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Kernel-based regularization least squares algorithm for nonlinear time-delayed systems using self-organizing maps

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2023年第8期33卷 4602-4615页

作者： Zhang, Zili Zhang, Yanxin Chen, Jing Guo, Liuxiao Jiangnan Univ Sch Sci Wuxi 214122 Peoples R China

This article proposes a kernel-based regularization least squares algorithm for nonlinear time-delayed systems with unknown structure. Since the structure and the time-delay of the model are unknown, a model pool constituted of several Volterra series is constructed with the aim of approximating the nonlinear model which has different time-delays. Then, a self-organizing maps method and a kernel-based regularization method are interactively used to update the time-delay and parameters. Compared with the traditional algorithms, the proposed algorithm has no limitation on the nonlinear model, and can describe the dynamics of the nonlinear model with a simple structure. Finally, simulation examples are given to show the effectiveness of the algorithm.

关键词： kernel-based regularization method least squares algorithm nonlinear time-delayed systems self-organizing maps Volterra series

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Application of the least squares algorithm to the observer design for linear time-varying systems

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 1999年第9期44卷 1742-1745页

作者： Chen, MS Yen, JY Natl Taiwan Univ Dept Mech Engn Taipei 10764 Taiwan

In this paper, it is shown that the least squares algorithm with covariance reset, which is originally developed for the purpose of constant parameter identification, can be effectively applied to the observer design for a general linear time-varging system. The new observer successfully avoids many of the disadvantages of other time-varying observers, such as slow convergence rate, heavy computation load, high amplification of measurement noise, and the inapplicability to systems with time-varying observability indexes or discontinuous parameter variations.

关键词： least squares algorithm linear time-varying system observer persistent excitation uniform observability

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A finite-data-window least squares algorithm with a forgetting factor for dynamical modeling

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APPLIED MATHEMATICS AND COMPUTATION 2007年第1期186卷 184-192页

作者： Ding, Feng Xiao, Yongsong So Yangtze Univ Control Sci & Engn Res Ctr Wuxi 214122 Peoples R China

In this paper, we extend the forgetting factor least squares and finite-data-window least squares identification algorithms, develop a finite-data-window least squares algorithm with a forgetting factor for dynamical system modeling, derive its recursive version, and also give its simplified form. We illustrate the advantages of the proposed algorithm with simulation examples. (c) 2006 Elsevier Inc. All rights reserved.

关键词： recursive identification parameter estimation least squares algorithm forgetting factor finite-data window

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Modified least squares algorithm incorporating exponential resetting and forgetting - Comment

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INTERNATIONAL JOURNAL OF CONTROL 1997年第4期68卷 947-953页

作者： Han, Z Frank, PM Department of Measurement and Control University of Duisburg Duisburg D-47048 Bismarckstr. 81 BB Germany

It has been proved that one constraint on the constant selection of the ERFA of Salgado et al. (1988) is not necessary. Instead of this, one new constraint is presented, which is looser in the selection of the constants. Moreover, under the new constraint the ERFA converges more quickly. Simulations are also given to verify the new constraint.

关键词： least squares algorithm forgetting Comment Constraint Constants algorithms Exponential

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Recursive least squares algorithm for nonlinear systems with piece-wise linearities

Recursive least squares algorithm for nonlinear systems with...

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2nd International Conference on Sensors, Measurement and Intelligent Materials (ICSMIM 2013)

作者： Yuan, Huijuan Wuxi Profess Coll Sci & Technol Wuxi 214028 Peoples R China

ISBN: (纸本)9783037859711

This paper proposes a recursive least squares algorithm for nonlinear systems with piece-wise linearities. By using a switching function, the model of the nonlinear systems be changed to an identification model, then based on the derived model, a recursive least squares algorithm is provided to estimate all the unknown parameters of the systems. An example is provided to show the effectiveness of the proposed algorithm.

关键词： Piece-wise linearity least squares algorithm Parameter estimation Nonlinear system

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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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7th International Conference on Machine Learning and Cybernetics

作者： Yu, Da-Yong Harbin Engn Univ Coll Automat Harbin 150001 Peoples R China

ISBN: (纸本)9781424420957

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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Voltage dip detection based on an efficient least squares algorithm for D-STATCOM application

Voltage dip detection based on an efficient least squares al...

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CES/IEEE 5th International Power Electronics and Motion Control Conference

作者： Manmek, Thip Mudannayake, Chathura P. Grantham, Colin Univ New S Wales Sch Elect Engn & Telecommun Sydney NSW Australia

ISBN: (纸本)1424404487

This paper presents a fast voltage dip detection technique that is suitable for use in a Distribution Static Synchronous Compensator (D-STATCOM) in compensating balanced dip and unbalanced voltages in power systems. The proposed voltage dip detection method is based on an efficient least squares algorithm which offers structural simplicity and less computational complexity while maintaining dynamic performance and accuracy. It is also robust against distortions present in voltage waveforms. The proposed method extracts the active and reactive parts of the positive- and negative-sequence component for generating reference values of current that need to be injected into the point of connection D-STATCOM in order to compensate the voltage errors. The effectiveness of the voltage dip detection method in the D-STATCOM application has been verified by simulation results.

关键词： Distribution Static Synchronous Compensator voltage dip detection least squares algorithm

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Real-time Identification of a servo system via a Robust least squares algorithm 10

Real-time Identification of a servo system via a Robust Leas...

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10th International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Rivera, Abraham Garrido, Ruben CINVESTAV IPN Dept Automat Control Mexico City DF Mexico

ISBN: (纸本)9798350373981;9798350373974

This work reports the parameter identification of a servo system through a novel Robust least squares algorithm with Variable Forgetting Factor (ROLSVFF), which is aimed to the identification of time-varying parameters in perturbed dynamic systems. Real-time experiments allow the evaluation of the proposed identification algorithm, and three excitation signals are tested including a chaotic signals generated by a Duffing system. The ROLSVFF algorithm is compared against the classic Gradient algorithm, and the integral of the absolute value of the Parameter Estimate Derivative (IAPED) allows evaluating the smoothness of the parameter estimates. The experimental outcomes show that the value of the IAPED index remains in the range 70-100 for the ROLSVFF algoritm whereas in the case of the Gradient algorithm the corresponding range is 50-18500, and the value of the IAPED index strongly depends on the excitation signal.

关键词： Parameter identification time-variant parameters gradient algorithm least squares algorithm forgetting factor online parameter identification dc servosystem

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Adaptive Filter Utilizing Recursive least squares algorithm with Adaptive Forgetting Factor

Adaptive Filter Utilizing Recursive Least Squares Algorithm ...

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作者： Javaid, Haseeb Texas A&M University - Kingsville

学位级别：M.S., Master of Science/Master of Surgery

In the context of adaptive filtering, the recursive least-squares (RLS) are a very popular algorithm, especially for its fast convergence rate. The most important parameter of this algorithm is the forgetting factor. It is well-known that a constant value of this parameter leads to a compromise between fine tuning and tracking. The purpose of this project is to integrate audio signal with a wind noise and design an efficient low pass adaptive RLS filter that can attenuate wind noise at an adaptive sequence. Adaptive RLS filter has been designed using MATLAB, where sampled signals have been replaced with audio signal and wind noise at adaptive frequency. We calibrated different frequency and tested the signals at adaptive forgetting factor and noise variants. Using an adaptive forgetting factor approach, aiming to better compromise between the performance criteria of the RLS algorithm. Also, we propose a practical solution to estimate the power of the system output noise. After receiving desired results, we computed signal-to-noise ratio, SNR at the input and output of the adaptive filter.

关键词： least squares algorithm Forgetting factor Audio signal Recursive least-squares Wind noise

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

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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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