Estimating Disturbance Covariances From Data For Improved Control Performance

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[1]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[2]  R. G. Reynolds.  Robust estimation of covariance matrices , 1990 .

[3]  H. Neudecker.  Some Theorems on Matrix Differentiation with Special Reference to Kronecker Matrix Products , 1969 .

[4]  Mats Viberg,et al.  Subspace-based methods for the identification of linear time-invariant systems , 1995, Autom..

[5]  R. Lynn Kirlin,et al.  Robust Adaptive Kalman Filtering with Unknown Inputs , 1986, 1986 American Control Conference.

[6]  J. Leathrum.  On sequential estimation of state noise variances , 1981 .

[7]  C. Price.  An analysis of the divergence problem in the Kalman filter , 1968 .

[8]  James B. Rawlings,et al.  Tutorial overview of model predictive control , 2000 .

[9]  L. Jetto,et al.  Low a priori statistical information model for optimal smoothing and differentiation of noisy signals , 1994 .

[10]  Maciej Niedzwiecki.  Identification of nonstationary stochastic systems using parallel estimation schemes , 1990 .

[11]  W. E. Larimore,et al.  Order-recursive factorization of the pseudoinverse of a covariance matrix , 1988, Proceedings of the 27th IEEE Conference on Decision and Control.

[12]  Ian Postlethwaite,et al.  Multivariable Feedback Control: Analysis and Design , 1996 .

[13]  Nazario D. Ramirez-Beltran,et al.  Autoregressive and adaptive estimation with an application to hurricane track prediction , 1995 .

[14]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[15]  S. Mahmood,et al.  Multivariable adaptive model algorithmic control , 1984, The 23rd IEEE Conference on Decision and Control.

[16]  D. Alspach,et al.  A parallel filtering algorithm for linear systems with unknown time varying noise statistics , 1974 .

[17]  P. Young,et al.  Comments on "Identification of optimum filter steady-state gain for systems with unknown noise covariances" , 1974 .

[18]  Michel Verhaegen,et al.  A Novel Non-Iterative Mimo State Space Model Identification Technique , 1991 .

[19]  James B. Rawlings,et al.  Online monitoring of MPC disturbance models using closed-loop data , 2003, Proceedings of the 2003 American Control Conference, 2003..

[20]  W. E. Larimore,et al.  Automated and optimal system identification by canonical variables , 1994, Proceedings of 1994 American Control Conference - ACC '94.

[21]  R. Mehra.  On-line identification of linear dynamic systems with applications to Kalman filtering , 1971 .

[22]  Mohamed Najim,et al.  A single microphone Kalman filter-based noise canceller , 1999, IEEE Signal Processing Letters.

[23]  W. E. Larimore,et al.  Automated multivariable system identification and industrial applications , 1999, Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251).

[24]  Eduardo D. Sontag,et al.  Mathematical control theory: deterministic finite dimensional systems (2nd ed.) , 1998 .

[25]  Stephen J. Wright,et al.  Nonlinear Model Predictive Control via Feasibility-Perturbed Sequential Quadratic Programming , 2004, Comput. Optim. Appl..

[26]  Suwanchai Sangsuk-Iam,et al.  Analysis of continuous-time Kalman filtering under incorrect noise covariances , 1988, Autom..

[27]  Lennart Ljung,et al.  A statistical perspective on state-space modeling using subspace methods , 1991, [1991] Proceedings of the 30th IEEE Conference on Decision and Control.

[28]  C. Chatfield.  SPECTRAL ANALYSIS AND TIME SERIES: Volume 1 Univariate Series Volume 2 Multivariate Series, Prediction and Control , 1982 .

[29]  Guanrong Chen,et al.  A modified adaptive Kalman filter for real-time applications , 1991 .

[30]  F. Kung,et al.  Adaptive fault detection in real-time GPS positioning , 2000 .

[31]  R.L.T. Hampton.  On unknown state-dependent noise, modeling errors, and adaptive filtering☆ , 1975 .

[32]  R. Mehra.  On the identification of variances and adaptive Kalman filtering , 1970 .

[33]  D. G. Watts,et al.  Spectral analysis and its applications , 1968 .

[34]  Franklin T. Luk,et al.  Canonical correlations and generalized SVD: Applications and new algorithms , 1989 .

[35]  Samer S. Saab,et al.  Sensitivity of discrete-time Kalman filter to statistical modeling errors , 1999 .

[36]  K. Liu,et al.  Identification and Control of NASA's ACES Structure , 1991, 1991 American Control Conference.

[37]  Jaleel Valappil,et al.  Systematic estimation of state noise statistics for extended Kalman filters , 2000 .

[38]  Louis L. Scharf,et al.  On stochastic approximation and an adaptive Kalman filter , 1972, CDC 1972.

[39]  Lennart Ljung,et al.  A simple start-up procedure for canonical form state space identification, based on subspace approximation , 1991, [1991] Proceedings of the 30th IEEE Conference on Decision and Control.

[40]  Chaw-Bing Chang,et al.  Application of state estimation to target tracking , 1984 .

[41]  Lang Hong.  Adaptive distributed filtering in multicoordinated systems , 1991 .

[42]  J. Rawlings,et al.  The stability of constrained receding horizon control , 1993, IEEE Trans. Autom. Control..

[43]  T. Bohlin.  Four Cases of Identification of Changing Systems , 1976 .

[44]  Sigurd Skogestad,et al.  Probably the best simple PID tuning rules in the world , 2001 .

[45]  Demetrios G. Lainiotis,et al.  Optimal Estimation in the Presence of Unknown Parameters , 1969, IEEE Trans. Syst. Sci. Cybern..

[46]  Sigurd Skogestad,et al.  Robust control of ill-conditioned plants: high-purity distillation , 1988 .

[47]  Bo Wahlberg,et al.  Analysis of state space system identification methods based on instrumental variables and subspace fitting , 1997, Autom..

[48]  Arthur E. Bryson,et al.  Applied Optimal Control , 1969 .

[49]  Dietmar Bauer,et al.  Consistency and asymptotic normality of some subspace algorithms for systems without observed inputs , 1999, Autom..

[50]  D. Alspach.  Comments on "On the identification of variances and adaptive Kalman filtering" , 1972 .

[51]  George B. Kleindorfer,et al.  CONSISTENT ESTIMATES OF THE PARAMETERS OF A LINEAR SYSTEM. , 1969 .

[52]  Petros G. Voulgaris,et al.  On optimal ℓ∞ to ℓ∞ filtering , 1995, Autom..

[53]  James B. Rawlings,et al.  Model predictive control with linear models , 1993 .

[54]  Thomas Kailath,et al.  Linear Systems , 1980 .

[55]  K. Åström.  Introduction to Stochastic Control Theory , 1970 .

[56]  Michel Verhaegen,et al.  Application of a subspace model identification technique to identify LTI systems operating in closed-loop , 1993, Autom..

[57]  Y. Bar-Shalom,et al.  A recursive multiple model approach to noise identification , 1994 .

[58]  W. Larimore.  System Identification, Reduced-Order Filtering and Modeling via Canonical Variate Analysis , 1983, 1983 American Control Conference.

[59]  Michel Verhaegen,et al.  Identification of the deterministic part of MIMO state space models given in innovations form from input-output data , 1994, Autom..

[60]  Stephen Barnett,et al.  Introduction to Mathematical Control Theory , 1975 .

[61]  Joydeep Ghosh,et al.  Hierarchical adaptive Kalman filtering for interplanetary orbit determination , 1998 .

[62]  U. B. Desai,et al.  A Generalized Approach to q-Markov Covariance Equivalent Realizations for Discrete Systems , 1987, 1987 American Control Conference.

[63]  Bo Wahlberg,et al.  On Consistency of Subspace Methods for System Identification , 1998, Autom..

[64]  Piotr J. Wojcik,et al.  On-line estimation of signal and noise parameters with application to adaptive Kalman filtering , 1991 .

[65]  Torben Knudsen.  Consistency analysis of subspace identification methods based on a linear regression approach , 2001, Autom..

[66]  Duncan A. Mellichamp,et al.  Identification of chemical processes using canonical variate analysis , 1990, 29th IEEE Conference on Decision and Control.

[67]  K. Tajima.  Estimation of steady-state Kalman filter gain , 1978 .

[68]  I. G. BONNER CLAPPISON.  Editor , 1960, The Electric Power Engineering Handbook - Five Volume Set.

[69]  D. Dee.  On-line Estimation of Error Covariance Parameters for Atmospheric Data Assimilation , 1995 .

[70]  Andrew H. Jazwinski,et al.  Adaptive filtering , 1969, Autom..

[71]  Joe Brewer,et al.  Kronecker products and matrix calculus in system theory , 1978 .

[72]  Yoram Baram.  Identifying nonstationary measurement noise in linear systems , 1982, IEEE Trans. Inf. Theory.

[73]  S. Skogestad.  Simple analytic rules for model reduction and PID controller tuning , 2004 .

[74]  S. Barnett,et al.  Simplification of the Lyapunov matrix equation A_{T}PA - P = -Q , 1974 .

[75]  Naresh K. Sinha.  Adaptive Kalman filtering using stochastic approximation , 1973 .

[76]  A. Jazwinski.  Limited memory optimal filtering , 1968 .

[77]  R. E. Kalman,et al.  New Results in Linear Filtering and Prediction Theory , 1961 .

[78]  Naresh K. Sinha,et al.  Adaptive state estimation for systems with unknown noise covariances , 1977 .

[79]  Thomas Kailath,et al.  Detection of signals by information theoretic criteria , 1985, IEEE Trans. Acoust. Speech Signal Process..

[80]  James B. Rawlings,et al.  Application of Autocovariance Least-Squares Methods to Laboratory Data , 2003 .

[81]  Amir Averbuch,et al.  Radar target tracking-Viterbi versus IMM , 1991 .

[82]  Bart De Moor,et al.  N4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems , 1994, Autom..

[83]  S. R. Searle,et al.  Matrix Algebra Useful for Statistics , 1982 .

[84]  Lennart Ljung,et al.  Performance of Subspace-Based System Identification Methods , 1993 .

[85]  A. Dobnikar,et al.  On-line correction of the gain in kalman filtering , 1994, Autom..

[86]  Robert H. Bishop,et al.  Adaptive Orbit Determination for Interplanetary Spacecraft , 1996 .

[87]  S. Godbole,et al.  Kalman filtering with no A-priori information about noise-White noise case: Part I: Identification of covariances , 1973, CDC 1973.

[88]  Gerald L. Smith,et al.  Sequential estimation of observation error variances in a trajectory estimation problem. , 1967 .

[89]  C. R. Cutler,et al.  Dynamic matrix control¿A computer control algorithm , 1979 .

[90]  S. Sangsuk-Iam,et al.  Analysis of discrete-time Kalman filtering under incorrect noise covariances , 1990 .

[91]  Minh Q. Phan,et al.  ESTIMATION OF KALMAN FILTER GAIN FROM OUTPUT RESIDUALS , 1993 .

[92]  A. Barraud.  ' An algorithm for solving the matrix equation X=FXFT + S' , 1978 .

[93]  J. Baillieul,et al.  Identification and filtering of nonlinear systems using canonical variate analysis , 1990, 29th IEEE Conference on Decision and Control.

[94]  Alexander Graham,et al.  Kronecker Products and Matrix Calculus: With Applications , 1981 .

[95]  R. Kashyap.  Maximum likelihood identification of stochastic linear systems , 1970 .

[96]  Jay H. Lee,et al.  Extended Kalman Filter Based Nonlinear Model Predictive Control , 1993, 1993 American Control Conference.

[97]  Louis L. Scharf,et al.  A Bayesian solution to the problem of state estimation in an unknown noise environment , 1974 .

[98]  P. Kalata,et al.  A polynomial algorithm for noise identification in linear systems , 1990, Proceedings. 5th IEEE International Symposium on Intelligent Control 1990.

[99]  J. Richalet,et al.  Model predictive heuristic control: Applications to industrial processes , 1978, Autom..

[100]  R. Grover Brown,et al.  A Kalman Filter Approach to Precision GPS Geodesy , 1983 .

[101]  James B. Rawlings,et al.  Chemical Reactor Analysis and Design Fundamentals , 2002 .

[102]  Andrew P. Sage,et al.  Adaptive filtering with unknown prior statistics , 1969 .

[103]  A. Roche,et al.  Organic Chemistry: , 1982, Nature.

[104]  S. N. Balakrishnan,et al.  Estimation of Process Noise Covariance in Homing Guidance , 1991, 1991 American Control Conference.

[105]  Dale E. Seborg,et al.  Identification of the Tennessee Eastman Challenge Process with Subspace Methods , 2000 .

[106]  Dale E. Seborg,et al.  On-line multivariable identification and control of chemical processes using canonical variate analysis , 1994, Proceedings of 1994 American Control Conference - ACC '94.

[107]  Bernard Friedland.  Estimating Noise Variances by Using Multiple Observers , 1982, IEEE Transactions on Aerospace and Electronic Systems.

[108]  Wang Tang,et al.  Application of multiple model estimation to a recursive terrain height correlation system , 1983 .

[109]  Wallace E. Larimore,et al.  Statistical optimality and canonical variate analysis system identification , 1996, Signal Process..

[110]  Wallace E. Larimore,et al.  Optimal Reduced Rank Modeling, Prediction, Monitoring and Control using Canonical Variate Analysis , 1997 .

[111]  Matthew L. Tyler,et al.  Estimation of cross-directional properties: Scanning vs. Stationary Sensors , 1995 .

[112]  J.-A. Guu,et al.  Tracking technique for manoeuvring target with correlated measurement noises and unknown parameters , 1991 .

[113]  Leonard Chin.  Adaptive Kalman filter for inertial navigation system accuracy improvement , 1978 .

[114]  M. Viberg.  Subspace-based state-space system identification , 2002 .

[115]  Robert E. Skelton,et al.  Identification of Linear Systems from their Pulse Responses , 1992, 1992 American Control Conference.

[116]  R. Mehra.  An algorithm to solve matrix equations PH^{T} = G and P = φ P φ T + γγ T , 1970 .

[117]  Jacques L. Willems,et al.  Divergence of the stationary Kalman filter for correct and for incorrect noise variances , 1992 .

[118]  Michael E. Hough.  Improved Performance of Recursive Tracking Filters Using Batch Initialization and Process Noise Adaptation , 1998 .

[119]  T. Moir,et al.  Optimal self-tuning filtering, prediction, and smoothing for discrete multivariable processes , 1984 .

[120]  Tae Yoon Um,et al.  Noise covariances estimation for systems with bias states , 2000, IEEE Trans. Aerosp. Electron. Syst..

[121]  Maciej Niedzwiecki.  Identification of time-varying systems using combined parameter estimation and filtering , 1990, IEEE Trans. Acoust. Speech Signal Process..

[122]  Alessandro Chiuso,et al.  Comparison of Two Subspace Identification Methods for Combined Deterministic-Stochastic System: Part 2 , 2000 .

[123]  H-K zou,et al.  The tracking of a manoeuvring object by using an adaptive Kalman filter , 2001 .

[124]  N. F. Toda,et al.  Divergence in the Kalman Filter , 1967 .

[125]  D. S. Bayard,et al.  An algorithm for state-space frequency domain identification without windowing distortions , 1992, [1992] Proceedings of the 31st IEEE Conference on Decision and Control.

[126]  Per Hagander,et al.  A self-tuning filter for fixed-lag smoothing , 1977, IEEE Trans. Inf. Theory.

[127]  Jen-Kuang Huang,et al.  Estimation of Steady-State Optimal Filter Gain From Nonoptimal Kalman Filter Residuals , 1994 .

[128]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[129]  P. Gutman,et al.  Tracking targets using adaptive Kalman filtering , 1990 .

[130]  T. W. Anderson.  ASYMPTOTIC THEORY FOR PRINCIPAL COMPONENT ANALYSIS , 1963 .

[131]  Manfred Morari,et al.  A general controller synthesis methodology based on the IMC structure and the H2-, H∞- and μ-optimal control theories , 1988 .

[132]  Michael Ghil,et al.  An efficient algorithm for estimating noise covariances in distributed systems , 1985 .

[133]  B. Moor,et al.  A geometrical strategy for the identification of state space models of linear multivariable systems with singular value decomposition , 1987 .

[134]  Raman K. Mehra,et al.  Approaches to adaptive filtering , 1970 .

[135]  Dietmar Bauer,et al.  Analysis of the asymptotic properties of the MOESP type of subspace algorithms , 2000, Autom..

[136]  C. V. Rao,et al.  Steady states and constraints in model predictive control , 1999 .

[137]  Jason L. Speyer,et al.  Adaptive noise estimation for homing missiles , 1984 .

[138]  R. H. Luecke,et al.  Estimation of the co variances of the process noise and measurement noise for a linear discrete dynamic system , 1995 .

[139]  Huibert Kwakernaak,et al.  Linear Optimal Control Systems , 1972 .

[140]  Patrick L. Smith.  Estimation of the Covariance Parameters of Non-Stationary Time-Discrete Linear Systems , 1971 .

[141]  D. Magill.  Optimal adaptive estimation of sampled stochastic processes , 1965 .

[142]  B. Tapley,et al.  Adaptive sequential estimation with unknown noise statistics , 1976 .

[143]  P. Bélanger.  Estimation of noise covariance matrices for a linear time-varying stochastic process , 1972, at - Automatisierungstechnik.

[144]  J. S. Hunter,et al.  Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building. , 1979 .

[145]  Dale E. Seborg,et al.  Nonlinear Process Control , 1996 .

[146]  D. Lainiotis,et al.  Recursive algorithm for the calculation of the adaptive Kalman filter weighting coefficients , 1969 .

[147]  M. Bartlett.  On the Theoretical Specification and Sampling Properties of Autocorrelated Time‐Series , 1946 .

[148]  H. Hagar,et al.  SEQUENTIAL FILTER FOR ESTIMATING STATE NOISE COVARIANCES. , 1974 .

[149]  H. Akaike.  A new look at the statistical model identification , 1974 .

[150]  Carlos E. Garcia,et al.  QUADRATIC PROGRAMMING SOLUTION OF DYNAMIC MATRIX CONTROL (QDMC) , 1986 .

[151]  Dietmar Bauer,et al.  Order estimation for subspace methods , 2001, Autom..

[152]  Subbarayan Pasupathy,et al.  Adaptive estimation of noise covariance matrices in real-time preprocessing of geophysical data , 1997, IEEE Trans. Geosci. Remote. Sens..

[153]  Tony T. Lee.  A direct approach to identify the noise covariances of Kalman filtering , 1980 .

[154]  R. G. Jacquot,et al.  Adaptive State Variable Estimation Using Robust Smoothing , 1984 .

[155]  Willi-Hans Steeb,et al.  Kronecker product of matrices and applications , 1991 .

[156]  H. Zeiger,et al.  Approximate linear realizations of given dimension via Ho's algorithm , 1974 .

[157]  Evanghelos Zafiriou,et al.  Robust process control , 1987 .

[158]  P. Belanger,et al.  Identification of optimum filter steady-state gain for systems with unknown noise covariances , 1973 .