Magnitude Optimum Techniques for PID Controllers

Vrančić, Damir

doi:10.5772/34404

Cited by 8 publications

(21 citation statements)

References 15 publications

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“…Namely, proportional (P) and PD controllers are not capable of rejecting input disturbances. However, in the paper, we show that MO criteria can be met by using 2-DOF PI controllers, similar to the case when optimising disturbance-rejection performance [60]. A 2-DOF controller implements reference weighting factor b.…”

Section: Introductionmentioning

confidence: 60%

“…According to [58], "one possible design aim is to maintain the closed-loop magnitude response curve as flat and as close to unity for as large a bandwidth as possible" (see Figure 2). This technique is called magnitude optimum (MO), modulus optimum, or Betragsoptimum, and results in a fast and non-oscillatory closed-loop time response for a large class of process models [60].…”

Section: Appl Sci 2020 10 X 4 Of 32mentioning

confidence: 99%

“…The fulfilment of Equation 3is simple. When using the controller structure containing an integral term, the steady-state control error becomes zero (under the This technique is called magnitude optimum (MO), modulus optimum, or Betragsoptimum, and results in a fast and non-oscillatory closed-loop time response for a large class of process models [60].…”

Section: Appl Sci 2020 10 X 4 Of 32mentioning

confidence: 99%

“…The MO method can be applied to various processes frequently encountered in chemical and processing industries. Tracking and disturbance-rejection closed-loop responses are usually fast without oscillations [60]. The applicability of the MO method has been extended by using a nonparametric approach in the time-domain instead of using explicit parametric identification of the process.…”

Section: Introductionmentioning

confidence: 99%

“…The extension is based on multiple integrations of process input and output signals, and is hence called the magnitude optimum multiple integration (MOMI) method [61]. The MOMI method extracts the required information from a simple time-domain experiment while retaining all advantages of the MO method [60].…”

Section: Introductionmentioning

confidence: 99%

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Parametric and Nonparametric PI Controller Tuning Method for Integrating Processes Based on Magnitude Optimum

2020

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Integrating systems are frequently encountered in the oil industry (oil–water–gas separators, distillation columns), power plants, paper-production plants, polymerisation processes, and in storage tanks. Due to the non-self-regulating character of the processes, any disturbance can cause a drift of the process output signal. Therefore, efficient closed-loop control of such processes is required. There are many PI and PID controller tuning methods for integrating processes. However, it is hard to find one requiring only a simple tuning procedure on the process, while the tuning method is based either on time-domain measurements or on a process transfer function of arbitrary order, which are the advantages of the magnitude optimum multiple integration (MOMI) tuning method. In this paper, we propose the extension of the MOMI tuning method to integrating processes. Besides the mentioned advantages, the extension provides efficient closed-loop control, while PI controller parameters calculation is still based on simple algebraic expressions, making it suitable for less-demanding hardware, like simpler programmable logic controllers (PLC). Additionally, the proposed method incorporates reference weighting factor b that allows users to emphasize either the disturbance-rejection or reference-following response. The proposed extension of the MOMI method (time-domain approach) was also tested on a charge-amplifier drift-compensation system, a laboratory hydraulic plant, on an industrial autoclave, and on a solid-oxide fuel-cell temperature control. All closed-loop responses were relatively stable and fast, all in accordance with the magnitude optimum criteria.

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Section: Introductionmentioning

confidence: 60%

Section: Appl Sci 2020 10 X 4 Of 32mentioning

confidence: 99%

Section: Appl Sci 2020 10 X 4 Of 32mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations