Direct continuous-time approaches to system identification. Overview and benefits for practical applications

Garnier, Hugues

doi:10.1016/j.ejcon.2015.04.003

Cited by 90 publications

(44 citation statements)

References 56 publications

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“…However, continuous-time models are more intuitive. Furthermore, as pointed out by Garnier (2015), direct identification of continuous-time models based on sampled data can outperform the discrete-time models in case of rapidly or irregularly sampled data. Algorithms to CONTACT Stefan Kersting stefan.kersting@tum.de identify PWA systems in state-space form are less common, due to increased complexity in case the state is not accessible.…”

Section: Introductionmentioning

confidence: 99%

Recursive estimation in piecewise affine systems using parameter identifiers and concurrent learning

Kersting

Buss

2017

International Journal of Control

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Piecewise affine systems constitute a popular framework for the approximation of non-linear systems and the modelling of hybrid systems. This paper addresses the recursive subsystem estimation in continuoustime piecewise affine systems. Parameter identifiers are extended from continuous-time state-space models to piecewise linear and piecewise affine systems. The convergence rate of the presented identifiers is improved further using concurrent learning, which makes concurrent use of current and recorded measurements. In concurrent learning, assumptions on persistence of excitation are replaced by the less restrictive linear independence of the recorded data. The introduction of memory, however, reduces the tracking ability of concurrent learning because errors in the recorded measurements prevent convergence to the true parameters. In order to overcome this limitation, an algorithm is proposed to detect and remove erroneous measurements at run-time and thereby restore the tracking ability. Detailed examples are included to validate the proposed methods numerically.

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Section: Introductionmentioning

confidence: 99%

Recursive estimation in piecewise affine systems using parameter identifiers and concurrent learning

Kersting

Buss

2017

International Journal of Control

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“…Moreover, to the best of our knowledge, none of these techniques attempt direct experimental identification of the state space model. While there are different methodologies, the common practice seems to first identify the transfer function [13] of the system then construct the state space model using realizations [18].…”

Section: Introductionmentioning

confidence: 99%

Experimental Determination of an Extended DC Servo-Motor State Space Model: An Undergraduate Experiment

2020

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State space systems and experimental system identification are essential components of control education. Early introduction of these tools to the curriculum of a control laboratory in an interconnected and accessible manner that does not over-dilute the concepts is important in two ways. First, it facilitates a student's transition to more senior and graduate level control concepts. It also provides an effective link to industrial applications. This paper provides two novel experimental procedures for directly identifying the state space model of a DC motor in an undergraduate control laboratory. The procedures do not require any specialized hardware and can be performed using standard laboratory equipment. They also do not place any simplifying assumptions on the motor's model. The first procedure is based on direct pseudo inversion of the state space model. It does not require advanced knowledge of the state space approach or signal filtering. It is easy to understand and suits a first control laboratory. The second procedure is more efficient. It is based on the Markov approach commonly used for realizing, indirectly, a state space model from an estimated transfer function. While the procedure is designed for use by undergraduate students, it requires relatively advanced knowledge in state space that makes it suitable for a second undergraduate control laboratory.

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“…For several decades, DT identification has been dominant due to the strong development of the digital computer. More recently, estimation using continuous-time identification methods has received much attention due to advantages such as providing insights to the physical system and being independent of the sampling time [15] [17] [25]. For example, with irregular sampling time, the DT model becomes time-varying; hence the DT system identification problem becomes more difficult while for CT identification, the system is still timeinvariant.…”

Section: Introductionmentioning

confidence: 99%

Useful redundancy in parameter and time delay estimation for continuous-time models

Welsh

Alamir

2018

Automatica

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This paper proposes an algorithm to estimate the parameters, including time delay, of continuous time systems based on instrumental variable identification methods. To overcome the multiple local minima of the cost function associated with the estimation of a time delay system, we utilise the useful redundancy technique. Specifically, the cost function is filtered through a set of low-pass filters to improve convexity with the useful redundancy technique exploited to achieve convergence to the global minimum of the optimization problem. Numerical examples are presented to demonstrate the effectiveness of the proposed algorithm.

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Direct continuous-time approaches to system identification. Overview and benefits for practical applications

Cited by 90 publications

References 56 publications

Recursive estimation in piecewise affine systems using parameter identifiers and concurrent learning

Recursive estimation in piecewise affine systems using parameter identifiers and concurrent learning

Experimental Determination of an Extended DC Servo-Motor State Space Model: An Undergraduate Experiment

Useful redundancy in parameter and time delay estimation for continuous-time models

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