2002
DOI: 10.1002/aic.690480622
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Optimal PID controller tuning method for single‐input/single‐output processes

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Cited by 16 publications
(12 citation statements)
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“…2b where, a linear PID controller controls the linear dynamic subsystem. So, we can tune the PID controller for the linear dynamic subsystem regardless of the nonlinear static function using various tuning rules like the ZN tuning method (Ziegler and Nichols, 1942), the margin speciÿcation tuning method ( Astr om and H agglund, 1984) and the optimal tuning method (Sung et al, 2002). Where y s (t) and z s (t) are the set point of the process output and the linear dynamic subsystem output, respectively andf is the estimated nonlinear static function.…”
Section: Control Strategysupporting
confidence: 47%
“…2b where, a linear PID controller controls the linear dynamic subsystem. So, we can tune the PID controller for the linear dynamic subsystem regardless of the nonlinear static function using various tuning rules like the ZN tuning method (Ziegler and Nichols, 1942), the margin speciÿcation tuning method ( Astr om and H agglund, 1984) and the optimal tuning method (Sung et al, 2002). Where y s (t) and z s (t) are the set point of the process output and the linear dynamic subsystem output, respectively andf is the estimated nonlinear static function.…”
Section: Control Strategysupporting
confidence: 47%
“…Sung et al [10] use a Levenberg-Marquardt optimization to find PID tuning parameters using analytical derivative formulas. Hjalmarsson et al [11] use what they termed iterative feedback tuning to find solve the optimization without analytical derivatives.…”
Section: Introductionmentioning
confidence: 48%
“…To deal with this problem, model-based controller tuning methods have been effectively developed (Morari and Zafiriou, 1989;Seborg, Edgar, and Mellichamp, 2003). Recently improved methods for tuning the proportional-integral-derivative (PID) controller, which is most commonly used in engineering practice, can be found in the literature (Piazzi and Visioli, 2006;Leva, Bascetta and Schiavo, 2005;Sree, Srinivas and Chidambaram, 2004;Skogestad, 2003;Ho et al, 2003;Sung, Lee and Park, 2002;Lee and Edgar, 2002;Hwang and Hsiao, 2002;Tan, Lee and Jiang, 2001). For such a controller design, a low-order process model is needed, of which the first-order-plus-deadtime (FOPDT) model structure is mostly used since it can effectively reflect the fundamental characteristics of process response, in particular for the low frequency range primarily referred to controller tuning (Åström and Hägglund, 1995;Yu, 2006).…”
Section: Introductionmentioning
confidence: 99%