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Papers by R.k.sundar Raman

Research paper thumbnail of Robust System Identification with Noisy Time/Frequency Response Experimental Data: Projection Operator and Linear Algorithms

1993 American Control Conference, 1993

In this paper we consider the problem of robust system identification with noisy time or frequenc... more In this paper we consider the problem of robust system identification with noisy time or frequency response measurement data. It is shown here that any linear identification algorithm which is convergent in the noise free case can be made robustly convergent in the presence of noise by incorporating a simple projection operator into the algorithm. Specific algorithms using this projection operator along with corrupted frequency/time response data are analyzed in detail. The computational simplicity and faster rate of convergence distinguish this approach from other existing robustly convergent non linear identification techniques.

Research paper thumbnail of A Linear Robustly Convergent Interpolatory Algorithm For System Identification

1992 American Control Conference, 1992

This paper presents a linear, robustly convergent interpolatory algorithm for system identificati... more This paper presents a linear, robustly convergent interpolatory algorithm for system identification in the presence of bounded noise. The proposed algorithm converges to the actual, but unknown system in frequency domain in the noise free case and maintains the robust convergence result in the face of bounded noise. This robustness property distinguishes the proposed linear algorithm from other existing linear schemes.

Research paper thumbnail of On line model uncertainty quantification: Hard upper bounds and convergence

Lecture Notes in Control and Information Sciences

This paper considers the problem of on line uncertainty bound quantification in identification of... more This paper considers the problem of on line uncertainty bound quantification in identification of restricted complexity models. Algorithms are presented, which provide hard and tight upper bound on the unknown model uncertainty in H2, H∞ and pointwise sense respectively. The algorithms proposed are very simple, on line and recursive. This allows robust control and adaptive identification to be combined.

Research paper thumbnail of A linear, robust and convergent interpolatory algorithm for quantifying model uncertainties

Systems & Control Letters, 1992

This paper presents a linear, interpolatory, convergent uncertainty estimation scheme which is ro... more This paper presents a linear, interpolatory, convergent uncertainty estimation scheme which is robust in the face of disturbance. The idea is that by simply changing from Lagrange interpolation to Hermite interpolation an effective scheme is derived for estimation of uncertainty. It is ...

Research paper thumbnail of On-line H2, H , and pointwise uncertainty-bound quantification in identification of restricted-complexity models

IMA Journal of Mathematical Control and Information, 1996

The authors consider the problem of online uncertainty bound quantification in identification of ... more The authors consider the problem of online uncertainty bound quantification in identification of restricted complexity models. The algorithms presented provide a hard and tight upper bound on the unknown model uncertainty in H2, H∞, and pointwise senses. The algorithms proposed are simple, online, and recursive. This allows robust control and adaptive identification to be combined

Research paper thumbnail of A linear interpolatory algorithm for robust system identification with corrupted measurement data

IEEE Transactions on Automatic Control, 1993

A linear, robustly convergent interpolatory algorithm for system identification in the presence o... more A linear, robustly convergent interpolatory algorithm for system identification in the presence of bounded noise is presented. The algorithm converges in the actual, but unknown, system in the frequency domain in the noise-free case and maintains the robust convergence result in the face of bounded noise. This robustness property distinguishes the algorithm from existing linear schemes. A key idea of

Research paper thumbnail of Adaptive quantification of model uncertainties by rational approximation

IEEE Transactions on Automatic Control, 1991

A bstl'tJet-Tbls paper presents an adaptive rational approximation approacb for quantifying tbe e... more A bstl'tJet-Tbls paper presents an adaptive rational approximation approacb for quantifying tbe eII ect of uucertainty. It will be sbown in tbe paper tbat a tigbt frequeucy upper bouud on tbe uncertainty is obtainable by adaptive rational approximation. Tbe idea is tbat tbe Ideatlfier consists of two parts: plant identifier and uucertaluty identifier. Plaut Ideatllier gives a nominal model wblch bas lower complexity tban tbat of the true plant. Errors between tbe estimated nominal model and tbe tme plant are cbaracterized by a sequence of rational functions wblch converges to tbe accurate upper bound of tbe uncertainty in frequency domain. Moreover, since approxlmadon and identification are grouped tOlletber, tbe wbole procedure is completely automatic. Tbls allows robust control and adaptive control to be combined.

Research paper thumbnail of Robust system identification with noisy experimental data: Projection operator and linear algorithms

Automatica, 1994

In this paper we consider the problem of robust system identification with noisy time or frequenc... more In this paper we consider the problem of robust system identification with noisy time or frequency response measurement data, It is shown here that any linear identification algorithm which is convergent in the noise free case can be made robustly convergent in the presence of noise by incorporating a simple projection operator into the algorithm. The computational simplicity and faster rate of convergence distinguish this approach from other existing robustly convergent nonlinear identification techniques.

Research paper thumbnail of Robust System Identification with Noisy Time/Frequency Response Experimental Data: Projection Operator and Linear Algorithms

1993 American Control Conference, 1993

In this paper we consider the problem of robust system identification with noisy time or frequenc... more In this paper we consider the problem of robust system identification with noisy time or frequency response measurement data. It is shown here that any linear identification algorithm which is convergent in the noise free case can be made robustly convergent in the presence of noise by incorporating a simple projection operator into the algorithm. Specific algorithms using this projection operator along with corrupted frequency/time response data are analyzed in detail. The computational simplicity and faster rate of convergence distinguish this approach from other existing robustly convergent non linear identification techniques.

Research paper thumbnail of A Linear Robustly Convergent Interpolatory Algorithm For System Identification

1992 American Control Conference, 1992

This paper presents a linear, robustly convergent interpolatory algorithm for system identificati... more This paper presents a linear, robustly convergent interpolatory algorithm for system identification in the presence of bounded noise. The proposed algorithm converges to the actual, but unknown system in frequency domain in the noise free case and maintains the robust convergence result in the face of bounded noise. This robustness property distinguishes the proposed linear algorithm from other existing linear schemes.

Research paper thumbnail of On line model uncertainty quantification: Hard upper bounds and convergence

Lecture Notes in Control and Information Sciences

This paper considers the problem of on line uncertainty bound quantification in identification of... more This paper considers the problem of on line uncertainty bound quantification in identification of restricted complexity models. Algorithms are presented, which provide hard and tight upper bound on the unknown model uncertainty in H2, H∞ and pointwise sense respectively. The algorithms proposed are very simple, on line and recursive. This allows robust control and adaptive identification to be combined.

Research paper thumbnail of A linear, robust and convergent interpolatory algorithm for quantifying model uncertainties

Systems & Control Letters, 1992

This paper presents a linear, interpolatory, convergent uncertainty estimation scheme which is ro... more This paper presents a linear, interpolatory, convergent uncertainty estimation scheme which is robust in the face of disturbance. The idea is that by simply changing from Lagrange interpolation to Hermite interpolation an effective scheme is derived for estimation of uncertainty. It is ...

Research paper thumbnail of On-line H2, H , and pointwise uncertainty-bound quantification in identification of restricted-complexity models

IMA Journal of Mathematical Control and Information, 1996

The authors consider the problem of online uncertainty bound quantification in identification of ... more The authors consider the problem of online uncertainty bound quantification in identification of restricted complexity models. The algorithms presented provide a hard and tight upper bound on the unknown model uncertainty in H2, H∞, and pointwise senses. The algorithms proposed are simple, online, and recursive. This allows robust control and adaptive identification to be combined

Research paper thumbnail of A linear interpolatory algorithm for robust system identification with corrupted measurement data

IEEE Transactions on Automatic Control, 1993

A linear, robustly convergent interpolatory algorithm for system identification in the presence o... more A linear, robustly convergent interpolatory algorithm for system identification in the presence of bounded noise is presented. The algorithm converges in the actual, but unknown, system in the frequency domain in the noise-free case and maintains the robust convergence result in the face of bounded noise. This robustness property distinguishes the algorithm from existing linear schemes. A key idea of

Research paper thumbnail of Adaptive quantification of model uncertainties by rational approximation

IEEE Transactions on Automatic Control, 1991

A bstl'tJet-Tbls paper presents an adaptive rational approximation approacb for quantifying tbe e... more A bstl'tJet-Tbls paper presents an adaptive rational approximation approacb for quantifying tbe eII ect of uucertainty. It will be sbown in tbe paper tbat a tigbt frequeucy upper bouud on tbe uncertainty is obtainable by adaptive rational approximation. Tbe idea is tbat tbe Ideatlfier consists of two parts: plant identifier and uucertaluty identifier. Plaut Ideatllier gives a nominal model wblch bas lower complexity tban tbat of the true plant. Errors between tbe estimated nominal model and tbe tme plant are cbaracterized by a sequence of rational functions wblch converges to tbe accurate upper bound of tbe uncertainty in frequency domain. Moreover, since approxlmadon and identification are grouped tOlletber, tbe wbole procedure is completely automatic. Tbls allows robust control and adaptive control to be combined.

Research paper thumbnail of Robust system identification with noisy experimental data: Projection operator and linear algorithms

Automatica, 1994

In this paper we consider the problem of robust system identification with noisy time or frequenc... more In this paper we consider the problem of robust system identification with noisy time or frequency response measurement data, It is shown here that any linear identification algorithm which is convergent in the noise free case can be made robustly convergent in the presence of noise by incorporating a simple projection operator into the algorithm. The computational simplicity and faster rate of convergence distinguish this approach from other existing robustly convergent nonlinear identification techniques.