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Analysis of the Kalman filter based estimation algorithm: an orthogonal decomposition approach

AUTOMATICA 2004年第1期40卷 5-19页

作者： Cao, LY Schwartz, HM Carleton Univ Dept Syst & Comp Engn Ottawa ON K1S 5B6 Canada Micro Opt Design Corp Moncton NB E1E 4M3 Canada

In this paper we shall provide new analysis on some fundamental properties of the Kalman filter based parameter estimation algorithms using an orthogonal decomposition approach based on the excited subspace. A theoretical analytical framework is established based on the decomposition of the covariance matrix, which appears to be very useful and effective in the analysis of a parameter estimation algorithm with the existence of an unexcited subspace. The sufficient and necessary condition for the boundedness of the covariance matrix in the Kalman filter is established. The idea of directional tracking is proposed to develop a new class of algorithms to overcome the windup problem. Based on the orthogonal decomposition approach two kinds of directional tracking algorithms are proposed. These algorithms utilize a time-varying covariance matrix and can keep stable even in the case of unsufficient and/or unbounded excitation. (C) 2003 Elsevier Ltd. All rights reserved.

关键词： Kalman filter recursive parameter estimation least squares algorithm windup directional tracking

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Development and implementation of control-oriented models for terminal heating and cooling units

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ENERGY AND BUILDINGS 2016年 121卷 78-91页

作者： Gunay, H. Burak O'Brien, William Beausoleil-Morrison, Ian Bisaillon, Philippe Shi, Zixiao Carleton Univ Dept Civil & Environm Engn 1125 Colonel By Dr Ottawa ON K1S 5B6 Canada Carleton Univ Dept Mech & Aerosp Engn Ottawa ON K1S 5B6 Canada

Two control-oriented models that can predict the temperature of a perimeter office space were developed by using the data gathered from light intensity, motion and temperature sensors, and terminal heating and cooling units. One model had five unknown parameters while the second had ten unknown parameters and an immeasurable state. The models' parameters were estimated in recursion by employing the Extended Kalman Filter. The appropriateness of the models to the dataset was analyzed through a residual analysis, and the predictive accuracy of the models was contrasted. Both models could make offline predictions over a two day horizon at less than 0.75 degrees C mean absolute error. It was concluded that the one-state model was able to mimic the temperature response of small perimeter office spaces parsimoniously. The one-state model was implemented inside four building controllers serving eight private office spaces. In tandem with Gunay et al. [1]'s occupancy-learning algorithm, the one-state model was employed to determine optimal start and stop times for the temperature setback periods. Results of this implementation indicated that the duration of the weekday temperature setback periods could be increased more than 50% for both heating and cooling in contrast to the default control scheme. Energy Plus simulation results suggest that this accounts for about 30% reduction in heating and 13% reduction in cooling loads without affecting the indoor air temperature during occupied periods. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Terminal HVAC controls Reduced-order models Building energy efficiency Extended Kalman filters recursive parameter estimation Self-adaptive models

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A heuristic reference recursive recipe for adaptively tuning the Kalman filter statistics part-1: formulation and simulation studies

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2016年第12期41卷 1473-1490页

作者： Ananthasayanam, M. R. Mohan, M. Shyam Naik, Naren Gemson, R. M. O. Indian Inst Sci Dept Aerosp Engn Bangalore India Indian Inst Technol Kanpur Uttar Pradesh India Indian Inst Technol Dept Elect Engn Kanpur Uttar Pradesh India Hindustan Aeronaut Ltd Bangalore Karnataka India

Since the innovation of the ubiquitous Kalman filter more than five decades back it is well known that to obtain the best possible estimates the tuning of its statistics , , , R and Q namely initial state and covariance, unknown parameters, and the measurement and state noise covariances is very crucial. The manual and other approaches have not matured to a routine approach applicable for any general problem. The present reference recursive recipe (RRR) utilizes the prior, posterior, and smoothed state estimates as well as their covariances to balance the state and measurement equations and thus form generalized cost functions. The filter covariance at the end of each pass is heuristically scaled up by the number of data points and further trimmed to provide the for subsequent passes. The importance of as the probability matching prior between the frequentist approach via optimization and the Bayesian approach of the Kalman filter is stressed. A simultaneous and proper choice for Q and R based on the filter sample statistics and other covariances leads to a stable filter operation after a few iterations. A typical simulation study of a spring, mass and damper system with a weak nonlinear spring constant by RRR shows it to be better than earlier techniques. Part-2 of the paper further consolidates the present approach based on an analysis of real flight test data.

关键词： Adaptive EKF expectation maximization maximum likelihood covariance matching recursive parameter estimation Cramer Rao bound probability matching prior

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Online Speech Dereverberation Using Kalman Filter and EM Algorithm

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2015年第2期23卷 394-406页

作者： Schwartz, Boaz Gannot, Sharon Habets, Emanuel A. P. Bar Ilan Univ Fac Engn IL-5290002 Ramat Gan Israel Joint Inst Univ Erlangen Nuremberg & Fraunhofer I Int Audio Labs Erlangen D-91058 Erlangen Germany

Speech signals recorded in a room are commonly degraded by reverberation. In most cases, both the speech signal and the acoustic system of the room are unknown and time-varying. In this paper, a scenario with a single desired sound source and slowly time-varying and spatially-white noise is considered, and a multi-microphone algorithm that simultaneously estimates the clean speech signal and the time-varying acoustic system is proposed. The recursive expectation-maximization scheme is employed to obtain both the clean speech signal and the acoustic system in an online manner. In the expectation step, the Kalman filter is applied to extract a new sample of the clean signal, and in the maximization step, the system estimate is updated according to the output of the Kalman filter. Experimental results show that the proposed method is able to significantly reduce reverberation and increase the speech quality. Moreover, the tracking ability of the algorithm was validated in practical scenarios using human speakers moving in a natural manner.

关键词： Dereverberation recursive parameter estimation recursive expectation-maximization convolution in STFT

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Improved recursive Newton-type algorithm for power system relaying and measurement

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IEE PROCEEDINGS-GENERATION TRANSMISSION AND DISTRIBUTION 1998年第1期145卷 15-20页

作者： Terzija, VV Univ Belgrade Fac Elect Engn YU-11001 Belgrade Yugoslavia

A new recursive Newton-type algorithm suitable for measurement applications in power systems is presented. In an improved form it is used for power system frequency, phasors and spectra estimation. The key algorithm improvement is provided through a new forgetting factor tuning procedure, by which the speed of convergence and accuracy are improved. The results of computer simulation, laboratory testing and offline full-scale real-life data processing are given to show the main algorithm properties.

关键词： compute relaying forgetting factor harmonic and frequency measurement recursive parameter estimation

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A recursive blind adaptive identification algorithm and its almost sure convergence

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2007年第6期54卷 1380-1388页

作者： Radenkovic, Miloje S. Bose, Tamal Univ Colorado Dept Elect Engn Denver CO 80217 USA Hlth Sci Ctr Denver CO 80217 USA Utah State Univ Dept Elect & Comp Engn Logan UT 84322 USA

This paper presents a novel blind adaptive identification algorithm based on least-squares type arguments. parameter estimates are recursively updated with each output measurement, without resorting to any matrix inversion operation. It is proved that the parameter estimates converge almost surely (a.s.) toward a scalar multiple of the true parameters. Possible application of this algorithm to the channel equalization problem is discussed.

关键词： almost sure eigenvalue blind adaptive identification blind equalization recursive parameter estimation

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recursive least squares identification of heat exchanger system using block-structured models

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING 2022年第4期236卷 870-879页

作者： Gupta, Saurav Padhee, Subhransu Pekar, Libor Vellore Inst Technol Sch Elect Engn SENSE Chennai Tamil Nadu India Sambalpur Univ Dept Elect & Elect Engn Inst Informat Technol Burla 768019 Odisha India Tomas Bata Univ Zlin Fac Appl Informat Dept Automat & Control Engn Zlin Czech Republic Coll Polytech Jihlava Dept Tech Studies Jihlava Czech Republic

This study provides a recursive parametric identification scheme for a liquid-saturated steam heat exchanger system. The recursive identification scheme uses block-structured Wiener and Hammerstein models as model structure and recursive least squares estimation scheme as the parameter estimation method. The estimated block-oriented model provides higher accuracy of estimation than linear models provided in the literature. From the simulation results, it is observed that the Wiener model can provide 88% goodness-of-FIT, whereas Hammerstein model can provide 96% goodness-of-FIT using the said technique.

关键词： Wiener model Hammerstein model heat exchanger recursive parameter estimation

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Development of an adaptive and explicit dual model predictive controller based on generalized orthogonal basis filters

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JOURNAL OF PROCESS CONTROL 2019年 83卷 196-214页

作者： Kumar, Kunal Patwardhan, Sachin C. Noronha, Santosh Indian Inst Technol Syst & Control Engn Mumbai 400076 Maharashtra India Indian Inst Technol Dept Chem Engn Mumbai 400076 Maharashtra India

A significant fraction of industrial MPC schemes employs linear prediction models. Closed loop performance of a linear model based MPC scheme can deteriorate over a period of time if the prediction model is not updated to account for the changing operating conditions. An effective way of handling this problem is to employ dual control, which directs the plant output towards a reference setpoint and simultaneously injects probing signals into the plant to get information-rich data which can be used for model identification under closed loop conditions. In this work, two multivariable adaptive dual MPC (ADMPC) schemes are developed based on MISO output error models that are parameterized using generalized orthonormal basis filters (GOBF). A nominal model is initially developed using offline identification exercise. The Fourier coefficients of the GOBF models are subsequently updated online using recursive parameter estimators. Also, to deal with large magnitude changes in the operating conditions, a shifting time window based approach is developed that can track variations of the GOBF poles online. To begin with, starting from a stochastic infinite horizon constrained optimal control problem, a computationally tractable finite horizon surrogate of the optimal control problem is derived. The surrogate problem includes terms that are sensitive to the parameter covariance. Based on the surrogate optimal control problem, two variants of ADMPC are proposed, viz. one that uses a fixed Fourier basis and another that uses a time varying Fourier basis. The effectiveness of the ADMPC schemes is evaluated by conducting stochastic simulations and experimental studies on the benchmark quadruple tank process. Analysis of the simulation results reveals that the proposed ADMPC schemes are able to achieve better tracking and regulatory performances when compared to the conventional adaptive MPC (AMPC). The simulation exercise also reveals that the proposed approach provides sufficient

关键词： Adaptive control Dual control Model predictive control recursive parameter estimation Orthogonal basis filters

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Online elicitation of Mamdani-type fuzzy rules via TSK-based generalized predictive control

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 2003年第3期33卷 465-475页

作者： Mahfouf, M Abbod, MF Linkens, DA Univ Sheffield Dept Automat Control & Syst Engn Sheffield S1 3JD S Yorkshire England

Many synergies have been proposed between soft-computing techniques, such as neural networks (NNs), fuzzy logic (FL), and genetic algorithms (GAs), which have shown that such hybrid structures can work well and also add more robustness to the control system design. In this paper, a new control architecture is proposed whereby the on-line generated fuzzy rules relating to the self-organizing fuzzy logic controller (SOFLC) are obtained via integration with the popular generalized predictive control (GPC) algorithm using a Takagi-Sugeno-Kang (TSK)-based controlled autoregressive integrated moving average (CARIMA) model structure. In this approach, GPC replaces the performance index (PI) table which, as an incremental model, is traditionally used to discover, amend, and delete the rules. Because the GPC sequence is computed using predicted future outputs, the new hybrid approach rewards the time-delay very well. The new generic approach, named generalized predictive self-organizing fuzzy logic control (GPSOFLC), is simulated on a well-known nonlinear chemical process, the distillation column, and is shown to produce an effective fuzzy rule-base in both qualitative (minimum number of generated rules) and quantitative (good rules) terms.

关键词： adaptive Mamdani-type rules performance index (PI) predictive control recursive parameter estimation self-organizing Takagi-Sugeno-Kang (TSK) models

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Application of neural network and time series techniques in wool growth modeling

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TRANSACTIONS OF THE ASAE 2000年第1期43卷 139-144页

作者： Hong, F Tan, J McCall, DG Univ Missouri Dept Biol & Agr Engn Columbia MO 65211 USA AgRes Hamilton New Zealand

An application of neural network and time series techniques in animal system modeling is presented, which sheds lights on strategies to deal with some typical peculiarities in biological system data. A neural network model was developed to describe the multi-input and multi-output relationship from live weight gain, age, breed, and rearing status to monthly wool growth rates of sheep. constrained synaptic weights were imposed to avoid logically unrealistic functional relationships. ARMAX models were developed to describe the dynamics in wool growth rate and to analyze the time relationship between live weight gain and wool growth rate. The recursive least-squares parameter estimation algorithm was employed for effective use of short-term and multi-subject data in developing time series models.

关键词： modeling neural network time series wool growth recursive parameter estimation

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