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Recursive parameter estimation for Hammerstein-Wiener systems using modified EKF algorithm

ISA TRANSACTIONS 2017年 70卷 104-115页

作者： Yu, Feng Mao, Zhizhong Yuan, Ping He, Dakuo Jia, Mingxing Northeastern Univ Dept Control Theory & Control Engn 11Lane 3WenHua Rd Shenyang Liaoning Peoples R China

This paper focuses on the recursive parameter estimation for the single input single output Hammerstein-Wiener system model, and the study is then extended to a rarely mentioned multiple input single output Hammerstein-Wiener system. Inspired by the extended kalman filter algorithm, two basic recursive algorithms are derived from the first and the second order Taylor approximation. Based on the form of the first order approximation algorithm, a modified algorithm with larger parameter convergence domain is proposed to cope with the problem of small parameter convergence domain of the first order one and the application limit of the second order one. The validity of the modification on the expansion of convergence domain is shown from the convergence analysis and is demonstrated with two simulation cases. (C) 2017 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Hammerstein-Wiener system Recursive parameter estimation extended kalman filter algorithm Multiple input single output Uniform convergence

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Smartphone-based crowdsourcing for position estimation of public transport vehicles

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IET INTELLIGENT TRANSPORT SYSTEMS 2017年第9期11卷 588-595页

作者： Mukheja, Pankaj Kiran, Mukesh K. Velaga, Nagendra R. Sharmila, R. B. Indian Inst Technol Dept Civil Engn Bombay 400076 Maharashtra India

In this research, a real-time positioning method, which utilises crowdsourced positioning data obtained from smartphone GPS is developed. Such vehicle location information obtained from crowdsourcing and smartphones in public transport could replace traditional automatic vehicle location systems. However, the location information from smartphone GPS is more erroneous. The proposed methodology serves as an alternative to existing positioning methods to improve the vehicle positioning accuracy. The developed enhanced particle filter algorithm takes smartphone GPS positioning data [from multiple passengers in a single transit vehicle (e.g. bus)] as input data. This crowdsourced' data can then be utilised to calculate the vehicles' positioning information with better accuracy using the developed enhanced particle filter algorithm. The developed algorithm was tested using data collected on 14 different bus routes in urban and suburban areas of Mumbai, India, and it was identified that the algorithm is effective in reducing the average error up to 21.3% from a regular smartphone GPS and 10% from extended kalman filter algorithm and was able to curtail positioning error within 8.672m (average over 14 routes).

关键词： smart phones crowdsourcing public transport Global Positioning System traffic information systems particle filtering (numerical methods) mobile computing intelligent transportation systems smartphone-based crowdsourcing position estimation public transport vehicles real-time positioning method vehicle location information automatic vehicle location systems vehicle positioning accuracy enhanced particle filter algorithm smartphone GPS positioning data crowdsourced data data collection bus routes extended kalman filter algorithm

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An adaptive fuzzy neural network for MIMO system model approximation in high-dimensional spaces

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 1998年第3期28卷 436-446页

作者： Chak, CK Feng, G Ma, J Univ New S Wales Sch Elect Engn Dept Syst & Control Sydney NSW 2052 Australia City Univ Hong Kong Dept Informat Syst Kowloon Hong Kong

An adaptive fuzzy system implemented within the framework of neural network is proposed. The integration of the fuzzy system into a neural network enables the new fuzzy system to have learning and adaptive capabilities. The proposed fuzzy neural network can locate its rules and optimize its membership functions by competitive learning, kalman filter algorithm and extended kalman filter algorithms. A key feature of the new architecture is that a high-dimensional fuzzy system can be implemented with fewer number of rules than the Takagi-Sugeno fuzzy systems. A number of simulations are presented to demonstrate the performance of the proposed system including modeling nonlinear function, operator's control of chemical plant, stock prices and bioreactor (multioutput dynamical system).

关键词： competitive learning extended kalman filter algorithm fuzzy systems kalman filter algorithm neural networks supervised learning

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Learning techniques to train neural networks as a state selector for inverter-fed induction machines using direct torque control

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IEEE TRANSACTIONS ON POWER ELECTRONICS 1997年第5期12卷 788-799页

作者： Cabrera, LA Elbuluk, ME Zinger, DS NO ILLINOIS UNIV DEPT ELECT ENGNDE KALBIL 60115

Neural networks are receiving attention as controllers for many industrial applications. Although these networks eliminate the need for mathematical models, they require a lot of training to understand the model of a plant or a process. Issues such as learning speed, stability, and weight convergence remain as areas of research and comparison of many training algorithms. This paper discusses the application of neural networks to control induction machines using direct torque control (DTC), A neural network is used to emulate the state selector of the DTC, The training algorithms used in this paper are the backpropagation, adaptive neuron model, extended kalman filter, and the parallel recursive prediction error, Computer simulations of the motor and neural-network system using the four approaches are presented and compared, Discussions about the parallel recursive prediction error and the extended kalman filter algorithms as the most promising training techniques is presented, giving their advantages and disadvantages.

关键词： adaptive neuron algorithm backpropagation algorithm direct torque control extended kalman filter algorithm gradient descent algorithms induction machines neural-networks structure and training Newton algorithms parallel recursive prediction error algorithm

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Error Analysis of Model-based State-of-Charge Estimation for Lithium-Ion Batteries at Different Temperatures

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INTERNATIONAL JOURNAL OF ELECTROCHEMICAL SCIENCE 2020年第10期15卷 9981-10006页

作者： Ren, Zhong Du, Changqing Wang, Huawu Shao, Jianbo Wuhan Univ Technol Hubei Key Lab Adv Technol Automot Components Wuhan 430070 Peoples R China Wuhan Univ Technol Hubei Collaborat Innovat Ctr Automot Components T Wuhan 430070 Peoples R China Dongfeng Commercial Vehicle Co Ltd Wuhan 430070 Peoples R China

State-of-charge (SOC) estimation of lithium-ion batteries (LIBs) is one of the core functions of a battery management system (BMS). Until now, numerous approaches have been proposed to achieve high accuracy SOC estimation, among which the model-based SOC estimation algorithm is the most popular algorithm implementation in an actual BMS. Since SOC estimation accuracy is directly influenced by battery model accuracy, it is essential to quantitatively analyze the relationship between model and SOC estimation accuracy, as well as the error sources of the model error. In this article, first, the model accuracy and SOC estimation accuracy are comprehensively studied based on the first-order resistance-capacitor (RC) model and extended kalman filter (EKF) algorithm under the constant current discharge (CCD) test and Federal Urban Dynamic Schedule (FUDS) test at different test temperatures (0 degrees C, 10 degrees C, 20 degrees C, 30 degrees C, 40 degrees C, and 50 degrees C). Second, regression and correlation analysis is applied to quantitatively evaluate the relationship between the normalized root-mean-square error (RMSE) of model and SOC estimation error. Third, the impact of the SOC open-circuit-voltage (OCV) curve, Ohmic resistance, impedance, and sensor error on SOC estimation accuracy are systematically studied as well. The results show that there is a one-dimensional linear positive relationship between model and SOC estimation accuracy, and the specific quantitative relationship is given. Among the parameters of the battery model, the accuracy of the SOC-OCV curve has the greatest influence on model and SOC estimation error compared to Ohmic resistance and impedance. In addition, compared to the effect of current sensor error, the voltage sensor error has a more significant impact on model and SOC estimation error.

关键词： Lithium-Ion Battery State-of-Charge Estimation extended kalman filter algorithm Error Analysis Battery Management System

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Tire-Ground Forces Estimation in a 4-Wheel Vehicle Using a Delayed Interconnected Cascade-Observer Structure

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IFAC-PapersOnLine 2016年第15期49卷 139-144页

作者： Cordeiro, Rafael A. Victorino, Alessandro C. Ferreira, Paulo A.V. de Paiva, Ely C. Bueno, Samuel S. School of Electrical and Computer Engineering University of Campinas Campinas Brazil Heudiasyc Lab UMR CNRS 7253 Université de Technologie de Compiègne Compiègne France School of Mechanical Engineering University of Campinas Campinas Brazil Division of Robotics and Computer Vision Center for Information Technology Renato Archer Campinas Brazil

The knowledge of tire-ground interaction forces is interesting for intelligent vehicles. However, tire forces transducers are expensive and not suitable for ordinary passengers cars. An alternative is to estimate these forces using common sensors. This paper presents an estimator structure capable of reconstructing tire-ground interaction forces in all directions. A delayed interconnected cascade-observer structure is proposed to eliminate mutual dependence between estimators. Observers are developed based on nonlinear vehicle dynamic models with the extended kalman filter algorithm. The estimator is validated with experimental results. The results are also compared with an existent estimator of the literature. © 2016

关键词： kalman filters Intelligent vehicle highway systems Tires Between estimators extended kalman filter algorithm Ground forces Interaction forces Mutual dependences State observer Vehicle dynamic model Vehicle dynamics

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Germinal Center Optimization Applied to Recurrent High Order Neural Network Observer

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IFAC-PapersOnLine 2018年第13期51卷 332-337页

作者： Rios, Jorge D. Villaseñor, Carlos Alanis, Alma Y. Arana-Daniel, Nancy Lopez-Franco, Carlos Centro Universitario de Ciencias Exactas e Ingenierías Universidad de Guadalajara Blvd. Marcelino García Barragán 1421 Guadalajara44430 Mexico

In this work, a germinal center optimization (GCO) algorithm which implements temporal leadership through modeling a non-uniform competitive-based distribution for particle selection is used to find an optimal set of parameters for a recurrent high order neural network observer (RHONNO). The RHONNO is trained with an extended kalman filter algorithm and it is capable of giving a model of the system besides of just giving state estimation. Furthermore, the RHONNO does not need previous knowledge of the system model, nor measurements, estimation or bounds of delays and disturbances. Applicability of the proposed methodology is presented using simulation results. © 2018

关键词： Parameter estimation Adaptive algorithms extended kalman filters Models Neural networks State estimation Time delay extended kalman filter algorithm Germinal centers Non uniform Optimal sets Parameter optimization Particle selection Recurrent high order neural networks System modeling

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Discrete-Time Neural Block Control for a Doubly Fed Induction Generator

Discrete-Time Neural Block Control for a Doubly Fed Inductio...

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International Joint Conference on Neural Networks (IJCNN)

作者： Ruiz, R. Sanchez, E. N. Loukianov, A. G. CINVESTAV IPN Zapopan 45015 Jalisco Mexico

ISBN: (纸本)9781424496365

This paper proposes a control scheme based on the discrete-time block control technique using sliding modes, for a doubly fed induction generator connected to an infinity bus. In order to obtain the generator mathematical model, it is proposed to use a recurrent high order neural network (RHONN) identifier, which is trained with an extended kalman filter (EFK) algorithm. Parameter changes are applied to test the scheme robustness. Its performance is illustrated via simulations.

关键词： Induction generators kalman filters Reactive power Resistance Rotors Stator windings Torque asynchronous generators discrete time systems discrete-time block control discrete-time neural block control doubly fed induction generator extended kalman filter algorithm generator mathematical model infinity bus machine control neurocontrollers nonlinear filters recurrent high order neural network identifier recurrent neural nets sliding modes variable structure systems

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Design and Application of a Small ROV Control System Based on ArduSub System 2

Design and Application of a Small ROV Control System Based o...

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2nd IEEE International Conference on Civil Aviation Safety and Information Technology (ICCASIT)

作者： Zhao, Yuming He, Zhen Li, Gelun Wang, Yabiao Li, Zhigang Chinese Acad Sci Shenyang Inst Automat Shenyang 110169 Peoples R China Chinese Acad Sci Inst Robot Shenyang 110169 Peoples R China Chinese Acad Sci Inst Intelligent Mfg Shenyang 110169 Peoples R China

ISBN: (纸本)9781728199481

A small ROV was designed and developed for its wide application and low cost, ArduSub system was used to construct ROV control system to realize ROV motion and attitude control. Equipped with componets such as cameras, underwater lights, sonar and manipulator, ROV has the characteristics of flexible movmcnt, strong anti-current ability, and underwater observation and operation capabilities. Structure and control system composition of ROV and the attitude control algorithm used is introduced in the paper. Through the pool test, the results showed that the use of ArduSub system to build ROV control system makes the ROV has better motion performance and attitude maintain ability, shorten the ROV development cycle and reduce cost.

关键词： ArduSub Pixhawk ROV extended kalman filter algorithm Double closed-loop PID

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kalman filter Carrier Synchronization algorithm

Kalman Filter Carrier Synchronization Algorithm

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10th International Symposium on Electronics and Telecommunications (ISETC)

作者： Campeanu, Andrei Gal, Janos Fac Elect & Telecommun Dept Commun Timisoara Romania

ISBN: (纸本)9781467311762

A new kalman carrier synchronization algorithm was developed for high-order QAM modulated signals which showed fast acquisition times and good frequency tracking performances. The proposed solution relies on a Decision Directed kalman filter combined with a lock detector which determines the status of the synchronization process, modifying consequently the filter parameters. Simulation results show that, compared to the performance of a basic kalman synchronization system, a tenfold improvement or better is achieved in terms of acquisition speed, while keeping a low residual phase noise.

关键词： component Acquisition Mode and Tracking Mode Decision-Directed Carrier Synchronization extended kalman filter algorithm Quadrature Amplitude Modulation Random Walk

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