Robust identification of first-order plus dead-time model from step response

Bi, Qiang; Cai, Wenjian; Lee, Eng-Lock; Wang, Qingguo; Hang, Chang-Chieh; Zhang, Yong

doi:10.1016/s0967-0661(98)00166-x

Cited by 142 publications

(79 citation statements)

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“…The model of the discharge flow, (wherev 1 and q 1 are input variable and output variable, respectively) and the model of the steam flow, (wherev 2 and q 2 are input variable and output variable, respectively) can be constructed using the process data. The dynamics of the above two valves can be described by the first-order plus dead-time models form as follows [16], [17]:…”

Section: B Dynamic Modeling Of the Control Valvementioning

confidence: 99%

Adaptive Decoupling Switching Control of the Forced-Circulation Evaporation System Using Neural Networks

Wang

Chai

et al. 2013

IEEE Trans. Contr. Syst. Technol.

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Abstract-For the forced-circulation evaporation process of alumina production, the control objectives include maintaining the liquid level and fast tracking of the product density with respect to its setpoint. However, with the strong coupling between the level control and product density control loops and the process exhibits strong nonlinearities, conventional control strategies, such as proportional-integral-derivative and other linear control design, cannot achieve satisfactory control performance and meet production demands. By augmenting the forced-circulation evaporation system model with dynamics of its operating valves, a nonlinear adaptive decoupling switching control strategy is proposed. This control strategy includes a linear adaptive decoupling controller, a neural-network-based nonlinear adaptive decoupling controller, and a switching mechanism. The linear adaptive decoupling controller is used to reduce the coupling between the two control loops. The neural-network-based nonlinear adaptive decoupling controller is employed to improve the transient performance and reduce the effects of the nonlinearities on the system, and the switching mechanism is introduced to guarantee the input-output stability of the closed-loop system. Simulation results show that the proposed method can decouple the loops effectively for the forced-circulation evaporation system and thus improve the evaporation efficiency.

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Section: B Dynamic Modeling Of the Control Valvementioning

confidence: 99%

Adaptive Decoupling Switching Control of the Forced-Circulation Evaporation System Using Neural Networks

Wang

Chai

et al. 2013

IEEE Trans. Contr. Syst. Technol.

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“…It is made by measuring the system step response and by using a least square type algorithm to approximate the process with a first-order plus dead-time model (see (Bi et al 1999)). For steps having amplitude of (1, 2 and 3), the identified continuous-time pole is (1.99×10 −3 , 2.33×10 −3 and 2.5×10 −3 ), which justifies the choice of 0.99 for the Laguerre basis pole.…”

Section: Multiple Models System Identificationmentioning

confidence: 99%

Multiple Model Identification and Control of Neonate Incubators Using Laguerre Basis

Oliveira

Amorim

Latawiec³

2005

IFAC Proceedings Volumes

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This work is focused on temperature and humidity control problem of closed newborn incubators. Such incubator promotes a controlled micro-climate, with small heat transfer between the premature and the environment, leading to a healthful environment. In this context, a laboratory pilot plant (full scale) was built to evaluate control algorithms and this plant is presented here. Furthermore, some identification results based on the use of orthonormal basis functions are discussed and a control scheme is also proposed. This control law is based on multiple local models and predictive control ideas. Closed-loop control examples validates the proposed method.

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“…Identification of FOPDT or SOPDT models from process empirical data helps in representing complex processes in minimum parameters and simple model structure but holding the essential process dynamics without the need of knowing the complex physics involved in the process. The simplest and easiest method for FOPDT or SOPDT model parameter estimation from empirical data is by the open-loop step test of the process [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Development of FOPDT and SOPDT model from arbitrary process identification data using the properties of orthonormal basis function

Seban¹,

Boruah²,

Roy³

2018

IJET

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Most of industrial process can be approximately represented as first-order plus delay time (FOPDT) model or second-order plus delay time (FOPDT) model. From a control point of view, it is important to estimate the FOPDT or SOPDT model parameters from arbitrary process input as groomed test like step test is not always feasible. Orthonormal basis function (OBF) are class of model structure having many advantages, and its parameters can be estimated from arbitrary input data. The OBF model filters are functions of poles and hence accuracy of the model depends on the accuracy of the poles. In this paper, a simple and standard particle swarm optimisation technique is first employed to estimate the dominant discrete poles from arbitrary input and corresponding process output. Time constant of first order system or period of oscillation and damping ratio of second order system is calculated from the dominant poles. From the step response of the developed OBF model, time delay and steady state gain are estimated. The parameter accuracy is improved by employing an iterative scheme. Numerical examples are provided to show the accuracy of the proposed method.

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Robust identification of first-order plus dead-time model from step response

Cited by 142 publications

References 0 publications

Adaptive Decoupling Switching Control of the Forced-Circulation Evaporation System Using Neural Networks

Adaptive Decoupling Switching Control of the Forced-Circulation Evaporation System Using Neural Networks

Multiple Model Identification and Control of Neonate Incubators Using Laguerre Basis

Development of FOPDT and SOPDT model from arbitrary process identification data using the properties of orthonormal basis function

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