 2024年第1期37卷 204-229页

作者： WANG Ying LI Xin ZHAO Yanlong ZHANG Ji-Feng Key Laboratory of systems and Control Institute of Systems ScienceAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China

This paper is concerned with the optimal threshold selection and resource allocation problems of quantized identification, whose aims are improving identification efficiency under limited resources. Firstly, the first-order asymptotically optimal quantized identification theory is extended to the weak persistent excitation condition. Secondly, the characteristics of time and space complexities are established based on the Cramér-Rao lower bound of quantized systems. On these basis, the optimal selection methods of fixed thresholds and adaptive thresholds are established under aperiodic signals, which answer how to achieve the best efficiency of quantized identification under the same time and space complexity. In addition, based on the principle of maximizing the identification efficiency under a given resource, the optimal resource allocation methods of quantized identification are given for the cases of fixed thresholds and adaptive thresholds, respectively, which show how to balance time and space complexity to realize the best identification efficiency of quantized identification.

关键词： Quantized output resource allocation system identification threshold selection.

The posterior selection method for hyperparameters in regularized least squares method

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Control Theory and Technology 2024年第2期22卷 184-194页

作者： Yanxin Zhang Jing Chen Yawen Mao Quanmin Zhu School of Science Jiangnan UniversityWuxi214122JiangsuChina The Science and Technology on Near-Surface Detection Laboratory Wuxi214122JiangsuChina Department of Engineering Design and Mathematics University of the West of EnglandBristolBS161QYUK

The selection of hyperparameters in regularized least squares plays an important role in large-scale system identification. The traditional methods for selecting hyperparameters are based on experience or marginal likelihood maximization method, which are inaccurate or computationally expensive. In this paper, two posterior methods are proposed to select hyperparameters based on different prior knowledge (constraints), which can obtain the optimal hyperparameters using the optimization theory. Moreover, we also give the theoretical optimal constraints, and verify its effectiveness. Numerical simulation shows that the hyperparameters and parameter vector estimate obtained by the proposed methods are the optimal ones.

关键词： Regularization method Hyperparameter system identification Least squares algorithm

identification of Linear systems Using Binary Sensors with Random Thresholds

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Journal of systems Science & Complexity 2024年第3期37卷 907-923页

作者： HUANG Zhiyong SONG Qijiang School of Mathematics Renmin University of ChinaBeijing 100872China School of Mathematics Sino-French InstituteRenmin University of ChinaBeijing 100872China

In this paper,the problem of identifying autoregressive-moving-average systems under random threshold binary-valued output measurements is considered.With the help of stochastic approximation algorithms with expanding truncations,the authors give the recursive estimates for the parameters of both the linear system and the binary sensor.Under reasonable conditions,all constructed estimates are proved to be convergent to the true values with probability one,and the convergence rates are also established.A simulation example is provided to justify the theoretical results.

关键词： α-mixing binary sensor quantized system stochastic approximation stochastic thresh-old strongly consistent system identification

system identification of Controlled Pulse Keyhole Plasma Arc Welding Process

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Chinese Journal of Mechanical Engineering 2012年第6期25卷 1274-1280页

作者： JIA Chuanbao DU Yongpeng GUO Ning WANG Fang HAN Yanfei Shandong Provincial Key Laboratory of Special Welding Technology Institute of Oceanographic InstrumentationShandong Academy of SciencesQingdao 266001China

The development of closed-loop control systems is one of the most effective ways to improve the stability of the keyhole status during keyhole plasma arc welding （K-PAW）. Due to the disadvantages of the ＂one-pulse-one-keyhole＂ technology based on the conventional square current waveform, the controlled pulse welding current waveform is newly applied to control the keyhole open and close periodically. In order to realize the real-time control on the keyhole behavior with this advanced current waveform, welding experiments and system identification are conducted based on the classical control theory. One complete welding cycle can be divided into 3 periods. The keyhole establishing time is the most important time variable, which determines the keyhole behavior and welding process stability. At the same time, the averaged effiux plasma arc voltage during one pulse cycle can reflect the real keyhole dimension and status in a real-time manner. Therefore, two single-input-single-output （SISO） systems are proposed, in which keyhole establishing time and keyhole average dimension are taken as the system controlled variables respectively. Welding experiments are designed with the peak current varying randomly. Experiments show that the keyhole establishing time changes in an opposite direction to the varied peak current, and the averaged efflux plasma arc voltage varies with the same trend as the peak current. Based on the least squares technique and F test of classical system identification, second order difference equation for keyhole establishing time/peak current system and first order difference equation for keyhole average dimension/peak current system are obtained. It is proved that the calculated data by the two mathematical expressions are well matched with the measured data. The proposed research provides mathematical expressions and theoretical analysis to develop closed-loop systems for the controlled pulse K-PAW.

关键词： controlled pulse plasma arc welding keyhole system identification control

system identification Modeling of Ship Manoeuvring Motion in 4 Degrees of Freedom Based on Support Vector Machines

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China Ocean Engineering 2015年第4期29卷 519-534页

作者：王雪刚邹早建余龙蔡韡 School of Naval Architecture Ocean and Civil Engineering Shanghai Jiao Tong University State Key Laboratory of Ocean Engineering Shanghai Jiao Tong University Jiujiang Branch of No.707 Research Institute China Shipbuilding Industry Corporation

Based on support vector machines, three modeling methods, i.e., white-box modeling, grey-box modeling and black-box modeling of ship manoeuvring motion in 4 degrees of freedom are investigated. With the whole-ship mathematical model for ship manoeuvring motion, in which the hydrodynamic coefficients are obtained from roll planar motion mechanism test, some zigzag tests and turning circle manoeuvres are simulated. In the white-box modeling and grey-box modeling, the training data taken every 5 s from the simulated 20°/20° zigzag test are used, while in the black-box modeling, the training data taken every 5 s from the simulated 15°/15°, 20°/20° zigzag tests and 15°, 25° turning manoeuvres are used; and the trained support vector machines are used to predict the whole 20°/20° zigzag test. Comparisons between the simulated and predicted 20°/20° zigzag tests show good predictive ability of the proposed methods. Besides, all mathematical models obtained by the proposed modeling methods are used to predict the 10°/10° zigzag test and 35° turning circle manoeuvre, and the predicted results are compared with those of simulation tests to demonstrate the good generalization performance of the mathematical models. Finally, the proposed modeling methods are analyzed and compared with each other in aspects of application conditions, prediction accuracy and computation speed. The appropriate modeling method can be chosen according to the intended use of the mathematical models and the available data needed for system identification.

关键词： ship manoeuvring 4 degrees of freedom system identification support vector machines