Handbook of PI and PID Controller Tuning Rules

O’Dwyer, Aidan

doi:10.1142/p424

Cited by 229 publications

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“…For second-order processes with no zeros or several well-known PID tuning strategies when applied to the control of FOPDT systems. (A comprehensive survey of modelbased PI and PID tuning rules has been published by O'Dwyer (2003).) Methods proposed by Rivera et al (1986), Lee et al (1998), Chen and Seborg (2002) and Skogestad (2003) were considered because they reduce the complexity of the PID tuning problem to specifi cation of a single adjustable parameter.…”

mentioning

confidence: 99%

A Comparison of PID Controller Tuning Methods

Foley

Julien²,

Copeland

2005

Can J Chem Eng

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It is a remarkable fact that the vast majority of chemical processes are well-controlled with the PID algorithm or one of its simpler forms. A widely-accepted rule-of-thumb is that use of the derivative mode should be confi ned to applications characterized by long time delays and/or higher-order dynamic lags, and then only provided that the feedback signal is virtually free of measurement noise. According to Luyben and Luyben (1997), approximately 80% of industrial controllers are PI or P-only, with PID accounting for the remaining 20%. Other studies (e.g. Bialkowski, 1992) report a much lower PID implementation rate.As one would expect, introduction of a third degree-offreedom into the design problem enables the engineer to obtain tighter control. This is because the phase lead contributed by the derivative term reduces the impact of deadtime and other sources of phase lag which limit the achievable performance of the feedback loop. From a state space point of view, the derivative of the process variable (PV) can be regarded as an estimate of a system state. For second-order processes with no zeros or deadtime, knowledge of the PV and its derivative permits one to assign the closed-loop poles to arbitrary locations (Kailath, 1980). Perhaps less well-recognized is the fact that derivative action normally increases the robustness of a control system to high-frequency modelling errors. Unfortunately, these improvements are obtained at the cost of larger control effort, which may lead to increased wear-and-tear and frequent saturation of the fi nal control element, greater interaction with other control loops and operator distress (Harris and Tyreus, 1987).It is an established fact that fi rst-order-plus-deadtime (FOPDT) approximations can adequately explain the behaviour of a wide range of processes and many industrially-proven techniques are now available for fi tting these models to plant data (see Seborg et al., 2004). The main objective of this paper was to compareThe derivative mode is often omitted in PID control strategies because it proves diffi cult to arrive by trial-and-error at a set of constants which meet plant requirements. The primary objective of this paper was to evaluate several model-based PID tuning methods. For lag-dominant processes, it was recommended that the SIMC algorithm fi rst be employed to determine whether satisfactory performance can be obtained with PI control. If it cannot, then derivative action should be introduced using the DS-d technique. For delay-dominant systems, IMC tuning is preferred. It was observed that when confi gured with the same derivative fi lter factor, the series form of the PID controller produces smoother valve adjustments than the parallel version, at the expense of a slight decrease in best achievable performance. Increasing this parameter improves the control effort but limits achievable performance.Le mode différentiel est souvent omis dans les stratégies de contrôle PID parce qu'il s'avère diffi cile de parvenir par essais-erreurs à un ensemble de const...

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confidence: 99%

A Comparison of PID Controller Tuning Methods

Foley

Julien²,

Copeland

2005

Can J Chem Eng

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“…To solve the nonlinear system (28)-(29) or (31)-(32), resort can be made to standard Matlab Ⓡ tools [17], such as the fsolve function based on the quadratically convergent Newton-Raphson algorithm which requires the specification of a starting point. It turns out experimentally that the solution corresponding to the global optimum can be reached fast from the parameter values determined according to the second methof of Ziegler and Nichols [3] or one of its many variants [1,2].…”

Section: Solution Proceduresmentioning

confidence: 99%

“…Observe, in this regard, that model (1) allows us to deal satisfactorily with robustness issues. For example, the set of all PID controllers ensuring given stability margins can easily be found when the plant is described by (1) [2,[5][6][7]. However, despite these advantages, the irrational nature of (1) hinders the application of analytically-based techniques to the approximation of high-order systems by means of FOPDT models.…”

Section: Introductionmentioning

confidence: 99%

L²–Optimal Fopdt Models of High–Order Transfer Functions

Casagrande

Krajewski

Viaro

2016

Asian Journal of Control

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Necessary conditions for the L2 optimality of a first order plus dead time (FOPDT) model of a high‐order plant are derived using classic analytic function theory. They are expressed as a set of three nonlinear equations that partly resemble the interpolation conditions valid for rational approximation. From these conditions a simple procedure to find the optimal FOPDT model is obtained. Examples taken from the relevant literature are worked out to show the performance of the method in comparison with alternative techniques.

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“…A large amount of research work has already been carried out in the past to determine the PI controllers' parameters [13]. Numerous tuning techniques are summarized in [11,14] and the references therein. Each tuning technique has its own features and constraints.…”

Section: Introductionmentioning

confidence: 99%

Optimum design of proportional-integral controllers in grid-integrated PMSG-based wind energy conversion system

Tripathi

Tiwari

Singh

2015

Int. Trans. Electr. Energ. Syst.

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SUMMARYIn this paper, a direct-driven small permanent magnet synchronous generator (PMSG) wind energy conversion system (WECS) with a back-to-back power converter working in grid-connected mode is discussed. Control structures based on field-oriented control and voltage-oriented control mechanisms are proposed for machine-side converter and grid-side converter, respectively. Optimization approaches namely 'modulus optimum' and 'symmetric optimum' are used to obtain analytical expressions for the selection of the parameters of involved proportional-integral (PI) controllers in different control loops. A transient system simulation using SimPowerSystem is built to evaluate the performance of the PMSG-based WECS by employing selected values of PI controller parameters both under varying wind conditions and under symmetrical and asymmetrical grid-fault conditions.

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Handbook of PI and PID Controller Tuning Rules

Cited by 229 publications

References 0 publications

A Comparison of PID Controller Tuning Methods

A Comparison of PID Controller Tuning Methods

L²–Optimal Fopdt Models of High–Order Transfer Functions

Optimum design of proportional-integral controllers in grid-integrated PMSG-based wind energy conversion system

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Handbook of PI and PID Controller Tuning Rules

Cited by 229 publications

References 0 publications

A Comparison of PID Controller Tuning Methods

A Comparison of PID Controller Tuning Methods

L2–Optimal Fopdt Models of High–Order Transfer Functions

Optimum design of proportional-integral controllers in grid-integrated PMSG-based wind energy conversion system

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L²–Optimal Fopdt Models of High–Order Transfer Functions