Centralized Proportional Integral Controller Design for the Activated Sludge Process

Anchan, Sanjith S.; Rao, C. Lakshmana

doi:10.1002/ceat.202100409

Cited by 2 publications

(3 citation statements)

References 24 publications

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“…32 Because of its ability for maintaining zero steady-state error for change in step responses, the PI controller is widely used in many process industries. [33][34][35] In the current study, the control system uses a multivariable PI controller proposed by Penttinen that adopts a manual controller tuning approach based on conventional Ziegler-Nichols tuning 36 counterparts for SISO systems. These tuning methods involved the tedious task of regulating the tuning values manually until a stable response with minimum performance indices are obtained.…”

Section: Introductionmentioning

confidence: 99%

“…Accordingly, the controller in the data‐driven approach does not depend on the accuracy of the plant model, and the twinborn problem of unmodeled dynamics does not prevail under a data‐driven framework 32 . Because of its ability for maintaining zero steady‐state error for change in step responses, the PI controller is widely used in many process industries 33–35 . In the current study, the control system uses a multivariable PI controller proposed by Penttinen that adopts a manual controller tuning approach based on conventional Ziegler–Nichols tuning 36 counterparts for SISO systems.…”

Section: Introductionmentioning

confidence: 99%

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Optimal detuning of multivariable proportional integral controller based on data‐driven approach for an activated sludge process

Anchan

Tanneru

Rao

2023

Asia-Pacific J Chem Eng

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The study deals with designing a multivariable centralized proportional‐integral (PI) controller for an activated sludge process (ASP) by adopting a new tuning technique. To overcome the tedious task of manual tuning by a classical method that was based on Ziegler–Nichols tuning, a novel approach is developed by formulating the optimization problem and considering it along with the theoretical results to calculate the controller parameters. The proposed method is then compared with the reported method to demonstrate the advantages of the proposed approach. A nonlinear ASP model is selected to evaluate the performance of the designed controller. The simulation carried through the proposed optimization technique exhibits a significant improvement in closed‐loop performance for tuning the centralized controller. It is observed that the proposed method minimizes the effort of tuning the controller significantly. The proposed controller achieves better load disturbance response and lower overshoot than the reported method. The proposed method could improve integral absolute error (IAE) and integral time absolute error (ITAE) performance indices, ranging between 18.69% to 53.80% and 16.42% to 52.97% for a step change in and , respectively, which are better compared to the reported method. The study also assesses the control system's robustness for their uncertainties with the time constant and time delay.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal detuning of multivariable proportional integral controller based on data‐driven approach for an activated sludge process

Anchan

Tanneru

Rao

2023

Asia-Pacific J Chem Eng

Self Cite

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“…Centralized controllers are full-structured controllers where the off-diagonal elements of the controller minimize the loop interactions. Most centralized controller design approaches reported in the literature focus on integer-order (IO) processes (Anchan and Sankar Rao, 2022; Besta and Chidambaram, 2017; Ram and Chidambaram, 2015).…”

Section: Introductionmentioning

confidence: 99%

Centralized model matching integer/fractional-order controller design for multivariable processes with real-time validation

Muthukumari,

Kanagalakshmi,

Sunil Kumar

2023

Transactions of the Institute of Measurement and Control

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This paper proposes a novel approach to design a centralized model matching integer/fractional-order (FO) controller for multivariable processes. As a first step, for a linear time-delay process, a desired closed-loop reference model is formulated with the inclusion of a linear quadratic regulator with integral controller. The reference model is formulated with the objective of embodying the desired closed-loop specifications. The second step involves equating the closed-loop system with the reference model, leading to a synthesis equation that yields a higher-order controller. In the third step, the lower-order approximant of the controller is obtained by matching a set of approximate generalized time moments/approximate generalized Markov parameters of the higher-order controller to that of its lower-order approximate at a few expansion points in the s-plane. The selection of an optimal set of expansion points is formulated in an optimization problem frame with the aim of minimizing the area between responses of the desired and designed closed-loop systems, keeping the stability of the designed closed-loop system as a constraint. The efficacy of the proposed approach is illustrated by the design of different controllers for time-delay-dominated nonsquare process and FO square process. Comparative analysis is carried out using time-domain performance indices by introducing parameter uncertainty and disturbance signals. To demonstrate the applicability of the proposed method, a real-time investigation is carried out with the design and implementation of a controller for interacting hybrid (conical and spherical) tank process setup.

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Centralized Proportional Integral Controller Design for the Activated Sludge Process

Cited by 2 publications

References 24 publications

Optimal detuning of multivariable proportional integral controller based on data‐driven approach for an activated sludge process

Optimal detuning of multivariable proportional integral controller based on data‐driven approach for an activated sludge process

Centralized model matching integer/fractional-order controller design for multivariable processes with real-time validation

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