Identification of continuous-time systems

Rao, Ganti Prasada; Unbehauen, H.

doi:10.1049/ip-cta:20045250

Cited by 228 publications

(120 citation statements)

References 89 publications

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“…Identifying continuous time [Rao and Unbehauen, 2006] models requires the computation of signal derivatives while identifying discrete time models does not.…”

Section: Parameter Identificationmentioning

confidence: 99%

Modeling, Identification, and Control of an Unmanned Surface Vehicle

Sonnenburg

Woolsey

2013

Journal of Field Robotics

147

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This dissertation addresses the modeling, identification, and control of an automated planing vessel. To provide motion models for trajectory generation and to enable model-based control design for trajectory tracking, several experimentally identified models are compared over a wide range of speed and planing conditions for the Virginia Tech Ribcraft Unmanned Surface Vehicle. The modeling and identification objective is to determine a model which is sufficiently rich to enable effective model-based control design and trajectory optimization, sufficiently simple to allow parameter identification, and sufficiently general to describe a variety of hull forms and actuator configurations. Beginning with a 6 degree of freedom nonlinear dynamic model, several linear steering and speed models are obtained as well as a thruster model.The Ribcraft USV tracks trajectories generated with the selected maneuvering models by using a backstepping trajectory controller. A PD cascade trajectory control law is also developed and the performance of the two controllers is compared using aggressive trajectories. The backstepping control law compares favorably to the PD cascade controller. The backstepping control law is then further modified to account for nonlinear sternward dynamics and for a constant or slowly varying fluid flow. ACKNOWLEDGMENTSThis dissertation is the product of the culmination of years of research and field experimentation that required the collective abilities only found in a group of skilled researchers. There are many people that guided me through the higher level decision-making and advising process as well as the more laborious experimentation and implementation efforts. Without these people I would not have been able to finish a dissertation even approaching this quality.

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“…Identifying continuous time [Rao and Unbehauen, 2006] models requires the computation of signal derivatives while identifying discrete time models does not.…”

Section: Parameter Identificationmentioning

confidence: 99%

Modeling, Identification, and Control of an Unmanned Surface Vehicle

Sonnenburg

Woolsey

2013

Journal of Field Robotics

147

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“…The idea in [21,26] is to set up an iteration between a linear least squares optimization and a forward solution, which is partly based on a Picard iteration [34]. This approach is therefore distinctively different from other integral formulations like the modulating function approach [35] which does not iterate and is therefore not directly suitable for the discrete data and high non-linearities present in this application. Furthermore, the CVS model of Fig.…”

Section: Unique Parameter Identificationmentioning

confidence: 99%

Unique parameter identification for cardiac diagnosis in critical care using minimal data sets

Hann

Chase

Desaive

et al. 2010

Computer Methods and Programs in Biomedicine

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Lumped parameter approaches for modelling the cardiovascular system typically have many parameters of which a significant percentage are often not identifiable from limited data sets. Hence, significant parts of the model are required to be simulated with little overall effect on the accuracy of data fitting, as well as dramatically increasing the complexity of parameter identification. This separates sub-structures of more complex cardiovascular system models to create uniquely identifiable simplified models that are one to one with the measurements. In addition, a new concept of parameter identification is presented where the changes in the parameters are treated as an actuation force into a feed back control system, and the reference output is taken to be steady state values of measured volume and pressure. The major advantage of the method is that when it converges, it must be at the global minimum so that the solution that best fits the data is always found.By utilizing continuous information from the arterial/pulmonary pressure waveforms and the end-diastolic time, it is shown that potentially, the ventricle volume is not required in the data set, which was a requirement in earlier published work. The simplified models can also act as a bridge to identifying more sophisticated cardiac models, by providing an initial set of patient specific parameters that can reveal trends and interactions in the data over time. The goal is to apply the simplified models to retrospective data on groups of patients to help characterize population trends or un-modelled dynamics within known bounds. These trends can assist in improved prediction of patient responses to cardiac disturbance and therapy intervention with potentially smaller and less invasive data sets. In this way a more complex model that takes into account individual patient variation can be developed, and applied to the improvement of cardiovascular management in critical care. Keywords :Model-based cardiac diagnosis ; Cardiovascular system ; Integral-based parameter identification ; Pressure waveform ; ECG ; Intensive care unit IntroductionIn critical care, cardiovascular dysfunction can be easily misdiagnosed due to incomplete information and the complexities involved, leading to premature discharge or non-optimal treatment [1][2][3]. It is also a major cause of increased length of stay and death [4,5]. Demand for critical care is growing dramatically severely affecting healthcare delivery [6][7][8]. The overall goal of this research is to use computational cardiac models to better aggregate available clinical data in an intensive care unit (ICU) into a more readily understood physiological context for clinicians. The computational models can be used to reveal non-linear dynamics and interactions that are not readily apparent in the data.A major difficulty faced with cardiovascular modelling in general, is the level of detail these models typically include. For example multi-scale modelling approaches utilizing finite elements have successfully expl...

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“…Moreover, the pole-placed method with spectral factorization satisfies basic control requirements such as disturbance attenuation, stability and reference signal tracking. Although the polynomial synthesis is considered for the continuous-time ELM, the recursive identification with exponential forgetting (Rao and Unbehauen 2005) runs in discrete time which is better from computation and programming point of view. This disagreement could be overcome with the use so called Delta (δ-) models (Middleton and Goodwin 2004) that belong the discrete-time models but parameters approaches to the continuous ones for the small sampling period (Stericker and Sinha 1993).…”

Section: Introductionmentioning

confidence: 99%

Two Types Of External Linear Models Used For Adaptive Control Of Continuous Stirred Tank Reactor

Vojtěšek

Dostál²

2011

ECMS 2011 Proceedings Edited By: T. Burczynski, J. Kolodziej, A. Byrski, M. Carvalho

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The main goal of this contribution is to show simulation results for two types of External Linear Models (ELM) used in the adaptive control as a linear representation of the originally nonlinear system. The nonlinearity is dispathed with the use of recursive identification which recomputes parameters of ELM in each step according to actual state of the system. The controller design employes polynomial approach with pole-placement method and spectral factorization and all these techniques together satisfies basic control requirements. The verification was done on the mathematical model of Continuous Stirred Tank Reactor (CSTR) as a typical member of the nonlinear equipment widely used in chemical industry.

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Identification of continuous-time systems

Cited by 228 publications

References 89 publications

Modeling, Identification, and Control of an Unmanned Surface Vehicle

Modeling, Identification, and Control of an Unmanned Surface Vehicle

Unique parameter identification for cardiac diagnosis in critical care using minimal data sets

Two Types Of External Linear Models Used For Adaptive Control Of Continuous Stirred Tank Reactor

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