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Adaptive Hammerstein Predistorter Using the recursive prediction error method

Tsinghua Science and Technology 2008年第1期13卷 17-22页

作者：李辉王德生陈兆武 Department of Electronic Engineering Tsinghua University

The digital baseband predistorter is an effective technique to compensate for the nonlinearity of power amplifiers （PAs） with memory effects. However, most available adaptive predistorters based on direct learning architectures suffer from slow convergence speeds. In this paper, the recursive prediction error method is used to construct an adaptive Hammerstein predistorter based on the direct learning architecture, which is used to linearize the Wiener PA model. The effectiveness of the scheme is demonstrated on a digital video broadcasting-terrestrial system. Simulation results show that the predistorter outperforms previous predistorters based on direct learning architectures in terms of convergence speed and linearization. A similar algorithm can be applied to estimate the Wiener PA model, which will achieve high model accuracy.

关键词： power amplifier predistorter Wiener system Hammerstein system recursive prediction error method

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Estimation of the State and Parameters of a Bioprocess Using the recursive prediction error method

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IFAC Proceedings Volumes 1989年第6期22卷 507-512页

作者： J.M. Flaus A. Chéruy Labomtoire d'Automatique de Grenoble INPG. Grenoble France

This paper presents an alternative to the Extended Kalman filter for the estimation of the state and parameters of a bioprocess. This method called recursive prediction error method has been developped by Ljung [83]and can be applied on a model of any degree of complexity as EKF, but stability and convergences properties can be analyzed more easily. From a practical point of view, another advantage is that the tuning parameters of the algorithm have a simpler meaning, and then made the algorithm easier to use.

关键词： recursive Estimation Biotechnology recursive prediction error method Kalman filters

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ADAPTIVE STATE TRACKING

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IEE PROCEEDINGS-D CONTROL THEORY AND APPLICATIONS 1988年第6期135卷 429-435页

作者： LIU, L SINHA, NK Department of Electrical & Computer Engineering McMaster University Hamilton Canada

The problem of adaptive state tracking is considered, where a specified state is required to follow the reference input signal. It is assumed that the system is completely controllable and observable, but the particular state is not directly available for measurement and the measurements are contaminated with stochastic noise. It is also assumed that there is little prior knowledge of the reference input, and its parameters have to be detected by the control system. The recursive prediction error (RPE) method is used for joint estimation of the parameters and states of the process to be controlled. The controller design is based on the placement of the poles and zeros of the state tracking error transfer function. As a result, adaptive state tracking is obtained for arbitrary external reference signals.

关键词： Self-adjusting control systems poles and zeros poles controllability adaptive state tracking transfer functions Simulation, modelling and identification adaptive systems state estimation adaptive systems, controllability state tracking error transfer function zeros recursive prediction error method observability parameter estimation Control system analysis and synthesis methods adaptive control

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Auxiliary model based parameter estimation for dual-rate output error systems with colored noise

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APPLIED MATHEMATICAL MODELLING 2013年第6期37卷 4051-4058页

作者： Ding, Jie Fan, Chunxia Lin, Jinxing Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210046 Peoples R China

The dual-rate sampled-data systems can offer better quality of control than the systems with single sampling rate in practice. However, the conventional identification methods run in the single-rate scheme. This paper focuses on the parameter estimation problems of the dual-rate output error systems with colored noises. Based on the dual-rate sampled and noise-contaminated data, two direct estimation algorithms are addressed: the auxiliary model based recursive extended least squares algorithm and the recursive prediction error method. The auxiliary model is employed to estimate the noise-free system output. An example is given to test and illustrate the proposed algorithms. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Dual-rate sampled data Auxiliary model Output error model Least square recursive prediction error method

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IDENTIFICATION OF RUDDER YAW AND RUDDER ROLL STEERING MODELS BY USING recursive prediction error TECHNIQUES

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OPTIMAL CONTROL APPLICATIONS & methodS 1994年第2期15卷 101-114页

作者： ZHOU, WW CHERCHAS, DB CALISAL, S UNIV BRITISH COLUMBIA DEPT MECH ENGNVANCOUVER V6T 1W5BCCANADA

Linear and non-linear recursive prediction error (RPE) methods have been employed for the purpose of identification of the dynamics of ship rudder-yaw and -roll motions. The evaluations of the goodness of model structures have been carried out by comparing simulated outputs from the identified model with real measurements. In the linear case Nomoto's first-order model has been initially considered for the transfer functions between rudder-yaw and -sway, with a second-order model for rudder-roll with coupling effects considered only between sway-yaw and -roll. Identification results have shown that such a model is not appropriate. Changing the model to second-order between rudder-sway and -yaw and further considering the coupling effects between roll angle-sway and -yaw shows that a significant improvement is achieved, while the coupling effect between yaw and roll appears negligible. In the non-linear case effects due to various non-linear terms have been studied with the non-linear RPE algorithm.

关键词： IDENTIFICATION recursive prediction error method SHIP STEERING DYNAMICS RUDDER ROLL DYNAMICS

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MODIFIED APPROACH TO STATE-SPACE SELF-TUNING CONTROL WITH POLE ASSIGNMENT

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IEE PROCEEDINGS-D CONTROL THEORY AND APPLICATIONS 1985年第6期132卷 257-262页

作者： OMANI, FK SINHA, NK Department of Electrical & Computer Engineering McMaster University Hamilton Canada

The paper presents a modified approach to state-space self-tuning pole-assignment control. The recursive prediction error method is used for joint state and parameter estimation in the controllable canonical form. This simplifies the state feedback law by eliminating the online computation of transformation matrices. The resulting algorithm is thus a feasible solution to the adaptive control of nonminimum-phase systems.

关键词： Self-adjusting control systems poles and zeros state feedback law Simulation, modelling and identification state-space methods state-space self-tuning pole-assignment control state estimation nonminimum-phase systems self-adjusting systems recursive prediction error method parameter estimation Control system analysis and synthesis methods adaptive control

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Particle filtering-based recursive identification for controlled auto-regressive systems with quantised output

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IET CONTROL THEORY AND APPLICATIONS 2019年第14期13卷 2181-2187页

作者： Ding, Jie Chen, Jiazhong Lin, Jinxing Jiang, Guoping Nanjing Univ Posts & Telecommun Sch Automat & Artificial Intelligence Jiangsu Engn Lab IOT Intelligent Robots Nanjing 210023 Jiangsu Peoples R China

recursive prediction error method is one of the main tools for analysis of controlled auto-regressive systems with quantised output. In this study, a recursive identification algorithm is proposed based on the auxiliary model principle by modifying the standard stochastic gradient algorithm. To improve the convergence performance of the algorithm, a particle filtering technique, which approximates the posterior probability density function with a weighted set of discrete random sampling points is utilised to correct the linear output estimates. It can exclude those invalid particles according to their corresponding weights. The performance of the particle filtering technique-based algorithm is much better than that of the auxiliary model-based one. Finally, results are verified by examples from simulation and engineering.

关键词： stochastic processes parameter estimation least squares approximations filtering theory gradient methods autoregressive processes recursive estimation probability particle filtering (numerical methods) auxiliary model principle standard stochastic gradient algorithm novel particle filtering technique posterior probability density function discrete random sampling points linear output estimates invalid particles particle filtering technique-based algorithm auxiliary model-based particle filtering-based recursive identification controlled auto-regressive systems quantised output recursive prediction error method main tools novel recursive identification algorithm

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Sensitivity models for nonlinear filters with application to recursive parameter estimation for nonlinear state-space models

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IEE PROCEEDINGS-CONTROL THEORY AND APPLICATIONS 2001年第2期148卷 137-145页

作者： Bohn, C Unbehauen, H Continental AG Strateg Technol Dept D-30419 Hannover Germany Ruhr Univ Bochum Dept Elect Elect & Informat Sci Control Engn Lab D-44780 Bochum Germany

If a recursive prediction error method is applied for the parameter estimation of state-space models, a filter must be used as the predictor, resulting in an adaptive filtering problem. Whereas for linear systems the optimal linear filter, the Kalman filter, can be used, approximate nonlinear filters have to be employed for nonlinear systems. For the parameter estimation, the gradient of the prediction error, and maybe the gradient of its covariance matrix as well, are required. These can be obtained from a sensitivity model;however, the derivation of such a sensitivity model is tedious and has only been done for the extended Kalman filter. Sensitivity models for four common continuous-discrete nonlinear filters are derived: the extended Kalman filter, the first-order bias-corrected filter, the truncated second-order filter, and the modified Gaussian second-order filter. The derivation of these sensitivity models relies heavily on the use of matrix differential calculus, and utilises a reformulation of the approximate nonlinear filters derived earlier. Elementwise derivatives and bookkeeping over indices are thus entirely avoided. An adaptive filter is applied for real-time parameter identification and tracking for a laboratory three-tank process.

关键词： adaptive filtering problem first-order bias-corrected filter recursive estimation Simulation, modelling and identification truncated second-order filter recursive parameter estimation state-space methods continuous-discrete nonlinear filter nonlinear control systems Nonlinear control systems Kalman filters recursive prediction error method approximate nonlinear filters modified Gaussian second-order filter Other topics in statistics Control system analysis and synthesis methods flow control matrix differential calculus nonlinear filters laboratory three-tank process extended Kalman filter nonlinear state-space models Level, flow and volume control Signal processing theory sensitivity models

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recursive online EM estimation of mixture autoregressions

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JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION 2013年第2期83卷 370-383页

作者： Aknouche, Abdelhakim Univ Sci & Technol Houari Boumedienne Fac Math Algiers Algeria Qassim Univ Dept Math Qasim Saudi Arabia

This paper develops a recursive expectationmaximization (REM) algorithm for estimating a mixture autoregression (MAR) with an independent and identically distributed regime transition process. The proposed method, which is useful for long time series as well as for data available in real time, follows a recursive predictor error-type scheme. Based on a slightly modified system to the expectationmaximization (EM) equations for an MAR model, the REM algorithm consists of two steps at each iteration: the expectation step, in which the current unobserved regime transition is estimated from new data using previous recursive estimates, and the minimization step, in which the MAR parameter estimates are recursively updated following a minimization direction. Details of implementation of the REM algorithm are given and its finite-sample performance is shown via simulation experiments. In particular, the EM and REM provide roughly similar estimates, especially for moderate and long time series.

关键词： mixture autoregression recursive prediction error method recursive weighted least squares EM algorithm recursive EM algorithm AMS 2000 Primary 62M10 Secondary 60F99

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Parameter Estimation for OEMA ModelsWith Dual-Rate Input-Output Data

Parameter Estimation for OEMA ModelsWith Dual-Rate Input-Out...

引用

31st Chinese Control Conference

作者： Ding Jie Yin Jun Zhu Songhao Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210003 Jiangsu Peoples R China Donghua Univ Sch Informat Sci & Technol Shanghai 201620 Peoples R China

ISBN: (纸本)9789881563811

This paper deals with the parameter estimation problems of the dual-rate sampled-data systems. The output error models with colored noise are considered as the nature models for systems in industries and thus are taken to model the dual-rate sampled-data systems in this paper. The recursive prediction error method is proposed to estimate the parameters directly from the dual-rate sampled data, and an auxiliary model is employed to estimate the noise-free system output. Simulation is given to test and illustrate the proposed algorithm in the paper.

关键词： Output error Model Dual-Rate Sampled Data Auxiliary Model recursive prediction error method

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