On-line relay identification, assessment and tuning of PID controller

Tan, Kok Kiong; Lee, T.H.; Jiang, Xi

doi:10.1016/s0959-1524(00)00012-3

Cited by 32 publications

(14 citation statements)

References 13 publications

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“…The reason for this phenomenon can be found in analog control, where pneumatic controllers were dominant. When pneumatic controllers were dominant, the PID algorithm was difficult to design due to its use of extremely expensive analog amplifiers [7]. Despite this drawback, Astrom and Hagglund [2] indicated that the ISA allows complex zeroes and is thus a more flexible structure than the series algorithm, which has real zeroes.…”

Section: Introductionmentioning

confidence: 99%

“…A variety of structure identification methods are under development. A popular and frequently used method is the relay feedback for both the off-line and on-line automatic identification of the PID [7]; however, relay feedback has disadvantages because it is unacceptable for a few classes of processes, such as unstable and integrals of the second-order processes. A different approach to identify PI structures was shown in [11], where artificial intelligence was used and PI algorithms were treated as a black box [12].…”

Section: Introductionmentioning

confidence: 99%

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Black box modeling of PIDs implemented in PLCs without structural information: a support vector regression approach

Sałat

Awtoniuk

2014

Neural Comput & Applic

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In this report, the parameters identification of a proportional–integral–derivative (PID) algorithm implemented in a programmable logic controller (PLC) using support vector regression (SVR) is presented. This report focuses on a black box model of the PID with additional functions and modifications provided by the manufacturers and without information on the exact structure. The process of feature selection and its impact on the training and testing abilities are emphasized. The method was tested on a real PLC (Siemens and General Electric) with the implemented PID. The results show that the SVR maps the function of the PID algorithms and the modifications introduced by the manufacturer of the PLC with high accuracy. With this approach, the simulation results can be directly used to tune the PID algorithms in the PLC. The method is sufficiently universal in that it can be applied to any PI or PID algorithm implemented in the PLC with additional functions and modifications that were previously considered to be trade secrets. This method can also be an alternative for engineers who need to tune the PID and do not have any such information on the structure and cannot use the default settings for the known structures.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Black box modeling of PIDs implemented in PLCs without structural information: a support vector regression approach

Sałat

Awtoniuk

2014

Neural Comput & Applic

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“…The approach adopted is similar to Panigrahi & Mujumdar (2000) and Cancelliere et al (2002), but with different modelling and operational aspects. Indeed the AFDCS is based on a full description of the reservoir model, based on a hybrid model (Gollu & Varaiya 1989) and non-linear Proportional-Integral (PI) strategies (Ho et al 1998;Tan et al 2001) for reservoir gate regulation, which first allows the decision strategy to be defined and then to control water releases. Further novel aspects with respect to the above works are the use of different fuzzy input variables and a Monte Carlo technique to tune the parameters of fuzzy rules systems.…”

Section: Introductionmentioning

confidence: 99%

Automated fuzzy decision and control system for reservoir management

Cavallo¹,

Nardo²,

Maria³

et al. 2013

Journal of Water Supply: Research and Technology-Aqua

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Traditional approaches to the management of an artificial reservoir involve the use of linear, dynamic, nonlinear or stochastic programming. Hence a purely model-based approach would be extremely difficult and thus, recently, a large number of papers devoted to the solution of reservoir management problems based on fuzzy logic approaches have appeared. In this work, two management problems of a reservoir are addressed with fuzzy logic: the definition of the water flow to supply to the user, a typical decision problem; and the regulation of the dam gate, which is a typical control problem. Both problems have been integrated in an Automated Fuzzy Decision and Control System (AFDCS) that is able to identify the ordinary and drought operating conditions. Fuzzy rules are developed from a database derived from the traditional experience of operators and they have been optimized with a genetic algorithm. Two cost functionals are used, able to weight user's desiderata (water demand) with water waste (water spills and evaporation). Three strategies are developed for a case study and are validated in different scenarios, using Monte Carlo simulations and worst case situations. Results show a good performance of AFDCS to alleviate the consequences of drought and to control of the dam gate.

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“…Such tuning rules, to compensate delayed processes by either minimising a performance criterion, or achieving a specified gain and/or phase margin, are discussed when the SISO process is modelled in IPD form (Kookos et al 1999), or stable or unstable SOSPD form ( Luyben 2000). Alternatively, ultimate cycle tuning rules, and modifications of the rules in which the proportional gain is set up to give a closed loop transient response decay ratio of 0.25, or a phase lag of 0 135 , may compensate general, possibly delayed, stable or unstable processes (Hay 1998;Tan et al 1999;Yu 1999;Prashanti and Chidambaram 2000;Tan et al 2001;Robbins, 2002a), sometimes to achieve either a specified gain and/or phase margin ( Prashanti and Chidambaram 2000;Tan et al 2001) or a specified closed loop response (Vrancic et al 1999(Vrancic et al , 2001). The controller settings are easily calculated; however, the system must generally be destabilised under proportional control, the empirical nature of the method means that uniform performance is not achieved in general, several trials must typically be made to determine the ultimate gain, the resulting process upsets may be detrimental to product quality, there is a danger of misinterpreting a limit cycle as representing the stability limit and the amplitude of the process variable signal may be so great that the experiment may not be carried out for cost or safety considerations.…”

Section: Tuning Rulesmentioning

confidence: 99%

PID compensation of time delayed processes 1998-2002: a survey

O’Dwyer¹

Proceedings of the 2003 American Control Conference, 2003.

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A time delay may be defined as the time interval between the start of an event at one point in a system and its resulting action at another point in the system. Delays are also known as transport lags or dead times; they arise in physical, chemical, biological and economic systems, as well as in the process of measurement and computation. Methods for the compensation of time delayed processes may be broadly divided into proportional integral derivative (PID) based controllers, in which the controller parameters are adapted to the controller structure, and structurally optimised controllers, in which the controller structure and parameters are adapted optimally to the structure and parameters of the process model. The purpose of this paper is to extract the essence of the developments in design, tuning and implementation of PID controllers for delayed processes over the five years 1998-2002, concentrating on journal publications. The paper will provide a framework against which the literature may be viewed.

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On-line relay identification, assessment and tuning of PID controller

Cited by 32 publications

References 13 publications

Black box modeling of PIDs implemented in PLCs without structural information: a support vector regression approach

Black box modeling of PIDs implemented in PLCs without structural information: a support vector regression approach

Automated fuzzy decision and control system for reservoir management

PID compensation of time delayed processes 1998-2002: a survey

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