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New adaptive robust Hc control of smart structures using synchrosqueezed wavelet transform and recursive least-squares algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年 116卷

作者： Li, Zhijun Adeli, Hojjat Xian Technol Univ Sch Civil & Architecture Engn Xian 710032 Peoples R China Ohio State Univ Columbus OH 43220 USA

Two kinds of uncertainties, one due to the dynamic earthquake loads with a wide frequency band and the other due to structural parameters exist in large and complex real-life structures. Most existing control algorithms consider only one of them, resulting in difficulty to guarantee necessary control performance for large complex structures, such as better vibration suppression on structural peak response and robustness performance. Con-sidering the two uncertainties simultaneously, in this paper, a new adaptive robust Hc control methodology is presented for vibration control of structures through adroit integration of synchrosqueezed wavelet transform (SWT) and recursive least-squares (RLS) algorithm. The robust Hc control is more effective than the traditional LQR/LQG control in terms of the stability and robustness of the control system. The external excitation signal from ground sensors is filtered by a low-pass filter based on SWT and then inputted into the filtered-x RLS adaptive controller. The effectiveness, accuracy, and computational efficiency of the new adaptive control method is demonstrated using a 76-story wind-excited benchmark super high-rise building structure and a 24-story shear-wall building with an active tuned mass damper (ATMD) system on the top floor. Compared with the existing linear quadratic Gaussian control algorithm, the wavelet-hybrid feedback-least mean square algorithm and the robust Hc control algorithm, the simulation results show that the control effect and robust performance indexes of the structure are increased by 5%-35% and 5%-25%, respectively, using the new control methodology.

关键词： Adaptive hybrid feedback control RobustHc control Synchrosqueezed wavelet transform recursive least-squares algorithm

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Nonlinear acoustic echo cancellation using low-complexity low-rank recursive least-squares algorithms

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SIGNAL PROCESSING 2024年 225卷

作者： Patel, Vinal Bhattacharjee, Sankha Subhra Jensen, Jesper Rindom Christensen, Graesboll Benesty, Jacob ABV IIITM Dept Elect & Elect Engn Gwalior 474015 Madhya Pradesh India Aalborg Univ Dept Elect Syst Aalborg Denmark Univ Quebec INRS EMT Montreal PQ Canada

Adaptive exponential functional link networks (AEFLN) are a type of linear-in-the-parameter nonlinear filters, which have shown enhanced modeling capability for nonlinear systems. However, the convergence speed of traditional AEFLN is generally slow for long impulse responses, hence, AEFLN trained using recursive least-squares becomes an attractive choice to achieve faster convergence. Moreover, huge computational burden of RLS makes it unsuitable in practical applications like echo cancellation. While low complexity versions of RLS are widely available in literature, they still suffer from low convergence speed. To address this issue, we propose a nonlinear acoustic echo cancellation (NAEC) system using AEFLN-RLS, based on the nearest Kronecker product (NKP) decomposition and low-rank approximation technique, which not only reduces computational complexity but also achieves improved convergence speed (especially tracking). To further improve the echo cancellation performance in non-stationary conditions, a variable regularization approach based NKP-AEFLN-RLS system is also proposed. To also reduce the computational complexity further, dichotomous coordinate descent (DCD) updates are incorporated into the proposed NKP-AEFLN-RLS NAEC system and its variable regularization version. Experimental results show the effectiveness of the proposed algorithms, with the variable regularized version of the algorithm using DCD iterations showing the best compromise between convergence capability and lower computational complexity.

关键词： Nonlinear echo cancellation Low-rank approximation Nearest Kronecker product recursive least-squares algorithm

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Dynamic decoupling and compensation based on the inverted network technique and experiment research for multi-axis hydraulic road simulator

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2024年第24期238卷 11309-11322页

作者： Wang, Xiao Hu, Fanglin Yao, Longyuan Li, Kun Han, Junwei Shandong Jianzhu Univ Sch Mech & Elect Engn Room 529Sci & Technol Bldg Jinan 250101 Shandong Peoples R China Jinan Municipal Bldg Safety Appraisal Testing Ctr Urban Renewal Engn Inst Jinan Shandong Peoples R China Harbin Inst Technol Sch Mechatron Engn State Key Lab Robot & Syst Harbin Peoples R China

This paper presents a dynamic decoupling and compensation approach to eliminate channel interaction for a multi-axis hydraulic road simulator. Misalignment of the concentrated moving mass centroid on the entire test bench with the control point and inconsistencies in actuator dynamics leads to dynamic coupling, which deteriorates the motion performance of the system. To solve the problem, an inverted decoupling and compensation network based on an identification model is proposed. Firstly, the system is modeled using the recursive extended least square (RELS) identification method. In the feedforward compensation modeling process, the steady-state inverse is obtained using zero-magnitude error technology control (ZMETC). In addition, a finite impulse response (FIR) filter with an adaptive algorithm is employed to reduce the adverse impact of the modeling error. The proposed decoupling strategy is validated by conducting experiments using a multi-axis hydraulic road simulator. The experimental results indicate that the designed compensator can effectively decrease the cross-coupling of multi-input and multi-output (MIMO) systems. The suggested approach is also applicable to applications that necessitate the elimination of interactions between different control variables.

关键词： Multi-axis hydraulic road simulator inverted decoupling feedforward compensation ZMETC FIR filter recursive least-squares algorithm

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least-squares algorithms for Complex-Valued Blind Source Separation

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2024年第4期43卷 2608-2625页

作者： E, Jianwei Lu, Zeyi Guangxi Minzu Univ Coll Math & Phys Ctr Appl Math Guangxi Nanning 530006 Peoples R China

Blind source separation (BSS), as a digital signal processing approach, focuses on estimating the underlying source signals from their linear mixtures without any prior information about the source signals and mixing matrix. Conventional methods for the BSS, however, are incapable of separating the complex-valued source signals. By leveraging the negative conjugate gradient to minimize the least mean square error reconstruction (LMSER) principle in complex domain, this paper proposes a collection of least-squares algorithms for complex-valued BSS (CBSS), including least-mean square (LMS)-type algorithms and recursive least-squares (RLS)-type algorithms. We demonstrate the availability of the proposed algorithms in both circular and non-circular source signals separation. Especially, the RLS algorithm for the CBSS without prewhitening is superior in cross-talking criterion to the others, as verified by computer simulations on artificial source signals.

关键词： Complex-valued blind source separation Conjugate gradient least-mean squares algorithm recursive least-squares algorithm

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Unsupervised robust recursive least-squares algorithm for impulsive noise filtering

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Science China(Information Sciences) 2013年第4期56卷 203-212页

作者： CHEN Jie MA Tao CHEN WenJie PENG ZhiHong School of Automation Beijing Institute of Technology Key Laboratory of Complex System Intelligent Control and Decision(Beijing Institute of Technology) Ministry of Education

A robust recursive least-squares(RLS) adaptive filter against impulsive noise is proposed for the situation of an unknown desired *** minimizing a saturable nonlinear constrained unsupervised cost function instead of the conventional least-squares function,a possible impulse-corrupted signal is prevented from entering the filter’s weight updating ***,a multi-step adaptive filter is devised to reconstruct the observed "impulse-free" noisy sequence,and whenever impulsive noise is detected,the impulse contaminated samples are replaced by predictive *** on simulation and experimental results,the proposed unsupervised robust recursive least-square adaptive filter performs as well as conventional RLS filters in "impulse-free" circumstances,and is effective in restricting large disturbances such as impulsive noise when the RLS and the more recent unsupervised adaptive filter fails.

关键词： unsupervised adaptive filtering impulsive noise suppression recursive least-squares algorithm

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Joint Semi-Blind Self-Interference Cancellation and Equalisation Processes in 5G QC-LDPC-Encoded Short-Packet Full-Duplex Transmissions

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SENSORS 2022年第6期22卷 2204-2204页

作者： Bao Quoc Vuong Gautier, Roland Hien Quang Ta Lap Luat Nguyen Fiche, Anthony Marazin, Melanie Univ Brest Lab STICC CNRS CS 938376 Ave Gorgeu F-29238 Brest 3 France Int Univ Sch Elect Engn Ho Chi Minh City 700000 Vietnam Vietnam Natl Univ Ho Chi Minh City 700000 Vietnam

The paper proposes a joint semi-blind algorithm for simultaneously cancelling the self-interference component and estimating the propagation channel in 5G Quasi-Cyclic Low-Density Parity-Check (QC-LDPC)-encoded short-packet Full-Duplex (FD) transmissions. To avoid the effect of channel estimation processes when using short-packet transmissions, this semi-blind algorithm was developed by taking into account only a small number (four at least) pilot symbols, which was integrated with the intended information sequence and used for the feedback loop of the estimation of the channels. The results showed that this semi-blind algorithm not only achieved nearly optimal performance, but also significantly reduced the processing time and computational complexity. This semi-blind algorithm can also improve the performances of the Mean-Squared Error (MSE) and Bit Error Rate (BER). The results of this study highlight the potential efficiency of this joint semi-blind iterative algorithm for 5G and Beyond and/or practical IoT transmission scenarios.

关键词： full-duplex digital self-interference cancellation 5G LDPC codes short-packet transmission semi-blind channel estimation recursive least-squares algorithm sum of product algorithm

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A Modified RLS algorithm for ICA with Weighted Orthogonal Constraint

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2020年第6期39卷 3046-3060页

作者： Jianwei, E. Ye, Jimin Xidian Univ Sch Math & Stat Xian 710071 Shaanxi Peoples R China

Independent component analysis (ICA), as an important data processing technique, is widely employed in many areas. The objective of the ICA is to recover independent components from observed signals. Several algorithms, such as equivariant adaptive separation via independence algorithm, least-mean-square (LMS)-type algorithms and recursive least-squares (RLS)-type learning rules, are proposed to solve the ICA problem. In the present paper, a modified RLS algorithm for ICA with weighted orthogonal constraint is developed to implement source separation based on the local convergence analysis of the available algorithm. Comparative experiment results demonstrate that the proposed algorithm is better than existing learning rules in the aspect of the accuracy of separation and stability.

关键词： Independent component analysis least-mean-square algorithm recursive least-squares algorithm Weighted orthogonal constraint

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Efficient recursive least-squares algorithms for the identification of bilinear forms

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DIGITAL SIGNAL PROCESSING 2018年 83卷 280-296页

作者： Elisei-Iliescu, Camelia Stanciu, Cristian Paleologu, Constantin Benesty, Jacob Anghel, Cristian Ciochina, Silviu Univ Politehn Bucuresti 1-3Luliu Maniu Blvd Bucharest 061071 Romania Univ Politehn Bucuresti Telecommun Dept 1-3Luliu Maniu Blvd Bucharest 061071 Romania Univ Politehn Bucuresti Telecommun Dept Fac Elect Telecommun & Informat Technol 1-3Luliu Maniu Blvd Bucharest 061071 Romania Univ Quebec INRS EMT Montreal PQ H5A 1K6 Canada

Due to its fast convergence rate, the recursive least-squares (RLS) algorithm is very popular in many applications of adaptive filtering, including system identification scenarios. However, the computational complexity of this algorithm represents a major limitation in applications that involve long filters. Moreover, when the parameter space becomes large, the system identification problem is more challenging and the adaptive filters should be able to cope with this aspect. In this paper, we focus on the identification of bilinear forms, where the bilinear term is defined with respect to the impulse responses of a spatiotemporal model. From this perspective, the solution requires a multidimensional adaptive filtering technique. Recently, the RLS algorithm tailored for bilinear forms (namely RLS-BF) was developed for this purpose. In this framework, the contribution of this paper is mainly twofold. First, in order to reduce the computational complexity of the RLS-BF algorithm, two versions based on the dichotomous coordinate descent (DCD) method are proposed;due to its arithmetic features, the DCD algorithm represents one of the most attractive alternatives to solve the normal equations. However, in the bilinear context, we need to consider the particular structure of the input data and the additional related challenges. Second, in order to improve the robustness of the RLS-BF algorithm in noisy environments, a regularized version is developed, together with a method to find the regularization parameters, which are related to the signal-to-noise ratio (SNR). Furthermore, using a proper estimation of the SNR, a variable-regularized RLS-BF algorithm is designed and two DCD-based low-complexity versions are proposed. Due to their nature, these variable-regularized algorithms have good robustness features against additive noise, which make them behave well in different noisy condition scenarios. Simulation results indicate the good performance of the proposed low-comple

关键词： Adaptive filter Bilinear forms recursive least-squares algorithm Dichotomous coordinate descent Variable regularization System identification

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Regularized recursive least-squares algorithms for the Identification of Bilinear Forms 13

Regularized Recursive Least-Squares Algorithms for the Ident...

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13th International Symposium on Electronics and Telecommunications (ISETC)

作者： Elisei-Iliescu, Camelia Paleologu, Constantin Stanciu, Cristian Anghel, Cristian Ciochina, Silviu Benesty, Jacob Univ Politehn Bucuresti Bucharest Romania Univ Quebec INRS EMT Montreal PQ Canada

ISBN: (纸本)9781538659250

The system identification problem is more challenging when the parameter space becomes large. This paper addresses the identification of bilinear systems based on the regularized recursive least-squares algorithm. Here, the bilinear term is defined with respect to the impulse responses of a spatiotemporal model. In order to improve the robustness of the algorithm in noisy environments, a variable-regularized version is also developed, where the regularization parameters are adjusted using an estimation of the signal-to-noise ratio. Simulation results outline the appealing features of these algorithms.

关键词： Adaptive filter bilinear forms recursive least-squares algorithm variable regularization system identification

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recursive least Square Estimation Approach to Real-Time Parameter Identification in Li-ion Batteries

Recursive Least Square Estimation Approach to Real-Time Para...

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IEEE Electrical Power and Energy Conference (EPEC)

作者： Raihan, Sheikh Arif Balasingam, Balakumar Univ Windsor Dept Elect & Comp Engn Windsor ON Canada

ISBN: (纸本)9781728134062

In this paper, we consider the problem of equivalent circuit model (ECM) parameter identification in Li-ion batteries. Accurate estimation of the ECM parameters is critical for the safety, efficiency and reliability of the battery system. Existing approaches to solve this problem depend on information and parameters, such as, battery capacity, state-of-charge (SOC) and open circuit voltage (OCV) characterization parameters. Such reliance on other parameters makes the ECM identification less accurate. In this paper, we present a real-time approach to ECM identification. The proposed approach relies only on the measured voltage across the battery terminal and current through the battery. Also, the proposed approach is unaffected by the amount of hysteresis in the battery. Further, robustness in parameter identification is achieved through the inclusion of the measurement noise covariance matrix. The proposed algorithm was tested on simulated as well as real world battery data and found to be accurate within 1% uncertainty.

关键词： Battery management system battery fuel gauge state of charge battery impedance equivalent circuit model recursive least-squares algorithm

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