A new PID controller tuning system and its application to a flue gas temperature control in a gas turbine power plant

Yukitomo, M.; Iino, Yutaka; Hino, Shiro; Takahashi, Kazutaka; Nagata, Kazue

doi:10.1109/cca.1998.721685

Cited by 15 publications

(7 citation statements)

References 2 publications

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“…The regulating loop feedback controller follows from (A42) by setting T þ d to zero, and using the gain definitions (A50), to obtain The cost-function can be optimized directly but a simple iterative solution can be obtained if the integral is approximated by a summation with a sufficient number of frequency points fo 1 ; o 2 ; :::; o N g: The optimization can then be performed by minimizing the sum of squares at each of the frequency points as used in a different context by Yukitomo et al [45]. The maximum frequency can be taken as the foldover frequency.…”

Section: A3 Polynomial Matrix Formsmentioning

confidence: 99%

Restricted structure predictive optimal control

Grimble

2004

Optim Control Appl Methods

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The design of low-order predictive optimal controllers, that involve a multi-step cost index and future setpoint knowledge, is considered. The usual predictive controller is of high order and the aim is to develop simpler structures, suitable for applications where PID controllers might be employed. The system is assumed to be represented by a discrete-time state-space model, which is very general, and the quadratic cost-function may include dynamic cost weighting terms. Using this approach, it is straightforward to generate a much lower-order predictive controller and thereby simplify implementation. Even with a continuing improvement in computational power there are many good reasons why low-order simple controllers have advantages in real applications

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Section: A3 Polynomial Matrix Formsmentioning

confidence: 99%

Restricted structure predictive optimal control

Grimble

2004

Optim Control Appl Methods

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“…Experience reveals that this back substitution iterative process always seems to converge but a proof of convergence properties is still to be obtained. The optimization can be performed by minimizing the sum of squares at each of the frequency points as used by Yukitomo et al [19]. The maximum frequency can be taken as a decade above the unity gain crossover frequency.…”

Section: Least Squares Solutionmentioning

confidence: 99%

“…The power spectra included in the cost-function (20) may be de"ned, using Equations (14)} (19), and noting the noise sources are assumed to be statistically independent. Also de"ne the signal f as…”

Section: Appendix A: Solution Of the Restricted Structure Series Tracmentioning

confidence: 99%

Restricted structure control of multiple model systems with Series 2 DOF tracking and feedforward action

Grimble

2001

Optim Control Appl Methods

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SUMMARYThe solution of a scalar optimal control problem is discussed where the feedback, series tracking and feedforward controllers are chosen to have a very simple. Each controller term may be chosen to be of reduced order, lead/lag, or PID forms, and the controller is required to minimize an LQG cost-index. The optimization is based upon a cost-function which also allows separate costing of the terms due to the feedback, tracking and feedforward controllers. The system model can be uncertain and can be represented by a set of models over which the optimization is performed. This provides a form of robust optimal control that might even be applied to non-linear systems that can be approximated by a set of linearized models.The theoretical problem considered is to obtain the causal, stabilizing, feedback, series-tracking and feedforward controllers, of a prespeci"ed form, that minimize an LQG criterion over the set of possible linear plant models. The underlying practical problem of importance is to obtain a simple method of tuning low-order controllers, given only an approximate model of the process. The results are illustrated in a power generation control problem for a system represented by 12 di!erent linearized plant models. The single feedback controller that is obtained has a simple form and stabilizes the full set of models.

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“…A more recent approach to designing H 2 and H 1 controllers, with a PID structure, is referred to as restricted structure optimal control [79]. In this approach a controller of restricted structure (RS) is optimised to minimise a given H 1 or H 2 cost index, which is very valuable for applications [80]. The same restricted structure ideas may be applied to feed-forward and tracking control problems [86].…”

Section: H 2 and H 1 Design And Relationship To Pid Controlmentioning

confidence: 99%

Introduction to Optimal and Robust Control

Grimble

2006

Robust Industrial Control Systems

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A new PID controller tuning system and its application to a flue gas temperature control in a gas turbine power plant

Cited by 15 publications

References 2 publications

Restricted structure predictive optimal control

Restricted structure predictive optimal control

Restricted structure control of multiple model systems with Series 2 DOF tracking and feedforward action

Introduction to Optimal and Robust Control

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