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Flight Control System improvement based on a software sensor derived from Partial least squares algorithm

IFAC Proceedings Volumes 2012年第20期45卷 1041-1046页

作者： Florian Cazes Marie Chabert Corinne Mailhes Patrice Michel Philippe Goupil Rémy Dayre Hervé Le-Berre EYCCC Flight Control System AIRBUS Operations S.A.S. Toulouse France University of Toulouse/IRIT/ENSEEIHT TéSA Lab

Global aircraft optimization is a main concern for future and upcoming programs. In particular, great research efforts are dedicated to Electrical Flight Control Systems (EFCS). Obviously, their reliability increases with the redundancy of the flight parameter sensors. However, physical redundancy, obtained by increasing the number of sensors, penalizes the aircraft weight and cost. This paper proposes a sensor failure detection method based on analytic redundancy. The flight parameter of interest is modelled as a linear function of independent sensor measurements on a sliding observation window. The Partial least squares (PLS) algorithm is used to estimate regression coefficients on this window. The PLS computes the solution via an iterative processing, and thus can be implemented in the flight control computer for a real time use. Two different failure detection strategies based on the behaviour of the regression coefficients are proposed. Simulation results show that the proposed method leads to robust detections.

关键词： Aircraft control command and control systems detection algorithm failure detection least squares algorithm sensor failure partial least squares

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Auxiliary model nonlinear innovation least squares algorithm for identification ship 4-DOF via full-scale test data

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SCIENTIFIC REPORTS 2024年第1期14卷 1-11页

作者： Song, Chunyu Li, Yinfu Sui, Jianghua Dalian Ocean Univ Nav & Ship Engn Coll Dalian 116023 Peoples R China

For the ship motion with large inertia coupled system identification modeling inaccuracy, the ship scale effect and the existence of partial unmeasured ship data problems. In this paper, an auxiliary model nonlinear innovation least squares identification algorithm is proposed. The new algorithm uses the output of the auxiliary model instead of the unmeasurable variables in the full-scale test data of the ship, and optimizes the error using the tangent function. Compared with the existing algorithm, the error of the improved algorithm decreases with the increase of time and continuously approaches to zero, which greatly improves the identification accuracy and convergence efficiency. The results show that the improved algorithm has significant identification accuracy and reliability. The identification method designed in this paper can be applied to the field of ship intelligent navigation engineering.

关键词： Auxiliary model Nonlinear innovation least squares algorithm Tangent function Full-scale test

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Adaptive Control of Nonlinear Systems Using Neural Network and least squares algorithm

Adaptive Control of Nonlinear Systems Using Neural Network a...

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1996年中国控制会议

作者： Ruisheng Li Institute of Systems Science Chinese Academy of Sciences Beijing, 100080, P. R. of China

＜正＞For a class of nonlinear discrete-time systems, a local convergence result that the tracking error between system output and reference command converges to a bounded ball is established by use of the neural network and the least squares （LS） algorithm with dead zone. The assumption, used in [1], that the neural network approximates the true system with accuracy ε over a compact set is not needed in this paper. No assumptions of the nonlinear functions being bounded away from zero and of the system being minimum phase are made in comparison with [1].

关键词： Nonlinear systems neural network least squares algorithm adaptive control

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Partitioned Hierarchical alternating least squares algorithm for CP tensor decomposition

Partitioned Hierarchical alternating least squares algorithm...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Anh-Huy Phan Petr Tichavsky Andrzej Cichocki Brain Science Institute RIKEN Wakoshi Japan Institute of Information Theory and Automation P.O.Box 18 182 08 Prague 8 Czech Republic

ISBN: (纸本)9781509041183

Canonical polyadic decomposition (CPD), also known as PARAFAC, is a representation of a given tensor as a sum of rank-one tensors. Traditional method for accomplishing CPD is the alternating least squares (ALS) algorithm. This algorithm is easy to implement with very low computational complexity per iteration. A disadvantage is that in difficult scenarios, where factor matrices in the decomposition contain nearly collinear columns, the number of iterations needed to achieve convergence might be very large. In this paper, we propose a modification of the algorithm which has similar complexity per iteration as ALS, but in difficult scenarios it needs a significantly lower number of iterations.

关键词： CANDECOMP/PARAFAC tensor decomposition partitioning ALS iterative methods Tensor decomposition least squares algorithm Amyotrophic Lateral Sclerosis ADVANCED LIGHT SOURCE ambient light sensor CPD gene algorithms complexity classes

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Implementation of least squares algorithm Based on Dynamic Threshold in Beidou Satellite Positioning

Implementation of Least Squares Algorithm Based on Dynamic T...

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Image Processing, Electronics and Computers (IPEC), Asia-Pacific Conference on

作者： Guoping Lei Xiuying Luo Zhiyuan Liu Minlu Dai School of Electronic & information Engineering Chongqing Three Gorges University Chongqing Wanzhou China

ISBN: (数字)9781728160672

ISBN: (纸本)9781728160689

At present, China's Beidou Navigation Satellite System (BDS) has been successfully applied in all walks of life, entering a period of rapid development. The least squares algorithm is often used to solve problems in the application of BDS, while the quaternary nonlinear equation set is used for solving the positioning and timing issues. The application of least square algorithm in the PVT solution of Beidou satellite navigation is to solve a set of quaternion nonlinear equations. The solution of the least squares is an iterative process, but if the convergence threshold is too small in the iterative process, it may lead to iteration without convergence. In order to improve the failure of convergence caused by too small convergence threshold in the original algorithm, a least squares algorithm based on dynamic threshold was proposed in this paper. The proposed algorithm could continue iteration until convergence by increasing the system's dynamic threshold. At the same time, the proposed algorithm could also change the satellite combination in time, so as to conduct the positioning and calculation again over the threshold value that increases to a fixed value but fails to converge. And the simulation results show that if the threshold is set too small, it can converge well. As a result, the algorithm not only effectively prevents the program failure in Beidou satellite positioning caused by the convergence failure of algorithm iteration, but also greatly improves the stability and reliability of the system program operation.

关键词： eidou satellite positioning algorithm least squares algorithm dynamic threshold convergence Converge least squares algorithm iterative methods Iterative Process algorithms Patient Positioning System of equations least squares method

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CleanEMG - Power line interference estimation in sEMG using an adaptive least squares algorithm

CleanEMG - Power line interference estimation in sEMG using ...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： G. D. Fraser A. D. C. Chan J. R. Green N. Abser D. MacIsaac Department of Systems & Computer Engineering Carleton University Department of Electrical & Computer Engineering University of New Brunswick

ISBN: (纸本)9781424441211

This paper presents an adaptive least squares algorithm for estimating the power line interference in surface electromyography (sEMG) signals. The algorithm estimates the power line interference, without the need for a reference input. Power line interference can be removed by subtracting the estimate from the original sEMG signal. The algorithm is evaluated with simulated sEMG based on its ability to accurately estimate power line interference at different frequencies and at various signal-to-noise ratios. Power line estimates produced by the algorithm are accurate for signal-to-noise ratios below 15 dB (SNR estimation error at 15 dB is 14.7995 dB ± 1.6547 dB).

关键词： SEMA5B gene signal to noise ratios Power cables least squares algorithm algorithms dubnium decibel Power (Psychology) interference interference

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KINEMATIC CALIBRATION OF PARALLEL ROBOTS BASED ON least squares algorithm

KINEMATIC CALIBRATION OF PARALLEL ROBOTS BASED ON LEAST SQUA...

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2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

作者： DA-YONG YU College of Automation Harbin Engineering University Harbin 150001 China

In the application of parallel robots, it is necessary to calibrate the kinematic parameters and improve the pose accuracy for accurate task performance. To do so, an error model is developed that takes into consideration all the kinematic parameter errors due to imprecision manufacturing and assembly. On the basis of the error model and the experimental data for the accuracy of the parallel robot, a least squares algorithm is proposed and tested. Simulations and experiments are presented to show the effectiveness of the proposed method.

关键词： Parallel robot Kinematic calibration least squares algorithm Error modeling Pose accuracy

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An Experimental Analysis on Scalable Implementations of the Alternating least squares algorithm

An Experimental Analysis on Scalable Implementations of the ...

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Federated Conference on Computer Science and Information Systems

作者： Dania Meira Jose Viterbo Flavia Bernardini Data Science Retreat/EyeEm Institute of Computing Fluminense Federal University

ISBN: (纸本)9781538623718

The use of the latent factor models technique overcomes two major problems of most collaborative filtering approaches: scalability and sparseness of the user's profile matrix. The most successful realizations of latent factor models are based on matrix factorization. Among the algorithms for matrix factorization, alternating least squares (ALS) stands out due to its easily parallelizable computations. In this work we propose a methodology for comparing the performance of two parallel implementations of the ALS algorithm, one executed with MapReduce in Apache Hadoop framework and another executed in Apache Spark framework. We performed experiments to evaluate the accuracy of generated recommendations and the execution time of both algorithms, using publicly available datasets with different sizes and from different recommendation domains. Experimental results show that running the recommendation algorithm on Spark framework is in fact more efficient, once it provides in-memory processing, in contrast to Hadoop's two-stage disk-based MapReduce paradigm.

关键词： Scalability matrix decomposition algorithms least squares algorithm execution time ISOCHRONOUS CYCLOTRONS collaborative filtering Amyotrophic Lateral Sclerosis ADVANCED LIGHT SOURCE ambient light sensor Frameworks infinitesimal analysis

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Research on Maps Building for Mobile Robot Based on least squares algorithm Using Laser Scanner

Research on Maps Building for Mobile Robot Based on Least Sq...

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2013先进智能制造技术发展研讨会

作者： Guo Tongying Liu Wei Du Zhenjun Guo Tongyan Wang haichen Shenyang Jianzhu University Shenyang110168China SIASUN Robot & Automation Co. LtdShenyang110168China Shenyang Daneng Electrical Technology Co. Ltd110015China

In this paper,the laser sensor is using to detect environmental *** problems of maps building of mobile robot are researched in the indoor structured *** squares algorithm is adopted to build local maps,and a method of dynamic partition are presented based on the distance of adjacent points and *** accomplishing the self-localization and route planning of the mobile robot in the indoor environment lacking of the prior map,this paper present the method of the global map building based on the match of the feature points and the characteristic *** experiments show the methods presented are effective and practicable.

关键词： Mobile robot maps building indoor environment least squares algorithm

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Recursive least squares parameter estimation algorithm for dual-rate sampled-data nonlinear systems

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NONLINEAR DYNAMICS 2014年第2期76卷 1327-1334页

作者： Li, Xiangli Zhou, Lincheng Sheng, Jie Ding, Ruifeng Jiangnan Univ Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Peoples R China Jiangsu Coll Informat Technol Wuxi 214153 Peoples R China Univ Washington Inst Technol Tacoma WA 98402 USA Jiangnan Univ Sch Internet Things Engn Wuxi 214122 Peoples R China

This paper focuses on the identification problem of Hammerstein systems with dual-rate sampling. Using the key-term separation principle, we derive a regression identification model with different input updating and output sampling rates. To solve the identification problem of the dual-rate Hammerstein systems with the unmeasurable variables in the information vector, an auxiliary model-based recursive least squares algorithm is presented by replacing the unmeasurable variables with their corresponding recursive estimates. Convergence properties of the algorithm are analyzed. Simulation results show that the proposed algorithm can estimate the parameters of a class of nonlinear systems.

关键词： Hammerstein system least squares algorithm Dual-rate sampling Key-term separation principle Auxiliary model identification idea

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