Low-Order Modeling from Relay Feedback

Wang, Qingguo; Hang, and Chang-Chieh; Zou, Biao

doi:10.1021/ie960412+

Cited by 105 publications

(75 citation statements)

References 8 publications

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“…Determination of the parameters of an FOTD model from an asymmetric relay experiment has been done in different ways in e.g. Wang et al (1997); Kaya and Atherton (2001); Srinivasan and Chidambaram (2003); Liu and Gao (2008).…”

Section: Asymmetric Relay Feedbackmentioning

confidence: 99%

Towards a New Generation of Relay Autotuners

Berner

Åström

Hägglund

2014

IFAC Proceedings Volumes

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The relay autotuner for PID control is based on the simple idea of investigating process dynamics by the oscillation obtained when the PID controller is replaced by a relay function. In this paper an asymmetric relay function is used, which provides an equation for the static gain of the process. A method to find the normalized time delay is proposed and the benefits of this is discussed. Ways to find low-order models from the experiment are described. Considerations of how to choose the relay parameters are made and some examples are given.

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Section: Asymmetric Relay Feedbackmentioning

confidence: 99%

Towards a New Generation of Relay Autotuners

Berner

Åström

Hägglund

2014

IFAC Proceedings Volumes

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“…Based on a single biased relay test, Wang et al 17 gave a FOPDT model, G m 5 1.00e According to the characteristics of the relay response shape obtained from a single biased relay test as in example 1, the proposed Algorithm-U-A is chosen to derive a SOPDT model, resulting in an underdamped model listed in Table 2. For comparison, the Nyquist plots of these models are shown in Figure 8, which indicates again that obviously improved fitting is captured by the proposed SOPDT model, in particu- lar for the referred low frequency range for controller design.…”

Section: Examplementioning

confidence: 99%

“…Luyben 16 discussed the efficiency of obtaining a FOPDT model from an unbiased relay feedback test. Recent papers 17,18 demonstrated that a FOPDT model can represent the frequency response characteristic of low-or high-order processes with desirable accuracy over the phase range of (2p, 0).In view of that a SOPDT model can give better fitting than a FOPDT model for a real high-order process, and the three types of SOPDT model, overdamped, critically damped, and underdamped, can be effectively used to represent the dynamic response characteristics for a wide variety of chemical processes, a systematic on-line identification method is proposed in this article to obtain such a SOPDT model from a single biased/unbiased relay feedback test. The guidelines for model structure selection are established by classifying the relay response shapes from a large number of relay identification tests on these SOPDT models with different parameter settings.…”

mentioning

confidence: 99%

“…To demonstrate the achievable control performance in terms of the identified models, the standard internal model control structure (IMC) 27 is herein used. Following the IMC theory, 27 the IMC controller based on a perfect knowledge of the process should be designed as Q p 5 (s 1 1)(s 2 1 s 1 1)/ (k P s 1 1) 3 , where k P is an adjustable control parameter, the IMC controller based on the proposed SOPDT model should be Q A 5 (0.9744s 2 1 1.4194s 1 1)/(k A s 1 1) 2 , the IMC , and the IMC controller based on the FOPDT model of Wang et al 17 should be Q W 5 (1.152s11)/(k W s 1 1). By adding a unity step change to the set-point and then a step load disturbance with a magnitude of 0.5 to the process, and taking k p 5 2.0, k A 5 k K 5 2.2, and k W 5 3.0 to obtain the same rising speed of the setpoint response for comparison, the corresponding output responses are shown in Figure 9, respectively.…”

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confidence: 99%

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A systematic approach for on‐line identification of second‐order process model from relay feedback test

2008

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in Wiley InterScience (www.interscience.wiley.com).Low-order process modeling provides a basis for control system design and on-line autotuning in process control. A systematic on-line identification method is proposed in this article to obtain a second-order-plus-dead-time (SOPDT) model from a single biased/unbiased relay feedback test. The relay response shapes of the three types of SOPDT model, overdamped, critically damped, and underdamped, are first examined and categorized for model structure selection before proceeding with the corresponding parameter identification. Exact expressions of the corresponding limit cycles are derived for assessing process response under relay feedback test. By using the measurable parameters of these limit cycles, the corresponding identification algorithms are subsequently derived in a transparent manner. Two denoising methods are given to cope with measurement noises. Illustrative examples are performed to demonstrate the effectiveness and merits of the proposed identification algorithms. 2008 American Institute of Chemical Engineers AIChE J, 54: 1560-1578, 2008 Keywords: identification, relay feedback test, biased, second-order-plus-dead-time (SOPDT) model, limit cycle, denoising IntroductionRelay feedback identification has attracted increasing attention in the process community, since the pioneering work of Å ström and Hägglund 1 which used relay feedback to generate sustained oscillations of the controlled variable for closed-loop identification.Compared with an open-loop identification such as using a step or impulse excitation signal, the process dynamic response is more effectively excited and it can be better observed with sustained oscillation resulted from a relay feedback test. Furthermore, relay feedback test will not cause the process to drift too far away from its operation level. This is important for many practical cases, in particular for highly nonlinear processes with rigorous operating conditions. Based on the concepts of ultimate gain and ultimate frequency obtained from such experiments, the early Correspondence concerning this article should be addressed to F. Gao at kefgao@ust.hk. American Institute of Chemical EngineersAIChE Journal June 2008 Vol. 54, No. 6 1560 relay feedback method, autotune variation (ATV), 2 has become a popular identification tool in process industry. Reviews on the rapid development of relay feedback identification for process regulation have recently been addressed by Atherton 3 and Hang et al. 4 Much research effort has been presently devoted to using a single relay feedback test for process identification. There are in general two kinds of relay feedback structure, unbiased (symmetrical) and biased (asymmetrical). On the basis of a single run of unbiased relay feedback test, Vivek and Chidambaram 5 proposed a first-order-plus-dead-time (FOPDT) modeling according to the Fourier analysis of the process response; Panda 6 reported an identification algorithm for obtaining an underdamped second-order-plus-dead-time (SOPDT) mode...

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“…A system with restricted (low) input resolution and no (average) steadystate offset is bound to cycle (Theorem 1) and the amplitude a of the oscillations is given by the process gain at the frequency of oscillations, e.g. see (10). So far, we have let the system cycle at its "natural" frequency ω L,180 , as given by (11) and (16).…”

Section: How To Mitigate Oscillations Caused By Restricted Input Resomentioning

confidence: 99%

Limit Cycles with Imperfect Valves: Implications for Controllability of Processes with Large Gains

Araújo

Skogestad

2006

Ind. Eng. Chem. Res.

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There is some disagreement in the literature on whether or not large plant gains are a problem when it comes to input-output controllability. In this paper, controllability requirements are derived for two kinds of input errors, namely restriced (low) input resolution (e.g. caused by a sticky valve) and input disturbances. In both cases, the controllability is limited if the plant gain is large at high frequencies. Limited input resolution causes limit cycle behavior (oscillations) similar to that found with relay feedback. The magnitude of the output variations depends on the plant gain at high frequency, but is independent of the controller tuning. Provided frequent input (valve) movements are acceptable, one may reduce the output magnitude by forcing the system to oscillate at a higher frequency, for example by introducing a faster local feedback (e.g. a valve positioner) or by pulse modulating the input signal.

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Low-Order Modeling from Relay Feedback

Cited by 105 publications

References 8 publications

Towards a New Generation of Relay Autotuners

Towards a New Generation of Relay Autotuners

A systematic approach for on‐line identification of second‐order process model from relay feedback test

Limit Cycles with Imperfect Valves: Implications for Controllability of Processes with Large Gains

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