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43rd Chinese Control Conference (CCC)

作者： Zhang, Xiufeng Tian, Zihan Zhang, Anqi Dalian Maritime Univ Nav Coll Dalian 116026 Peoples R China

ISBN: (纸本)9798350366907;9789887581581

The mathematical model of ship maneuvering motion is the core of ship motion simulation and control, and it is still a difficult problem to accurately establish the mathematical model that reflects the actual ship motion state. In this paper, based on the typical maneuvering test data of the real ship, the recursive prediction error (RPE) method is used to identify the parameters of the 4-DOF motion mathematical model of the real ship, and the grey box mathematical model driven by the real ship data is established. Compared with the experimental data outside the training sample data set, the results have higher accuracy and reliability. This method is suitable for the motion prediction of real ship based on typical test data and can be used in the modeling of intelligent ship motion control and autonomous navigation motion prediction mathematical model.

关键词： Parameter identification Ship motion predict Typical ship sea trial recursive prediction error method full-scale YUKUN

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A Low-Complexity DPD to Fully Linearize the Power Amplifiers in a mMIMO Transmitter 59

A Low-Complexity DPD to Fully Linearize the Power Amplifiers...

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59th Annual IEEE International Conference on Communications (IEEE ICC)

作者： Prasad, Ganesh Johansson, Hakan Laskar, Rabul Hussain Linkoping Univ Dept Elect Engn Div Commun Syst Linkoping Sweden Natl Inst Technol Silchar Dept Elect & Commun Engn Silchar India

ISBN: (纸本)9781728190549

A radio frequency (RF) power amplifier (PA) is crucial to enhance the signal to transmit via antenna over long distances. High-power transmission often leads to nonlinear behavior in the PA, necessitating the use of digital predistortion (DPD) signal processing to restore linearity by preinverting the nonlinearity. However, when dealing with a massive MIMO (mMIMO) transmitter with numerous PAs, a single DPD is not enough, and allocating a separate DPD for each PA is intricate and cost-inefficient. In this study, we tackle these challenges through our proposed low-complexity DPD (LC-DPD) architecture. The LC-DPD has the flexibility to choose the parameters of its architecture as per the desired tradeoff between the performance and complexity in the linearization. It employs learning of its coefficients through algorithms utilizing an indirect learning architecture based recursive prediction error method (ILA-RPEM), which is adaptive and free from matrix inversions.

关键词： Digital predistortion signal processing indirect learning architecture recursive prediction error method

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Neural Networks for Optimal Control

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IFAC Proceedings Volumes 1995年第8期28卷 361-366页

作者： Ole Sørensen Aalborg University Inst. of Electronic Systems Dept. of Process Engineering Fredrik Bajers vej 7 DK-9220 Aalborg Ø Denmark phone +45 98 15 85 22 telefax +45 98 15 17 39

Two neural networks are trained to act as an observer and a controller, respectively, to control anon-linear, multi-variable process. The process model is the well-known Innovation State Space model. Firstly, the observer network is trained with a recursive prediction error method using a Gauss-Newton search direction to minimize the the prediction error. Next, the trained observer network is applied in a closed-loop simulation to train another neural network, the controller. During this training an optimal control cost function is minimized using a recursive, off-line, backward training method, similar to the Back Propagation Through Time (BPTT) method. Finally, a practical, non-linear, noisy and multi-variable example confirms, that the model and the training methods are a promising technique to control non-linear processes, which are difficult to model. 2 reseaux neuraux sont developpes pour agir respectivement comme un observateur et un controlleur, afin de controller un processus non lineaire et multi-variable. Le modele de ce processus est le bien connu modele des espaces d'etats (Innovation State Space Model). Premierement le reseau observateur est mis en place avec une methode recursive d'erreur de prediction (recursive prediction error method) utilisant une recherche de Gauss-Newton pour minimiser l'erreur de prediction. Ensuite ce reseau est applique dans une simulation a boucle fermee pour former un autre reseau neural, le controlleur. Pendant cette sequence de formation une fonction de cout de controle optimal est minimisee, utilisant une methode recursive, a priori, similaire a la propagation arriere a travers le temps (Back Propagation Through Time method). Finallement un exemple pratique, non lineaire, bruite, et multi-variable confirme que le modele et les methodes de mise en place de ce modele sont une technique promettante pour controler des processus non lineaires, qui sont difficiles a modeliser.

关键词： Neural network innovation model extended Kalmann filter recursive prediction error method non-linear control optimal control

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Identification methods for Nonlinear MISO Systems

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IFAC Proceedings Volumes 1987年第5期20卷 233-238页

作者： M. Kortmann H. Unbehauen Department of Electrical Engineering Ruhr-University Bochum Automatic Control Laboratory D-4630 Bochum 1 FRG

Two new on-line identification algorithms for the recursive estimation of the parameters of a multi-input single-output nonlinear process which can be described by a multi-input single-output Hammerstein model are proposed. The first method is an extension of the recursive least squares identification scheme for linear MISO systems while the other method is derived from a recursive prediction error algorithm. It is shown that the second method has the important property for on-line identification that no redundancy in the parameters to be estimated results as in the first algorithm. In contrast to the first method the second algorithm has the advantage that no extended dynamic parts of the Hammerstein submodels are obtained by application of the estimation procedure. The results of a simulation study are included to illustrate the efficiency of the derived methods.

关键词： Identification nonlinear systems parameter estimation difference equations recursive least-squares method recursive prediction error method

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Model-based estimation of the torque characteristics of an automated dual-clutch transmission in wet technology

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IFAC Proceedings Volumes 2010年第7期43卷 455-460页

作者： A. Tarasow C. Bohn M. Vinaske G. Wachsmuth R. Serway Institute of Electrical Information Technology Clausthal University of Technology Clausthal-Zellerfeld Germany IAV GmbH Ingenieurgesellschaft Auto und Verkehr Berlin Germany

In many modern automotive driveline concepts the power transfer between the drive input and output is conducted by a friction clutch. The drivability of a vehicle and the exploitation of the engine power depend primarily on the interaction between the available torque and the torque delivered through the clutch. The clutch characteristic is therefore important in this context. A clutch characteristic defines the relationship between the actuating variable of the contact force and the torque delivered through the clutch. In this paper two different model approaches are discussed for estimating of the clutch characteristics using an adaptive, extended Kalman filter in combination with the recursive prediction error method. The two approaches differ in the level of abstraction and itemization used in the prediction models. The verification studies indicate that both modelling approaches allow an unbiased estimation of the clutch characteristic. However, model-dependent differences arise in transient behaviour (such as convergence time) as well as in the implementation and computational costs.

关键词： adaptive Kalman filter parameter estimation recursive prediction error method clutch control dual-clutch transmission automotive driveline control

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Adaptive Control in Practice: Possibilities and Constraints

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IFAC Proceedings Volumes 1985年第15期18卷 133-137页

作者： M.W. Tijsseling H.J. de Groot Dow Chemical Company Terneuzen The Netherlands Gist Brocades Company Delft The Netherlands

Feedback is the most frequently used method for controlling a process variable. It does not require precise tuning, but it may turn out slow for fast upsets. Feedforward is able to handle these disturbances if it is tuned taylormade to the process dynamics. Non linear systems may need adaptive feedforward. This paper describes a cooler with non linear dynamics where adaptive control can not be used. However, the identification algorithm gives enough information to tune the feedforward properly.

关键词： Discrete time systems adaptive control feedforward control direct digital control identification recursive prediction error method

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Application of RPE in Ship Maneuvering Motion Parameter Identification Based on Ship Sea Trial Data

Application of RPE in Ship Maneuvering Motion Parameter Iden...

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第43届中国控制会议

作者： Xiufeng Zhang Zihan Tian Anqi Zhang Navigation College Dalian Maritime University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The mathematical model of ship maneuvering motion is the core of ship motion simulation and control, and it is still a difficult problem to accurately establish the mathematical model that reflects the actual ship motion state. In this paper, based on the typical maneuvering test data of the real ship, the recursive prediction error(RPE) method is used to identify the parameters of the 4-DOF motion mathematical model of the real ship, and the grey box mathematical model driven by the real ship data is established. Compared with the experimental data outside the training sample data set, the results have higher accuracy and reliability. This method is suitable for the motion prediction of real ship based on typical test data and can be used in the modeling of intelligent ship motion control and autonomous navigation motion prediction mathematical model.

关键词： Parameter identification Ship motion predict Typical ship sea trial recursive prediction error method full-scale YUKUN

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Parameter Estimation for OEMA Models With Dual-Rate Input-Output Data

Parameter Estimation for OEMA Models With Dual-Rate Input-Ou...

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第三十一届中国控制会议

作者： DING Jie~1,YIN Jun~2,ZHU Songhao~1 1.College of Automation,Nanjing University of Posts & Telecommunications,Nanjing 210003,P.R.China 2.School of Information Science & Technology,Donghua University,Shanghai,201620,P.R.China

This paper deals with the parameter estimation problems of the dual-rate sampled-data *** 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 *** 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 *** 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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PERFORMANCE BOUNDS FOR TRACKING ALGORITHMS BASED ON A TIME-VARYING THIRD-ORDER NONLINEAR MODEL

PERFORMANCE BOUNDS FOR TRACKING ALGORITHMS BASED ON A TIME-V...

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IET International Conference on Radar Systems (Radar 2007)

作者： Anders Erik Nordsjo Saab Microwave Systems AB Jarfalla Sweden

The problem of deriving accurate algorithms for tracking of maneuvering targets is central in many applications, such as radar and sonar. Assuming that a certain model is indeed capable of describing the unknown system, it is still important to have a measure of the achievable accuracy. Clearly, the Cramer-Rao inequality has been applied to a large number of different parameterized models to obtain a lower bound of the variance of any unbiased estimate of the true system parameters. Also, in a tracking scenario it is of great interest to be able to quantify the best possible performance that can be achieved. For this purpose certain time-varying Cramer-Rao lower bounds, CRB's are derived in the following. These bounds are based on the assumption of a third-order model which incorporates both time-varying and time-invariant parameters. In addition, possible measurement nonlinearities are taken into account. The so obtained CRB's depend on not only the unknown filter parameters but also certain second-order statistics, namely the variance of the (presumedly random) target acceleration and the measurement noise. In general, it is of great importance to be able to quantify the impact of different values of these quantities, which typically are unknown in practice.

关键词： Target Tracking Cramer-Rao Bounds recursive prediction error method

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