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algorithms for Estimation of the Assemblywise Power Distribution in WWER Type Reactors

IFAC Proceedings Volumes 1989年第16期22卷 333-336页

作者： R. Tsvetanov F.F. Pashchenko N.P. Kolev Institute of Control Science Profsoyuznaia 65 Moscow 117342 USSR Institute for Nuclear Research and Nuclear Energy baul. Lenin 12 Sofia 1784 Bulgaria

Two efficient algorithms for estimation of the assemblywise Power distribution on WWER type reactors are presented. The algorithms combine reference code pre-calculations with aposteriori corrections based on temperature measurements at the reactor core outlet. An adaptive multiplicative correction function and a quadratic performance criterion are used. The problem for estimation of the unknown coefficients in the correction function is solved in two ways: (a) using a recursive stochastic approximation algorithm, and (b) by a least squares algorithm with singular value decomposition techniques. The two algorithms were run with sets of measured data in order to provide results for a comprehensive comparison of their accuracy, efficiency and stability with respect to measurement errors. One of the presented procedures is implemented in a computerized power shape monitoring subsystem at the Kozlodui NPP.

关键词： Nuclear reactors power distribution estimation computer evaluation recursive algorithms stochastic approcimations least-squares approximations numerical methods

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Global recursive stochastic optimization

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IFAC Proceedings Volumes 1999年第2期32卷 5029-5034页

作者： Hai-Tao Fang Han-Fu Chen Laboratory of Systems and Control Institute of Systems of Science Chinese Academy of Sciences Beijing 100080 P.R.China

In this paper a new recursive algorithm for searching the global minimizer of a function is proposed when the function is observed with noise. The algorithm is based on switches between the stochastic approximation (SA) and the random search (RS) with stepsizes decreasing as SA develops and as RS takes steps. It is proved that the algorithm a.s. converges to the global minimizer and is asymptotically normal.

关键词： Random search Stochastic approximation recursive algorithms Global optimization

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Real-Time Adaptive State Space Controlled High Dynamic Linear Motor

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IFAC Proceedings Volumes 1997年第6期30卷 1275-1280页

作者： Harald G. Wild Stephen J. Dodds Department of Mechanical Engineering College of Engineering Berne CH-3014 Berne Switzerland Department of Electrical & Electronic Engineering University of East London Dagenham Essex RM82AS UK

Une commande adaptive basée sur une regulateur d'état avec observateur est developée pour positioner une charge mécanique variable avec un moteur linéaire. Afin de recevoir une bande passsante qui est équilibre aux hautes performances des ces types de moteurs, une fréquence d'échantillonage élevée dans une zone de 10 Khz à 20 Khz est nécessaire. Done une compensation d'une changement de masse en temps reel exige un algorithme très efficace par rapport de la vitesse de calcul. Un tel algorithme est proposé dans ce papier. L'algorithme est en état de calculer la masse et des components parasitaires, comme la friction, avec 5 multiplications et 5 additions dans une boucle d'irération. A full adaptive observer based state space controller is developed for positioning of a mechanical load using linear synchronous motors. To fully exploit the high dynamic response capabilities of such linear motors, it is necessary to operate at relatively high sampling frequencies in the range of 10 Khz to 20 Khz. To compensate the change of the moved mass during system operation, an computational efficiency on-line parameter estimation algorithm is therefore developed here. The algorithm can estimate the load mass and parasitic components such as the friction with only 5 multiplication's and 5 additions per iteration.

关键词： Electric Machines Model Reduction State Feedback Mcxlel Reference Adaptive Control Real Time Systems recursive algorithms

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Adaptive recursive Identification with Model Change Prediction

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IFAC Proceedings Volumes 1994年第11期27卷 267-272页

作者： Eugen Saffert Technical University of Ilmenau Department of Computer Science and Automation PF 327 D-98684 Ilmenau Germany

This paper presents a new identification algorithm that is particular suitable for the parameter estimation of instationary processes. It estimates the rate of change of an instationary model along with the model parameters. This approach allows the prediction of future model parameters which results in a faster adaptation to instationary processes with a smaller prediction error. Based on a Bayesian probability concept the paper derives the structure of the identification algorithm which is then developed into a set of recursive update equations. A simulation example shows the advantages of the new algorithm over standard RLS estimation.

关键词： System identification parameter estimation prediction error estimation multivariable systems time-varying systems stochastic systems multivariate distributions Bayes methods recursive algorithms

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Application of the Stochastic Gradient Method to the Control of a Non-Linear Process Modelled as a Markov Chain

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IFAC Proceedings Volumes 1985年第5期18卷 357-362页

作者： A. Majdoul L.E.E.S.A. Faculte de Science B.P. 1014 Rabat Morocco

Computing the control law for non-linear systems can be facilitated by using a stochastic automaton model. The automaton adapts i5 policy by means of a stochastic gradient algorithm, that depends on the estimation of ... 详细信息

关键词： Non-linear process markov chain learning automata recursive algorithms

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ASYMPTOTICALLY OPTIMAL SMOOTHING OF AVERAGED LMS FOR REGRESSION PARAMETER TRACKING

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IFAC Proceedings Volumes 2002年第1期35卷 163-168页

作者： Alexander V. Nazin Lennart Ljung Division of Automatic Control Linköping University SE-58183 Linköping Sweden Institute of Control Sciences Profsoyuznaya str. 65 117997 Moscow Russia

The sequence of estimates formed by the LMS algorithm for a standard linear regression estimation problem is considered. In this paper it is first shown that smoothing the LMS estimates using a matrix updating will lead to smoothed estimates with optimal tracking properties, also in the case the true parameters are slowly changing as a random walk. The choice of smoothing matrix should be tailored to the properties of the random walk. Second, it is shown that the same accuracy can be obtained also for a modified algorithm, SLAMS, which is based on averages and requires much less computations.

关键词： Regression Parameter estimation Random walk recursive algorithms Tracking Smoothing Mean-square error Asymptotic properties

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recursive System Identification Using Instrumental Variable Subspace Tracking

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IFAC Proceedings Volumes 1997年第11期30卷 1579-1584页

作者： Tony Gustafsson Signal Processing Group Department of Applied Electronics Chalmers University of Technology S-412 96 Gothenburg Sweden

In this paper a recursive instrumental variable (IV) based subspace identification algorithm is proposed. The basic idea of the algorithm is to utilize the close relationship with sensor array signal processing. Utilizing this relationship, an IV based subspace tracking algorithm originally developed for direction of arrival tracking is applied to track the subspace spanned by the observability matrix. The main features of the proposed algorithm are, a) a relatively low computational complexity, and b) the ability to handle colored disturbances that are mutually correlated.

关键词： recursive algorithms Subspace methods Identification Tracking

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Parameter Estimation: A Second Order recursive Least Squares Algorithm

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IFAC Proceedings Volumes 1994年第8期27卷 1381-1386页

作者： Holger Broman Åke Andersson Chalmers University of Technology Department of Applied Electronics S-412 96 Göteborg Sweden

A second order recursive least squares algorithm is derived. It is shown that the algorithm encompasses both the RLS and the LMS algorithms as special cases. The computational complexity is the same as for the RLS algorithm, but requires some extra memory storage. The associated Ordinary Differential Equation (ODE) for the algorithm is proven to be globally exponentially stable. Further, it is demonstrated that the proposed algorithm has a higher ability to track time-varying signals than has the RLS-algorithm. The proposed algorithm especially handles those situations well where there is a simultaneous system change and decrease of signal power.

关键词： Adaptive algorithms recursive algorithms Time-varying Systems Parameter estimation

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RECURRENT NEURAL CONTROL OF WASTEWATER TREATMENT BIOPROCESS VIA MARQUARDT LEARNING

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IFAC Proceedings Volumes 2007年第4期40卷 289-294页

作者： Ieroham S. Baruch Saul F. Escalante Carlos R. Mariaca-Gaspar Josefina Barrera-Cortes Dept. of Automatic Control CINVESTAV-IPN Mexico Dept. of Biotechnology and Bioengineering CINVESTAV-IPN Mexico

The aim of this paper is to propose a new Recurrent Neural Network (RTNN) topology and a dynamic recursive Levenberg-Marquardt algorithm of its learning capable to estimate the states and parameters of a highly nonlinear wastewater treatment bioprocess. The proposed RTNN identifier is implemented in a direct adaptive control scheme incorporating feedback/feedforward recurrent neural controllers and a noise rejecting filter. The proposed control scheme is applied for continuous wastewater treatment bioprocess plant model, taken from the literature, where a good convergence, noise filtering and a low Mean Squared Error of reference tracking is achieved.

关键词： adaptive control backpropagation algorithms biotechnology feedback control feedforward control identification state estimation learning algorithms neural network models recursive algorithms

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A Branching Strategy for Exploring the Objective Space in Bi-objective Optimization Problems

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IFAC-PapersOnLine 2022年第7期55卷 364-369页

作者： Ihab Hashem Viviane De Buck Seppe Seghers Jan Van Impe

The process of optimization of chemical/ biochemical processes can often involve multiple conflicting objectives. This gives rise to a class of problems called multi-objective optimization problems. Solving such problems results in an infinite set of points, the Pareto set, which includes all the solutions in which no objective can be improved without worsening at least one other objective. In this paper, we propose a new strategy that is inspired by branching phenomena in nature for exploring the objective space to obtain a representation of the Pareto set. The algorithm starts from a single point in the objective space, and systematically constructs branches towards the Pareto front by solving correspondingly-modified subproblems. This process continues till points that lie at the Pareto front are obtained. This way, it ensures that no region in the objective space gets explored more than a single time. Additionally, using a proximity parameter, the branches density can be controlled, consequently leading to controlling the resolution of the Pareto front. The proposed method has been applied to a numerical bi-objective optimization problem as well as the problem of the bi-objective control of a William-Otto reactor. Results show that the new algorithm has managed to obtain a Pareto front with adaptive resolution where the areas with high trade-offs are represented with higher points density.

关键词： Multi-objective optimization Optimal control recursive algorithms Process industry

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