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12 IEEE Int C Elect Energy Env Communications Computer Control

作者： Bhardwaj, Akshay Agarwal, Vijyant Parthasarathy, Harish Netaji Subhas Inst Technol Delhi Dept Mfg Proc & Automat Engn Delhi India Netaji Subhas Inst Technol Delhi Dept Elect & Commun Engn Delhi India

ISBN: (纸本)9781467365406

In this research, we have designed and implemented recursive least squares (RLS) algorithm in master slave tracking on Geomagic (R) Touch (TM) (Phantom Omni) haptic device. RLS algorithm enables us to achieve optimal tracking in a tele-operation system in which the system parameters vary with time and the noise is weakly non-stationary. In our previous work on teleoperation, we employed Widrow's least mean square algorithm instead of RLS algorithm and achieved satisfactorily high tracking accuracy. There, we employed instantaneous errors to update filter coefficients and hence slave positions. This study initiated with the idea that if we account all or some of the previous errors in updating filter coefficients and thus reducing current error, we might he probably able to achieve even higher tracking accuracy than that achieved with WLMS. Therefore, in order to understand this influence of older errors on tracking accuracy, we have applied RLS algorithm with forgetting factor. The use of forgetting factor in the least squares algorithm enables us to base our tracking on different weights of past errors that further helps us in understanding this influence at a broader level.

关键词： Master slave teleoperation nonlinear dynamical system adaptive filter recursive least squares algorithm Kalman Gain matrix linear time varying systems

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Direct Inverse Hysteresis Compensation of Piezoelectric Actuator Using recursive least squares Method 9

Direct Inverse Hysteresis Compensation of Piezoelectric Actu...

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IEEE International Conference on Manipulation, Manufacturing and Measurement on the Nanoscale (IEEE 3M-NANO)

作者： Duan, Heng Jia, Rurui Qin, Yanding Nankai Univ Coll Artificial Intelligence Tianjin Peoples R China

ISBN: (纸本)9781728102054

The hysteretic nonlinearity of piezoelectric actuators (PEAs) becomes one of the main factors limiting the PEA's motion accuracy. Conventional hysteresis compensation approach consists of hysteresis modeling and inversion, and the inverse hysteresis model can be utilized to compensate the hysteretic nonlinearity. It is found that the accuracy of the inversion calculation in this `modeling-inversion' approach significantly decreases in the fast motion control due to the rate-dependence of the PEA's hysteresis. In the meantime, direct inverse modeling (DIM) was also proposed to construct the inverse hysteresis model without inversion calculation. The applicability of DIM in fast motion control can be guaranteed as no inversion calculation is necessary. However, the off-line identification still increases the complexity and difficulty for the practitioners. This paper proposes the integration of recursive least squares algorithm (RLS) and DIM so as to adjust the parameters of the inverse hysteresis model on-line with no manual intervention. As a result, the requirements on the knowledge and experience of the practitioners can be reduced, guaranteeing wide applicability. Tracking experiments of smooth and non-smooth trajectories are conducted to verify the performance of the proposed method. Trajectory tracking results show the proposed method has excellent performance across a wide frequency range.

关键词： direct inverse modeling recursive least squares algorithm hysteresis compensation

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Adaptive Filtering Techniques using Cyclic Prefix in OFDM Systems for Multipath Fading Channel Prediction

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2016年第10期35卷 3595-3618页

作者： Kohli, Amit Kumar Kapoor, Divneet Singh Thapar Univ Dept Elect & Commun Engn Patiala 147004 Punjab India

This paper presents adaptive channel prediction techniques for wireless orthogonal frequency division multiplexing (OFDM) systems using cyclic prefix (CP). The CP not only combats intersymbol interference, but also precludes requirement of additional training symbols. The proposed adaptive algorithms exploit the channel state information contained in CP of received OFDM symbol, under the time-invariant and time-variant wireless multipath Rayleigh fading channels. For channel prediction, the convergence and tracking characteristics of conventional recursive least squares (RLS) algorithm, numeric variable forgetting factor RLS (NVFF-RLS) algorithm, Kalman filtering (KF) algorithm and reduced Kalman least mean squares (RK-LMS) algorithm are compared. The simulation results are presented to demonstrate that KF algorithm is the best available technique as compared to RK-LMS, RLS and NVFF-RLS algorithms by providing low mean square channel prediction error. But RK-LMS and NVFF-RLS algorithms exhibit lower computational complexity than KF algorithm. Under typical conditions, the tracking performance of RK-LMS is comparable to RLS algorithm. However, RK-LMS algorithm fails to perform well in convergence mode. For time-variant multipath fading channel prediction, the presented NVFF-RLS algorithm supersedes RLS algorithm in the channel tracking mode under moderately high fade rate conditions. However, under appropriate parameter setting in 2x1 space-time block-coded OFDM system, NVFF-RLS algorithm bestows enhanced channel tracking performance than RLS algorithm under static as well as dynamic environment, which leads to significant reduction in symbol error rate.

关键词： Orthogonal frequency division multiplexing Space-time block code Cyclic prefix Multipath fading Kalman filter least mean square recursive least squares algorithm Channel estimation

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An on-line algorithm for creating self-organizing fuzzy neural networks

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NEURAL NETWORKS 2004年第10期17卷 1477-1493页

作者： Leng, G Prasad, G McGinnity, TM Univ Ulster Magee Intelligent Syst Engn Lab Sch Comp & Intelligent Syst Derry BT48 7JL North Ireland

This paper presents a new on-line algorithm for creating a self-organizing fuzzy neural network (SOFNN) from sample patterns to implement a singleton or Takagi-Sugeno (TS) type fuzzy model. The SOFNN is based on ellipsoidal basis function (EBF) neurons consisting of a center vector and a width vector. New methods of the structure learning and the parameter learning, based on new adding and pruning techniques and a recursive on-line learning algorithm, are proposed and developed. A proof of the convergence of both the estimation error and the linear network parameters is also given in the paper. The proposed methods are very simple and effective and generate a fuzzy neural model with a high accuracy and compact structure. Simulation work shows that the SOFNN has the capability of self-organization to determine the structure and parameters of the network automatically. (C) 2004 Published by Elsevier Ltd.

关键词： EBF recursive least squares algorithm self-organizing fuzzy neural network TS model

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Partially-coupled least squares based iterative parameter estimation for multi-variable output-error-like autoregressive moving average systems

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IET CONTROL THEORY AND APPLICATIONS 2019年第18期13卷 3040-3051页

作者： Ma, Hao Pan, Jian Ding, Feng Xu, Ling Ding, Wenfang Hubei Univ Technol Sch Elect & Elect Engn Hubei Key Lab High Efficiency Utilizat Solar Ener Wuhan 430068 Hubei Peoples R China Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 266061 Shandong Peoples R China Jiangnan Univ Sch Internet Things Engn Wuxi 214122 Jiangsu Peoples R China

This study considers the parameter estimation of a multi-variable output-error-like system with autoregressive moving average noise. In order to solve the problem of the information vector containing unknown variables, a least squares-based iterative algorithm is proposed by using the iterative search. The original system is divided into several subsystems by using the decomposition technique. However, the subsystems contain the same parameter vector, which poses a challenge for the identification problem, the approach taken here is to use the coupling identification concept to cut down the redundant parameter estimates. In addition, the recursive least squares algorithm is provided for comparison. The simulation results indicate that the proposed algorithms are effective.

关键词： iterative methods vectors parameter estimation autoregressive moving average processes least squares approximations gradient methods iterative parameter estimation multivariable output-error-like autoregressive moving average systems multivariable output-error-like system autoregressive moving average noise information vector unknown variables iterative search parameter vector identification problem redundant parameter estimates recursive least squares algorithm

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Newton-like minimum entropy equalization algorithm for APSK systems

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SIGNAL PROCESSING 2014年 101卷 74-86页

作者： Ali, Anum Abrar, Shafayat Zerguine, Azzedine Nandi, Asoke K. King Fahd Univ Petr & Minerals Dhahran 31261 Saudi Arabia COMSATS Inst Informat Technol Islamabad Pakistan Brunel Univ Uxbridge UB8 3PH Middx England

In this paper, we design and analyze a Newton-like blind equalization algorithm for the APSK system. Specifically, we exploit the principle of minimum entropy deconvolution and derive a blind equalization cost function for APSK signals and optimize it using Newton's method. We study and evaluate the steady-state excess mean square error performance of the proposed algorithm using the concept of energy conservation. Numerical results depict a significant performance enhancement for the proposed scheme over well established blind equalization algorithms. Further, the analytical excess mean square error of the proposed algorithm is verified with computer simulations and is found to be in good conformation. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Constant modulus algorithm Blind equalizer recursive least squares algorithm Newton's method Tracking performance Amplitude phase shift keying

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The research of superheated steam temperature control based on generalized predictive control algorithm and adaptive forgetting factor

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INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING 2020年第1期34卷 15-31页

作者： Jiang, Cheng Qian, Hong Pan, Yuekai Chai, Tingting Shanghai Univ Elect Power Shanghai 200090 Peoples R China Shanghai Key Lab Power Stn Automat Technol Shanghai Peoples R China

The superheated steam temperature system of the thermal power plant has the characteristics of large inertia, nonlinearity, and strong time variation, which make it difficult to be controlled. To address these problems, this paper proposes a generalized predictive control algorithm with an adaptive forgetting factor. First, based on a fuzzy algorithm and a recursive least squares algorithm, the controlled object's model can be quickly and accurately obtained with the adaptive forgetting factor in real time. It overcomes the nonlinear and time-varying problems of the controlled object in the control progress. Meanwhile, it also solves the problem of data saturation and the weight assignment of the "new and old" data during online identification. Second, an adaptive generalized predictive controller algorithm has been developed with the controlled object. It solves the large inertia problem of the controlled object. Finally, through establishing simulation model of the superheated steam temperature system and simulating, the results show that the proposed method has better control performance, antidisturbance ability, adaptability, and robustness. Moreover, it has a certain reference significance for the design of a practical control system.

关键词： adaptive forgetting factor fuzzy algorithm generalized predictive control recursive least squares algorithm superheated steam temperature

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Parameter identification of a lithium-ion battery based on the improved recursive least square algorithm

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IET POWER ELECTRONICS 2020年第12期13卷 2531-2537页

作者： Ren, Biying Xie, Chenxue Sun, Xiangdong Zhang, Qi Yan, Dan Xian Univ Technol Dept Elect Engn Xian Peoples R China Key Lab Shaanxi Prov Complex Syst Control & Intel Xian Peoples R China

Accurate parameter identification of a lithium-ion battery is a critical basis in the battery management systems. Based on the analysis of the second-orderRCequivalent circuit model, the parameter identification process using the recursive least squares (RLS) algorithm is discussed firstly. The reason for the RLS algorithm affecting the accuracy and rapidity of model parameter identification is pointed out. And an improved RLS algorithm is proposed, an inner loop with the estimated parameter vector updated multiple times is inserted into the conventional RLS algorithm, so that the identification results are improved. The test platform of a single lithium-ion battery is built. The experimental results show that the improved RLS algorithm has better tracking ability, smaller prediction error and has a moderate computational burden compared with the conventional RLS algorithm and a variable forgetting factor RLS algorithm.

关键词： battery management systems equivalent circuits parameter estimation secondary cells least squares approximations recursive estimation estimated parameter vector conventional RLS algorithm single lithium-ion battery improved RLS algorithm variable forgetting factor RLS algorithm improved recursive least square algorithm accurate parameter identification critical basis battery management systems second-order RC equivalent circuit model parameter identification process recursive least squares algorithm model parameter identification

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Maximum likelihood based identification methods for rational models

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2019年第14期50卷 2579-2591页

作者： Chen, Jing Ding, Feng Zhu, Quanmin Liu, Yanjun Jiangnan Univ Sch Sci Wuxi 214122 Jiangsu Peoples R China Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao Shandong Peoples R China Jiangnan Univ Sch Internet Things Engn Wuxi Jiangsu Peoples R China Univ West England Dept Engn Design & Math Bristol Avon England

In a rational model, some terms of the information vector are correlated with the noise, which makes the traditional least squares based iterative algorithms biased. In order to overcome this shortcoming, this paper develops two recursive algorithms for estimating the rational model parameters. These two algorithms, based on the maximum likelihood principle, have three integrated key features: (1) to establish two unbiased maximum likelihood recursive algorithms, (2) to develop a maximum likelihood recursive least squares (ML-RLS) algorithm to decrease the computational efforts, (3) to update the parameter estimates by the ML-RLS based particle swarm optimisation (ML-RLS-PSO) algorithm when the noise-to-output ratio is large. Comparative studies demonstrate that (1) the ML-RLS algorithm is only valid for rational models when the noise-to-output ratio is small, (2) the ML-RLS-PSO algorithm is effective for rational models with random noise-to-output ratio, but at the cost of heavy computational efforts. Furthermore, the simulations provide cases for potential expansion and applications of the proposed algorithms.

关键词： Parameter estimation rational model maximum likelihood method PSO algorithm recursive least squares algorithm

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Design of a generalized predictive controller for a biological wastewater treatment plant

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WATER SCIENCE AND TECHNOLOGY 2016年第8期73卷 1986-2006页

作者： Sadeghassadi, M. Macnab, C. J. B. Westwick, D. Univ Calgary 2500 Univ Dr NW Calgary Calgary AB T2N 1N4 Canada

This paper presents a generalized predictive control (GPC) technique to regulate the activated sludge process found in a bioreactor used in wastewater treatment. The control strategy can track dissolved oxygen setpoint changes quickly, adapting to the system uncertainties and disturbances. Tests occur on an Activated Sludge Model No. 1 benchmark of an activated sludge process. A T filter added to the GPC framework results in an effective control strategy in the presence of coloured measurement noise. This work also suggests how a constraint on the measured variable can be added as a penalty term to the GPC framework which leads to improved control of the dissolved oxygen concentration in the presence of dynamic input disturbance.

关键词： Benchmark Simulation Model No. 1 biological wastewater treatment process model predictive control recursive least squares algorithm

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