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4th International Conference on Information, Cybernetics and Computational Social Systems (ICCSS)

作者： Liu, Yunfeng Li, Jiaqi Zhang, Biqing Bohai Univ Jinzhou Peoples R China

ISBN: (纸本)9781538632574

So as to improve the detectability of small targets on sea clutter, the method based on variable forgetting factor linear prediction is proposed. First, the recursive least square method is used to dynamically adjust the parameters of the prediction model. Secondly, the value of forgetting factor is given by the adjustable formula. Finally, the prediction error is calculated and the target results are obtained by threshold comparison. The experiments show that the detect performance of the proposed method is superior to the conventional method. The proposed method overcomes the limitation of the fixed model and improves detection capability.

关键词： target detection sea clutter recursive least square variable forgetting factor linear prediction

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variable forgetting factor identification algorithm for fault diagnosis of wind turbines

Variable forgetting factor identification algorithm for faul...

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第28届中国控制与决策会议

作者： Dinghui Wu Jin Song Yanxia Shen Key Laboratory of Advanced Process Control for Light Industry Jiangnan University

ISBN: (纸本)9781467397155

To diagnose the fault in the pitch actuator of wind turbines,a fault diagnosis method is proposed in this paper,which is based on the variable forgetting factor recursive least-squares(VFF-RLS) *** to the characteristic that the system parameters are changed by faults,the VFF-RLS system identification algorithm is adopted to estimate the changing *** fault diagnosis problem is transformed into a parameter estimation *** the time-varying natural frequency and damping ratio of pitch actuators are estimated based on the discrete *** convergence rate and identification accuracy of the identification algorithm can be guaranteed by adjusting the forgetting factor *** simulation results validate the effectiveness of the proposed method.

关键词： Wind turbines Pitch actuator Fault diagnosis System identification variable forgetting factor

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An Improved Strong Tracking variable forgetting factor RLS Algorithm with Low Complexity for Dynamic System Identification

An Improved Strong Tracking Variable Forgetting Factor RLS A...

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2nd IEEE International Conference on Electronics and Communication Engineering (ICECE)

作者： Lv Shuhua Quan Zhi Zhengzhou Univ Sch Informat Engn Zhengzhou Peoples R China

ISBN: (数字)9781728147840

ISBN: (纸本)9781728147840

The recursive least squares (RLS) adaptive filter is an appealing choice in systems identification problems, mainly due to its fast convergence rate. However, it is computationally very complex, which may make it impractical for the identification of the large length impulse response, and the fixed forgetting factor RLS algorithm in the time-varying system cannot guarantee both fast convergence rate and low mean squared error (MSE). In this paper, we proposed a novel approach which improves the efficiency of the RLS algorithm. The basic idea is to apply dichotomous coordinate descent (DCD) and a practical variable forgetting factor (VFF) RLS algorithms. Compared with the traditional RLS and sliding window RLS (SRLS) algorithms, the proposed RLS algorithm applies the low computational DCD iterations without explicit division/ multiplication operations. And a real time forgetting factor updated by restoring the system noise from an error signal is designed in this algorithm, which can effectively improve the tracking performance and increase the strong robustness against process uncertainties. The simulation results show that the proposed RLS algorithm provides a lower MSE and stronger robustness than existing tracking RLS algorithms.

关键词： recursive least squares variable forgetting factor dichotomous coordinate descent strong tracking

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A Novel Method for Lithium-Ion Battery Online Parameter Identification Based on variable forgetting factor Recursive Least Squares

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ENERGIES 2018年第6期11卷 1358-1358页

作者： Lao, Zizhou Xia, Bizhong Wang, Wei Sun, Wei Lai, Yongzhi Wang, Mingwang Tsinghua Univ Grad Sch Shenzhen Shenzhen 518055 Peoples R China Sunwoda Elect Co Ltd Shenzhen 518108 Peoples R China

For model-based state of charge (SOC) estimation methods, the battery model parameters change with temperature, SOC, and so forth, causing the estimation error to increase. Constantly updating model parameters during battery operation, also known as online parameter identification, can effectively solve this problem. In this paper, a lithium-ion battery is modeled using the Thevenin model. A variable forgetting factor (VFF) strategy is introduced to improve forgetting factor recursive least squares (FFRLS) to variable forgetting factor recursive least squares (VFF-RLS). A novel method based on VFF-RLS for the online identification of the Thevenin model is proposed. Experiments verified that VFF-RLS gives more stable online parameter identification results than FFRLS. Combined with an unscented Kalman filter (UKF) algorithm, a joint algorithm named VFF-RLS-UKF is proposed for SOC estimation. In a variable-temperature environment, a battery SOC estimation experiment was performed using the joint algorithm. The average error of the SOC estimation was as low as 0.595% in some experiments. Experiments showed that VFF-RLS can effectively track the changes in model parameters. The joint algorithm improved the SOC estimation accuracy compared to the method with the fixed forgetting factor.

关键词： variable forgetting factor recursive least squares lithium-ion battery online parameter identification state of charge

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Recursive identification of the time-varying frequency parameters of a space solar power satellite using a robust subspace tracking method based on the variable forgetting factor

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AEROSPACE SCIENCE AND TECHNOLOGY 2023年第1期140卷

作者： Ni, Zhiyu Wu, Shunan Wu, Chenchen Shenyang Aerosp Univ Coll Aerosp Engn Shenyang 110136 Peoples R China Sun Yat Sen Univ Sch Aeronaut & Astronaut Shenzhen 518107 Peoples R China Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Shenyang 110016 Peoples R China

Considering changes in the structural configuration caused by the rotation of solar panel arrays in space solar power satellites, this study proposes an improved robust recursive subspace tracking algorithm to identify the time-varying modal frequency parameters of the system. In this algorithm, variable forgetting factor and robust weight coefficient are involved to improve the tracking adaptability of time-varying system and decrease the influence of impulsive disturbance. Then, the time-varying pseudo modal frequency parameters are identified using the recursive projection subspace tracking. In the numerical simulations, the influence of the different rotation speeds of the solar panel arrays on the frequency identification results is studied. The computational results for the different rotation speeds reveal that the tracking performance of the proposed algorithm is better than that of the original method for time varying system. Under the influence of impulsive disturbances, the relative values of the average relative error under the proposed algorithm are approximately 65.9-75.2% less than those under the original algorithm. Additionally, the results reveal that when the rotation speed of the solar panel arrays is time varying, the proposed algorithm achieves a better tracking performance than the conventional recursive methods by the adjustment of the variable forgetting factor. When the signal-to-noise ratio is 15 dB, the absolute and relative values of the average relative error under the proposed algorithm are less by approximately 6.4% and 64.8%, respectively, under the two reference methods with a fixed forgetting factor.& COPY;2023 Elsevier Masson SAS. All rights reserved.

关键词： Space solar power satellite Modal parameter identification Robust subspace tracking Time-varying system variable forgetting factor

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High Order Feedback-feedforward Iterative Learning Control Scheme with a variable forgetting factor

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2016年第3期13卷 95-95页

作者： Wang, Hongbin Dong, Jian Wang, Yueling Yanshan Univ Qinhuangdao Heibeisheng Peoples R China

In this work, we present a new iterative learning control (ILC) scheme for a class of non-linear systems with uncertain and non-repetitive disturbances, in order to achieve perfect tracking by proposing a high order feedback-feedforward ILC algorithm with a variable forgetting factor. The high order feedback-feedforward iterative learning controller can fully apply the previous control data to the system, which allows the system to track expectations more rapidly and precisely. Introducing a variable forgetting factor can weaken the former control output and its variance in the control law, while strengthening the robustness of the ILC. Through rigorous analyses, we demonstrate that uniform convergence of the state tracking error is guaranteed under this new ILC scheme. Simulation examples are also included to demonstrate the feasibility and effectiveness of the proposed learning controls.

关键词： High Order Iterative Learning Control Feedback-feedforward Control variable forgetting factor

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MODELING AND DETERIORATION DIAGNOSIS OF CATALYST ON VEHICLE BY variable forgetting factor-BASED ON-LINE IDENTIFICATION METHOD

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IFAC Proceedings Volumes 2006年第1期39卷 1364-1369页

作者： Seiji Hashimoto Yasushi Okada Hiroyuki Okuda Shuichi Adachi Shinji Niwa Mitsunobu Kajitani Gunma University Japan Utsunomiya University Japan Daihatsu Motor Co. Ltd. Japan

The majority of the conventional system purifying exhaust gas is composed of a three-way catalyst and an electronic fuel injection. However, harmful pollutants are increasingly emitted when the catalyst becomes aged. Therefore, it is necessary to detect the deterioration of the catalyst by means of on board diagnosis. In this paper, we propose a diagnosis method of the aged catalyst using recursive system identification method with variable forgetting factor. We focus on the parameter of identified model which represents characteristics of the catalyst, and possibility to describe the diagnosis of the aged catalyst.

关键词： deterioration diagnosis of catalyst system identification variable forgetting factor recursive least squares

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Adaptive coordinated motion control with variable forgetting factor for a dual-arm space robot in post-capture of a noncooperative target

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2019年第5期16卷

作者： Jiao, Chunting Yang, Jun Wang, Xueqian Liang, Bin Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Tsinghua Univ Grad Sch Shenzhen Shenzhen Peoples R China Shenzhen Lab Space Robot & Telesci Shenzhen Peoples R China

To overcome the problem of dynamics coupling between a space robot and a target satellite, this study introduces a new coordinated motion control approach with an adaptive filtering algorithm for a dual-arm free-floating space robot. Based on the reaction null space control scheme, one arm is used to complete the capture task and the other to counteract disturbance to the space base. However, when space robot captures a noncooperative target, the system may experience abrupt changes in dynamic parameters and output measurement noise, which can cause traditional control methods to achieve poor results in practical applications. Thus, an adaptive filtering algorithm with a variable forgetting factor is proposed to improve the tracking capabilities and robustness of the system. The convergence analysis is performed based on a Lyapunov function. The simulation results demonstrate the effectiveness of the proposed algorithm.

关键词： Coordinated motion control dual-arm space robot variable forgetting factor recursive least square

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Non-causal Iterative Learning Algorithm with variable forgetting factor

Non-causal Iterative Learning Algorithm with Variable Forget...

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32nd Chinese Control Conference (CCC)

作者： Wang Hong-rui Wang Na Wang Hong-bin Wang Yue-ling Wang Chao Yanshan Univ Qinhuangdao 066004 Peoples R China

ISBN: (纸本)9781479900305

A non-causal iterative learning algorithm (ILC) with variable forgetting factor is proposed for a class of nonlinear system with initial error. This algorithm eliminates the limitation that the initial state should be consistent with the desired one or fixed in the learning process of conventional algorithms. The error information of future time in previous iteration is used to correct the control law in the non-causal ILC algorithm, so the controller has the function of compensating future disturbances ahead of time. The introduced variable forgetting can adjust the control input effectively and do filtering in the iteration direction, thus it accelerates the convergent speed and weakens the influence on the convergence which is generated from the indeterminacy. Compared with the conventional PD-type algorithm, this algorithm not only increases information content but also avoids using the derivative of error signals. From the perspective of execution, it is easier to measure, and the noise level is greatly reduced. At last, the new algorithm presented in the paper is used in the simulation test of manipulator trajectory tracking, and the result illustrates effectiveness and real-time performance.

关键词： Initial error variable forgetting factor Non-causal iterative learning Manipulator trajectory tracking

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Polar Motion Prediction Based on Adaptive Filtering of variable forgetting factor

Polar Motion Prediction Based on Adaptive Filtering of Varia...

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Forum on Cooperative Positioning and Service (CPGPS)

作者： Jia, Song Xu, Tianhe Yang, Honglei Changan Univ Sch Geol Engn & Surveying Xian 710054 Shanxi Peoples R China Shandong Univ Inst Space Sci Weihai 246209 Shandong Peoples R China State Key Lab Geoinformat Engn Xian 710054 Shanxi Peoples R China

ISBN: (纸本)9781509050222

The Polar Motion (PM) is the important parameter of Earth Rotation Parameters (ERP), and the high-precision prediction of PM plays a key role in the applications of autonomous orbit determination, the geodetic survey, navigation and aviation. In this paper, a modified algorithm is proposed to improve the PM prediction accuracy based on combination of Least Square and Autoregressive Model (LS+AR). An adaptive filtering of variable forgetting factor is developed to amend the LS fitting terms and predict extrapolations, which is named LS+AR+AF algorithm. The numerical results show that LS+AR+AF algorithm can significantly enhance the prediction accuracy of PM, especially for the long-term perdition. The accuracy improvement of 360-day prediction for PM X component, PM Y component and total PM can reach 30.66%, 28.19% and 29.59% respectively, when using LS+AR+AF algorithm.

关键词： Polar Motion Prediction variable forgetting factor Adaptive Filtering

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