Simultaneous automatic tuning of cascade control systems from closed-loop step response data

Jeng, Jyh‐Cheng; Lee, Ming-Wei

doi:10.1016/j.jprocont.2012.04.010

Cited by 39 publications

(31 citation statements)

References 19 publications

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“…B-spline series B-splines are a natural signal representation for continuous signals, where many continuous-domain operations can be carried out exactly once a B-spline approximation has been done [6]. Introduce the B-splines on semi-infinite interval computations [6,13]. In this study, we use 3rd-order B-splines, with knot sequence 0,1, 2, , k k x x , which has the form of piecewise function [13], its plots as shown in Figure 1.…”

Section: B-spline Series Based Responses Approximationmentioning

confidence: 99%

“…Modeling computation is the key step to determine model accuracy [2][3][4][5][6][7], which can usually be divided into three categories. The first falls into frequency domain computations, the second belongs to time-domain based methods (many of them using least-squares estimate), and the third is to quantize graphic features.…”

Section: Introductionmentioning

confidence: 99%

“…By using Bspline series expansions to provide reasonable interpolation values [6,13], a weighted least-squares estimate is implemented to compute the accurate model parameters. Compared with the previous methods, the proposed method has two advantages: 1) Even if there are less step response data available, the B-spline series will provide effective inter-and extrapolation data, and the interpolation does not decrease the accuracy and reliability of modeling algorithms; 2) The proposed compensation scheme takes into account truncation errors and estimated deviations from measurement, which improves modeling accuracy better.…”

Section: Introductionmentioning

confidence: 99%

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Identification of first-order plus dead-time model from less step response data

Luo

Cai

Liu

et al. 2014

2014 9th IEEE Conference on Industrial Electronics and Applications

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This study presents an improved method for firstorder plus dead-time (FOPDT) model identification from less step response data. Firstly, B-spline series expansions are used to approximate step responses, providing effective interpolation values for modeling computation. Then, to enhance modeling accuracy, a least-squares method with a regressive compensation scheme is proposed, which adaptively adjusts error weight coefficients to minimize the response deviation between identified model and actual process. The proposed identification method is not only suitable for cases of less sampling data, but also for those of non-uniform sampling data (where most existing methods of transfer function model identification cannot be applied directly). Simulation results illustrate the effectiveness of the proposed approach.

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Section: B-spline Series Based Responses Approximationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Identification of first-order plus dead-time model from less step response data

Luo

Cai

Liu

et al. 2014

2014 9th IEEE Conference on Industrial Electronics and Applications

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“…Therefore, the following correlated robust design criterion 15 provides the required value of λ̅ 2 , for a process parameter θ̅ 2 , to achieve a desired level of robustness for M S2 . …”

Section: Design Of the Secondary Controllermentioning

confidence: 99%

A Simultaneous Tuning Method for Cascade Control Systems Based on Direct Use of Plant Data

Jeng

Liao

2013

Ind. Eng. Chem. Res.

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This paper presents a novel method for tuning cascade control systems in which both primary and secondary controllers are tuned simultaneously by directly using plant data without resorting to process models. The required plant data are collected from a one-shot step test that can be conducted under either closed-loop or open-loop conditions. The goal of the proposed design is to obtain the parameters of two proportional-integral-derivative (PID) controllers such that the resulting inner and outer loops behave as closely to appropriately specified reference models as possible. The optimization problems related to the proposed design are derived. On the basis of the rationale behind cascade control, the secondary controller is designed to attenuate disturbance faster. The primary controller is designed to accurately account for the inner loop dynamics without requiring an additional test. Moreover, robustness consideration is included in the proposed tuning method, which enables the designer to explicitly address the trade-off between performance and robustness for inner and outer loops independently. Simulations show that the proposed method exhibits superior control performance compared with the existing (model-based) design methods, confirming the effectiveness of this novel method of PID controller design for cascade control systems. INTRODUCTIONCascade control is one of the most successful structures for enhancing the performance of single-loop control, particularly when disturbances are associated with the manipulated variable. 1 Thus, cascade control is applied extensively in chemical process industries. In the standard cascade-control approach, one feedback loop is nested inside another feedback loop using two controllers. The controller of the inner loop is called the secondary (or slave) controller, whereas the controller of the outer loop is the primary (or master) controller. The rationale behind this configuration is that the fast dynamics of the inner loop enable fast attenuation of disturbance and minimize the possible effects of disturbances before they affect the primary output, which is the controlled variable of interest.Although sophisticated schemes for cascade control have been proposed, 2−4 the basic scheme still comprises two nested loops with two proportional-integral-derivative (PID) controllers. Because this scheme involves tuning two PID controllers, the design of cascade control systems is more complex than the design of single-loop control systems. The usual approach involves first tuning the secondary controller by setting the primary controller on a manual mode. The primary controller is then tuned by considering the action of the secondary controller on the inner loop. Such a tuning procedure is time-consuming because at least two runs of the plant test are typically required. 5,6 However, the sequential tuning procedure has been improved so that only a single experiment is conducted for tuning the two controllers simultaneously. 7−12 In most methods of simultaneous tuning, low-ord...

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“…This makes the tuning of PID cascade control more difficult and takes more time consuming. Some researches on the simultaneous cascade control system have been carried out [8][9][10][11]. In PID cascade control, there are 6 parameters to be determined.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous optimization of tuning PID cascade control system using Duelist Algorithms

Biyanto¹,

Alfarisi²,

Afdanny³

et al. 2016

2016 International Seminar on Intelligent Technology and Its Applications (ISITIA)

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Abstract-In this research, Proportional-Integral-Derivative (PID) controller was integrated into the advanced control system to achieve an optimal performance. One type of advanced control system is cascade control. In conventional cascade control, controller parameters in primary and secondary loops are tuned sequentially. First, the parameters in secondary controller are tuned using certain tuning rule, meanwhile the primary loop in the open loop mode. Furthermore, the controller parameters in primary loop are tuned by considering the influence of the controlled secondary loop. This makes the tuning of PID cascade control more difficult and takes more time consuming. This paper describes a method of simultaneous tuning cascade control system using Duelist Algorithm (DA) to estimate the proper control parameters of the primary and the secondary loops in the plants. The analysis was conducted based on the evaluation of the closedloop response to a step set point changes. The results show that the proposed method has better control performance than the previous method. PID auto tuning using DA provide more robust performance of the control system.

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Simultaneous automatic tuning of cascade control systems from closed-loop step response data

Cited by 39 publications

References 19 publications

Identification of first-order plus dead-time model from less step response data

Identification of first-order plus dead-time model from less step response data

A Simultaneous Tuning Method for Cascade Control Systems Based on Direct Use of Plant Data

Simultaneous optimization of tuning PID cascade control system using Duelist Algorithms

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