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Asia-Pacific Power and Energy Engineering Conference (APPEEC)

作者： Cao, Jian Lin, Tao Liu, Lin Ling, Jia-kai Huang, Xue-ling Wuhan Univ Sch Elect Engn Wuhan 430072 Hubei Province Peoples R China Jiangsu Wuxi Power Supply Co Substn Operat Ctr Wuxi 214012 Jiangsu Peoples R China

ISBN: (纸本)9781424462551

Based on new time frequency algorithm, a new interharmonic analysising method is raised in this paper. The number and approximate frequency of interharmonics is firstly gotten using least-square algorithm. Then new time frequency algorithm is applied to measure the accurate frequency and magnitude of each interharmonic. The simulation indicates that this method mentioned above is able to measure frequency and magnitude of interharmonics accurately and real-timely, with the advantage of high frequency resolution and measuring precision and low calculation amount performance.

关键词： power system interharmonics least-squares algorithm new time frequency analysis

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Estimating Wheel-road Vertical Load for Nonlinear Vehicle Vibration System Based on Cubature Kalman Filter 30

Estimating Wheel-road Vertical Load for Nonlinear Vehicle Vi...

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30th Chinese Control and Decision Conference (CCDC)

作者： Zhang, Yuexin Wang, Lihui Southeast Univ Sch Instrument Sci & Engn Minist Educ Key Lab Microinertial Instrument & Adv Nav Techno Nanjing 210096 Peoples R China

ISBN: (纸本)9781538612446

The vibration of vehicle is most governed by the load generated of the road roughness, which can affect comfort of vehicle and cause fatigue damage of the vehicle suspension. So knowledge of the load is very important for vehicle control systems to enhance vehicle stability and satety. During the process of the estimation, noise affects the identification accuracy, and maybe cause divergence. The cubature Kalman filter (CKF) has good accuracy perfomiance, numerical stability and computational costs. So this paper presented a new methodology based on CKF and a recursive least-squares algorithm for estimating wheel -road vertical load. The state equations and measurement equations were established according to the second order vibration system, and RK4 was employed to discrctize the continuous -time equations. The CKF was used to calculate the residual innovation sequences and the residual innovation sequences were used to calculate the vertical load. To verify the effectiveness of the identification method, numerical simulations of the five degrees of freedom vehicle vibration system subjected to white noises and four types of forces were employed. Simulation results validated and proved the feasibility of this approach.

关键词： Vertical load Five degrees of freedom vehicle vibration system Cubature Kalman filter Recursive least-squares algorithm Vehicle stability

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The Modeling of Steam Turbine Speed Control System Based on Radial Basis Function Neural Network

The Modeling of Steam Turbine Speed Control System Based on ...

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International Conference on Manufacturing Engineering and Automation

作者： Ren, Yong Yang, Tao Gao, Wei Li, Yanghai Huazhong Uni Sci & Tech Sch Energy & Power Engn Wuhan 430074 Peoples R China

ISBN: (纸本)9780878492268

The model of speed control system plays an important role in power system stability studies, the non-linear properties prevent us from getting accurate mechanism model. In this paper, the radial basis function neural network with self-structuring and fast convergence is used in the modeling of steam turbine speed control system in the modeling process, also, this paper presents a method which combines particle swarm optimization algorithm and least-squares algorithm for the neural network's training, it has the property of high accuracy and fast convergence, after training, the proposed model and related training algorithm are verified by the test data of one power plant, it has proved that the neural network can be used in the modeling of the speed control system for the power system stability studies.

关键词： Steam turbine Speed control system RBF neural network Particle swarm optimization algorithm least-squares algorithm

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Experimental Evaluation of Parameter Identification Schemes on an Anthropomorphic Direct Drive Robot

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2012年第5期9卷

作者： Chavez-Olivares, Cesar Reyes-Cortes, Fernando Gonzalez-Galvan, Emilio Mendoza-Gutierrez, Marco Bonilla-Gutierrez, Isela Univ Autonoma San Luis Potosi Fac Ingn Ctr Invest & Estudios Posgrad San Luis Potosi Mexico Benemerita Univ Autonoma Puebla Fac Ciencias Elect Grp Robot Puebla Pue Mexico Univ Autonoma San Luis Potosi Fac Ciencias San Luis Potosi Mexico

The inertial and friction parameters of a robot are used in the development and evaluation of model-based control schemes and their accuracy is related directly to the performance. These parameters can also be used for a realistic simulation, which may be useful before implementation of new control schemes. In principle, the numerical value of the parameters could be obtained via CAD analysis, but inevitably assembly and manufacturing errors exist. Direct measurement is not a realistic option because the complex nature of the system involves intense, time-consuming effort. Alternatively, we can deduce the values of the parameters by observing the natural response of the system under appropriate experimental conditions, i.e., by using identification schemes, which is an efficient way. This paper presents the experimental evaluation of five identification schemes used to obtain the inertial and friction parameters of a three-degrees-of-freedom direct-drive robot. We assume that the inertial and friction parameters are totally unknown but, by design, the dynamic model is fully known, as in many practical cases. We consider the schemes based on the dynamic regression model, the filtered-dynamic regression model, the supplied-energy regression model, the power regression model and the filtered-power regression model. This paper presents a comparison between experimental and simulated robot response, which enables us to verify the accuracy of each regression model.

关键词： Direct Drive Robot Identification Schemes Regression Models least-squares algorithm

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Determination of proximity effect parameters based on nonlinear curve fitting

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Weixi Jiagong Jishu/Microfabrication Technology 2006年第2期 12-15+20页

作者： Hao, Hui-Juan Zhang, Yu-Lin Song, Hui-Ying Wei, Qiang Institute of Electron Beam Shandong University Jinan 250061 China

In order to determine the proximity effect parameters more accurately, the process of the electron beam scattering in solid was simulated with the optimized model of electron beam scattering and modified Monte Carlo algorithm. It was obtained that the distribution of the energy deposition of different exposure in resist. The double Gaussian fitting of energy distribution was carried out by the method of nonlinear least-squares curve fitting, which elicited the parameters (α, β and η). The comparison between the fitting parameters and the experiment results shows that the method of nonlinear least-squares curve fitting can be used to determine the parameters of proximity effect. The parameters fitting for different exposure shows the change trend of α, β and η. The increment of electron beam energy leads to the decrease of α and increase of β, while η is almost constant. The increase of resist thickness results in the increment of α while β and η are almost invariable. The increase of substrate's atom number causes decrease of β and increment of η, while α is unchangeable. The results can not only provide theoretical guide for optimizing the exposure conditions and reducing the proximity effect in electron beam lithography, but also can quickly provide more accurate parameters for proximity effect correction.

关键词： Curve fitting Electron beam lithography least-squares algorithm Monte Carlo simulation Proximity effect

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Identification of Multivariable Canonical State-Space Representations

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IFAC Proceedings Volumes 2009年第10期42卷 1638-1643页

作者： L. Bako G. Mercère S. Lecoeuche Ecole des Mines de Douai Département Informatique et Automatique 941 rue Charles Bourseul 59508 Douai Cedex France Université de Poitiers Laboratoire d'Automatique et d'Informatique Industrielle 40 avenue du recteur Pineau 86022 Poitiers France

The paper introduces a method to estimate a linear state-space model from its input-output data. Its starting point is the conversion of the state-space model into an input-output ARMAX-like model. In order to conveniently estimate the parameters of this ARMAX model, it is necessary, given the structural characteristics of the initial state-space model, to assume that the dynamic state matrix A is nonderogatory. Under this condition, it is shown that the input-output model and the state-space model from which it is derived have the same order. Based on this important result in linear system realization theory, a canonical state-space representation is finally obtained through a simple construction technique. The developed method can be regarded as an alternative to the standard subspace methods particularly in situations where the state coordinates basis need to be maintained constant with respect to the input-output realization used for the estimation.

关键词： least-squares algorithm State-space representation Canonical form ARMAX model Multi-input/multi-output systems

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Attitude Determination With GPS Based on Predictive Filter

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IFAC Proceedings Volumes 2001年第15期34卷 247-252页

作者： Jing Yang Hong Yue Zhang Xiang Qun Liu Beijing University of Aeronautics and Astronautics Beijing 100083 China Fax: 86-10-82328116

The attitude determination with Global Positioning Systems (GPS) can be realized using predictive filter based on dual-difference carrier phase. Compared with single-difference, dual-difference carrier phase can improve accuracy of attitude estimate. This algorithm is robust with respect to initial condition errors. It can converge to true attitude rapidly. The simulation show that attitude estimate based on this algorithm is almost similar to which based on nonlinear least-squares algorithm, and robust to initial condition errors. When three baselines are used, during the maneuver of vehicle, the baselines nearly orthogonal can estimate attitude better than other frames of baselines.

关键词： Global Positioning Systems Prediction Methods Attitude algorithm Phase Difference least-squares algorithm

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The least-squares Identification of Fir Systems Subject to Worst-Case Noise

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IFAC Proceedings Volumes 1994年第8期27卷 395-400页

作者： Hüseyin Akçay Håkan Hjalmarson Department of Electrical Engineering Linkoping University S-58J 83 Linköping Sweden

The least-squares identification of FIR systems is analyzed assuming that the noise is a bounded signal and the input signal is a pseudo random binary sequence. A lower bound on the worst-case transfer function error shows that the least-square estimate of the transfer function diverges as the order of the FIR system is increased. This implies that, in the presence of the worst-case noise, the trade-off between the estimation error due to the disturbance and the bias error (due to unmodeled dynamics) is significantly different from the corresponding trade-off in the random error case: with a worst-case formulation, the model complexity should not increase indefinitely as the size of the data set increases.

关键词： Worst-case identification FIR Systems least-squares algorithm

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Associative Search and Wavelet Analysis Techniques in System Identification

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IFAC Proceedings Volumes 2012年第16期45卷 1227-1232页

作者： Natalia N. Bakhtadze Vladimir A. Lototsky Stanislav A. Vlasov Ekaterina A. Sakrutina A. Trapeznikov Institute of Control Sciences 65 Profsoyuznaya Moscow 117997 Russia (tel: +7 495 334 9201

A system identification method using predictive models based on the imitation of analyst's associative thinking is developed. Identification algorithms using associative search for description of the knowledge abo... 详细信息

关键词： identification algorithms virtual model least-squares algorithm associative search fuzzy models knowledge base

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Online Estimation of Ship Steering Dynamics and Its Application in Designing an Optimal Autopilot

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IFAC Proceedings Volumes 2000年第24期33卷 33-38页

作者： Minh-Due Le Graduate School of Engineering Hiroshima University Higashi Hiroshima-shi Kagamiyama 1-4-1 Hiroshima 739-8527 Japan Tel. (Mobile): (81) 070-5671-2114.

Recursive least Square (RLS) algorithm applied to a Multivariate Auto-Regressive (MAR) process is used to estimate ship steering dynamics online. The estimation method is then linked to the Linear Quadratic (LQ) algorithm to design an optimal autopilot for steering ships. The estimation method was applied to several ships and model ships and in all the cases the estimated parameters converged well. The design algorithm was used to construct a tracking system for course keeping and course changing manoeuvres. Simulation results for the ships show the robustness of the estimation method and prove that the autopilot has very good performance.

关键词： estimation and identification autoregressive models least-squares algorithm quadratic control ship control design systems

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