Computer-aided process control system design using interactive graphics

Edgar, Thomas F.; Heeb, R.; Hougen, Joel O.

doi:10.1016/0098-1354(81)87015-9

Cited by 31 publications

(11 citation statements)

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“…The PM is within 10% of 45", and M , is 1.30 in all cases. The second example presented in Table 2 is a process for which Edgar et al (1981) also designed controllers. Both methods gave comparable results in terms of closed-loop system performance.…”

Section: Examples and Discussionmentioning

confidence: 99%

“…With some techniques, e.g., Ziegler-Nichols, an unstable system occasionally is obtained. Graphical methods, such as the one recently presented Correspondence concerning this paper should be directed to Sandra L H~~~~~ by Edgar et al (1981) based on the interactive use of a graphics terminal, yield much superior results in terms of system performance, but can be quite time consuming to use.…”

Section: Introductionmentioning

confidence: 99%

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Controller tuning using optimization to meet multiple closed‐loop criteria

Harris

Mellichamp

1985

AIChE Journal

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magnitude of the coefficients. However, when the average mass transfer coefficient was matched to the total drop formation time, a linear correlation was again obtained on semilogarithmic coordinates. Figure 2 shows this result for the present study of dispersed phase resistance. DISCUSSION LITERATURE CITEDHeertjes, P. M., W. A. Holve, and H. Talsma, "Mass Transfer Betweer, Isobutanol and Water in a Spray-Column," Chem. Eng. Sci., 3, 122 (1954).

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Section: Examples and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Controller tuning using optimization to meet multiple closed‐loop criteria

Harris

Mellichamp

1985

AIChE Journal

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“…It consists in finding the ideal parameters by the Maclaurin series. This method is compared with the method in [24,25]. The method in reference [23] outperforms the other methods.…”

Section: Introductionmentioning

confidence: 99%

Cascade Control of Grid-Connected PV Systems Using TLBO-Based Fractional-Order PID

Badis

Mansouri

Boujmil

2019

International Journal of Photoenergy

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Cascade control is one of the most efficient systems for improving the performance of the conventional single-loop control, especially in the case of disturbances. Usually, controller parameters in the inner and the outer loops are identified in a strict sequence. This paper presents a novel cascade control strategy for grid-connected photovoltaic (PV) systems based on fractional-order PID (FOPID). Here, simultaneous tuning of the inner and the outer loop controllers is proposed. Teaching-learning-based optimization (TLBO) algorithm is employed to optimize the parameters of the FOPID controller. The superiority of the proposed TLBO-based FOPID controller has been demonstrated by comparing the results with recently published optimization techniques such as genetic algorithm (GA), particle swarm optimization (PSO), and ant colony optimization (ACO). Simulations are conducted using MATLAB/Simulink software under different operating conditions for the purpose of verifying the effectiveness of the proposed control strategy. Results show that the performance of the proposed approach provides better dynamic responses and it outperforms the other control techniques.

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“…To obtain a second-order plus dead-time model, a Taylor series expansion of the dead-time term is combined with the ultimate data matching technique of Chen (1989). To tune the PID controller, a frequency domain method based on methods of Edgar et al (1981) and Harris and Mellichamp (1985) is applied, yielding controller settings much superior to the ZN method or the first-order methods.…”

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confidence: 99%

An improved technique for PID controller tuning from closed‐loop tests

1990

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The proportional-integral-derivative (PID) controller is the dominant type of controller used in current industrial practice. The behavior and capabilities of the PID controller are familiar to both the design engineer and the field operator, and it is relatively easy to tune manually by a variety of open-loop methods (Seborg et al., 1989). An important recent improvement in the PID controller tuning method is a self-tuning capability (Astrom and Hligglund, 1,984; Seborg et al., 1989), while the control loop is closed. Here we propose a new closed-loop tuning method for the PID controller plus dead-time models, the control performances for some processes are very poor. Here we improve the YS method by identifying processes with a second-order plus dead-time model under closed-loop conditions. We also employ a more elaborate frequency domain tuning method. To obtain a second-order plus dead-time model, a Taylor series expansion of the dead-time term is combined with the ultimate data matching technique of Chen (1989). To tune the PID controller, a frequency domain method based on methods of Edgar et al. (1981) and Harris and Mellichamp (1985) is applied, yielding controller settings much superior to the ZN method or the first-order methods. Second-Order Plus Dead-Time Model IdentificationTo identify a model of a process, Gp(s), a control system under proportional control [G,(s) = KJ is tested. We choose a proportional controller with a sufficiently large gain so that the closed-loop system is underdamped. From a response curve to step change in set point of magnitude A, we can obtain an approximate model of the closed-loop system G,,(s) as (Yuwana and Seborg, 1982;Chen, 1989);

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Computer-aided process control system design using interactive graphics

Cited by 31 publications

References 0 publications

Controller tuning using optimization to meet multiple closed‐loop criteria

Controller tuning using optimization to meet multiple closed‐loop criteria

Cascade Control of Grid-Connected PV Systems Using TLBO-Based Fractional-Order PID

An improved technique for PID controller tuning from closed‐loop tests

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