A tutorial review on process identification from step or relay feedback test

Liu, Tao; Wang, Qingguo; Huang, Hsiao‐Ping

doi:10.1016/j.jprocont.2013.08.003

Cited by 193 publications

(88 citation statements)

References 104 publications

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“…Model-based PI and PID control strategies have been increasingly explored in recent years [21]. The main reason for this is their superior performance in set-point tracking and disturbance rejection.…”

Section: Discussionmentioning

confidence: 99%

A comparison of software-based approaches to identifying FOPDT and SOPDT model parameters from process step response data

Cox

Tindle

Burn

2016

Applied Mathematical Modelling

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a b s t r a c tSystem identification is the experimental approach to deriving process models, which can take many forms depending upon their intended use. In the work described in this paper, the ultimate aim is to use them in the design of controllers for regulating engineering processes. Modelling always involves approximations since all real systems are to some extent non-linear, time-varying, and distributed. Thus, it is highly improbable that any set of models will contain the 'true' system structure. A more realistic aim is therefore to identify a model that provides an acceptable approximation, in the context of the application in which it is used. In controller design, a first step is often to determine the model using step and frequency response data. This paper compares different modern software approaches that exploit step response data, where the aim is to determine either a first-or second-order-plus-dead-time (FOPDT or SOPDT) transfer function. They include an integral equation method, an algorithm available in the MATLAB Optimization Toolbox, and recently developed in-house software that uses a particle swarm optimisation (PSO) approach.

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Section: Discussionmentioning

confidence: 99%

A comparison of software-based approaches to identifying FOPDT and SOPDT model parameters from process step response data

Cox

Tindle

Burn

2016

Applied Mathematical Modelling

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“…Among second CVs group (denoted as ZONE-CV) there are the six zone temperatures, the temperature difference between zone 1 and zone 2, the smokeexchanger temperature, the total air flow rate and the fuel and air valves opening (per cent). Linear dynamic models between the variables of the second group and MVs/DVs have been derived through an identification procedure based on a black box approach (Ljung [11], Liu et al [12]). Discrete time first order strictly proper linear models without delays have been obtained.…”

Section: Furnace Model Identificationmentioning

confidence: 99%

Energy saving and environmental impact decreasing in a walking beam reheating furnace

Pepe¹,

Zanoli²,

Cocchioni³

2016

WIT Transactions on Ecology and the Environment

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In this work, an Advanced Process Control system aimed at the control and the optimization of a walking beam billets reheating furnace has been designed. In particular, a two-layer linear Model Predictive Control strategy has been adopted. A billets reheating first principles model has been formulated, in order to obtain accurate relationships between furnace zones temperature and billets temperature. To model the effect of the fuel flow rates on the furnace zone temperature, a blackbox approach has been adopted. The cooperation between the control architecture and several supervision modules assures a flexible and resilient control, capable to cope with the most critical situations. Simulation tests and real plant results have demonstrated the soundness and the reliability of the proposed approach. With respect to the previous control system, manually operated by plant operators, the designed Advanced Process Control system allows the process to be maintained closer to its variables operating limits. In this way, energy saving and environmental impact decreasing have been obtained.

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“…[5], [6]. A review of the current state of process modeling from relay experiments can be found in [7]. However, these improvements do not seem to have made their way out to the industrial products.…”

Section: Introductionmentioning

confidence: 99%

An experimental comparison of PID autotuners

Berner

Soltesz

Hägglund

et al. 2018

Control Engineering Practice

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Abstract-In this paper two novel autotuners are compared with two industrially available ones. The aim is to see if the research frontline can improve the industry standard of today. Experiments are made on three laboratory processes with different characteristics. Two lag-dominated processes of which one is a level control problem with fast dynamics, and one a temperature control problem with slow dynamics, as well as one delay-dominated level control process. Both the experiments and the obtained controller performances are evaluated and discussed. The results show that the performance of the stateof-the-art industrial autotuners can be significantly improved.

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A tutorial review on process identification from step or relay feedback test

Cited by 193 publications

References 104 publications

A comparison of software-based approaches to identifying FOPDT and SOPDT model parameters from process step response data

A comparison of software-based approaches to identifying FOPDT and SOPDT model parameters from process step response data

Energy saving and environmental impact decreasing in a walking beam reheating furnace

An experimental comparison of PID autotuners

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