Nonlinear design of adaptive controllers for linear systems

Krstić, Miroslav; Kanellakopoulos, I.; Kokotović, P.V.

doi:10.1109/9.286250

Cited by 383 publications

(427 citation statements)

References 8 publications

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“…В настоящее время существует достаточное количество модификаций этого мето-да [2][3][4][5][6][7][8][9][10][11][12][13]. Так в [1, 4] рассмотрен адаптивный и робастный регулятор для управления линейными система-ми по выходу.…”

Section: Introductionunclassified

“…Данные подходы основаны на особой параметризации уравнения объекта, позволяющей по-лучить производную его выходной переменной. В робастном алгоритме в [1], в отличие от [5][6][7][8], не исполь-зовалась идентификация неизвестных параметров объекта.…”

Section: Introductionunclassified

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Backstepping algorithm for linear SISO plants under structural uncertainties

Borisivich¹,

Artem²

2016

Naučno-teh. vestn. inf. tehnol. meh. opt.

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Section: Introductionunclassified

Backstepping algorithm for linear SISO plants under structural uncertainties

Borisivich¹,

Artem²

2016

Naučno-teh. vestn. inf. tehnol. meh. opt.

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“…Inspired by [13,23], the extraction of a previous state given the current state can be done by simultaneously solving a set of nonlinear equations, provided that the nonlinear dynamics (2) satisfy certain properties.…”

Section: Reducing Incompleteness Of Observation Sequencementioning

confidence: 99%

HMM-based characterization of channel behavior for networked control systems

Chang

Venkatasubramanian

Enyioha

et al. 2012

Proceedings of the 1st International Conference on High Confidence Networked Systems

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We study the problem of characterizing the behavior of lossy and data corrupting communication channels in a networked control setting, where the channel's behavior exhibits temporal correlation. We propose a behavior characterization mechanism based on a hidden Markov model (HMM). The use of a HMM in this regard presents multiple challenges including dealing with incomplete observation sequences (due to data losses and corruptions) and the lack of a priori information about the model complexity (number of states in the model). We address the first challenges by using the plant state information and history of received/ applied control inputs to fill in the gaps in the observation sequences, and by enhancing the HMM learning algorithm to deal with missing observations . Further, we adopt two model quality criteria for determining behavior model complexity. The contributions of this paper include: (1) an enhanced learning algorithm for refining the HMM model parameters to handle missing observations, and (2) simultaneous use of two welldefined model quality criteria to determine the model complexity. Simulation results demonstrate over 90\% accuracy in predicting the output of a channel at a given time step, when compared to a traditional HMM based model that requires complete knowledge of the model complexity and observation sequence. ABSTRACTWe study the problem of characterizing the behavior of lossy and data corrupting communication channels in a networked control setting, where the channel's behavior exhibits temporal correlation. We propose a behavior characterization mechanism based on a hidden Markov model (HMM). The use of a HMM in this regard presents multiple challenges including dealing with incomplete observation sequences (due to data losses and corruptions) and the lack of a priori information about the model complexity (number of states in the model). We address the first challenges by using the plant state information and history of received/applied control inputs to fill in the gaps in the observation sequences, and by enhancing the HMM learning algorithm to deal with missing observations . Further, we adopt two model quality criteria for determining behavior model complexity. The contributions of this paper include: (1) an enhanced learning algorithm for refining the HMM model parameters to handle missing observations, and (2) simultaneous use of two well-defined model quality criteria to determine the model complexity. Simulation results demonstrate over 90% accuracy in predicting the output of a channel at a given time step, when compared to a traditional HMM based model that requires complete knowledge of the model complexity and observation sequence.

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“…The set of systems which can be controlled by these controllers includes linear time-invariant (LTI) systems. Moreover, for LTI systems, these controllers bear strong parametric robustness in the sense that global stability can be achieved by choosing appropriate design parameters without the precise knowledge of the values of the plant parameters [6], [7], [17]. The corresponding adaptive controllers which deal with unknown but constant parameters [9], [7] can achieve arbitrarily improved transient performance [7], [12].…”

mentioning

confidence: 99%

“…We first consider the use of the backstepping controllers proposed in [6], [17] based on TI models to control LTV systems with known parameters by treating the time varying parameters as constant at each time . We demonstrate that the quadratic Lyapunov function-based analysis used in [6], [17] to show stability and asymptotic tracking for LTI systems does not work for LTV systems in general, even when the plant parameters are known exactly at each time .…”

mentioning

confidence: 99%