A novel controller tuning of pH neutralization process

Saji, K.S.; Sasikumar, M.

doi:10.1109/icsccn.2011.6024643

Cited by 4 publications

(5 citation statements)

References 6 publications

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“…In [6] author proposed a hybrid strategy based on Ant colony optimization (ACO) and Particle swarm optimization (PSO) techniques, as a way to tune the PID controller. Their efforts succeed in designing a robust controller.…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear model feedback linearization control strategy of a pH neutralization process

Elameen

Karsiti

Ibrahim

2014

2014 5th International Conference on Intelligent and Advanced Systems (ICIAS)

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pH neutralization process is considered as a difficult control problem, because of its high nonlinearity dynamics, time-varying properties and its sensitivity to changes around the neutralization point. Therefore, there is a need to design a robust control algorithm to handle this problem. In this paper a preliminary investigation into application of feedback linearization in the dynamic modeling of pH in complex, time-varying system have been carried out. The controller is designed based on feedback linearization as a technique to transfer the system into a linear one, with pole placement method as a linear controller. The result shows the effectiveness of the proposed strategy.

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Section: Introductionmentioning

confidence: 99%

“…Efforts done in [6,7] concentrated on the potential tuning methods of PID controller. In [6] author proposed a hybrid strategy based on Ant colony optimization (ACO) and Particle swarm optimization (PSO) techniques, as a way to tune the PID controller.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear model feedback linearization control strategy of a pH neutralization process

Elameen

Karsiti

Ibrahim

2014

2014 5th International Conference on Intelligent and Advanced Systems (ICIAS)

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“…A highly acidic or alkaline pH would be fatal to the ecological surroundings. However, control of a pH neutralization system is highly challenging and complicated because of the inherent nonlinearity in the system (Abdullah et al, 2012;Bharathi et al, 2007;Ibrahim & Murray-Smith, 2007;Saji & Sasi, 2011;Shinskey, 1973). Classical control methods prove to be beneficial only for linear processes and for analytical testing.…”

Section: Introductionmentioning

confidence: 99%

“…The most widely used controller for this type of system is continuous Proportional-Integral-Derivative (PID) control, and experiments and studies have been carried out to figure out the appropriate tuning method for the PID controller. Saji and Sasi (2011) employed particle swarm optimization and Ant colony optimization algorithm to tune the PID controller parameters. They achieved setpoint tracking, robustness, and sensitivity from their nonlinear controller (Saji & Sasi, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…Saji and Sasi (2011) employed particle swarm optimization and Ant colony optimization algorithm to tune the PID controller parameters. They achieved setpoint tracking, robustness, and sensitivity from their nonlinear controller (Saji & Sasi, 2011). Bingi et al (2016) preferred particle swarm optimization algorithm over Ziegler Nichols method to tune PID controller for a pH neutralization plant (Bingi et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Real-time implementation of QFT, GA, and BFTPSO controller for pH neutralization system

Aparna¹,

Jamal²

2021

JART

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The control of a pH process is complex because of severe nonlinearities in its behavior. A continuous pH neutralization process is usually represented as a first-order plus dead time system, but its gain varies for different operating points. Therefore, a conventional linear controller cannot be used, and the pH system was thus represented as a linear state-space model around an equilibrium point. This linear model was then used to compute the PID controller gains using robust and optimization techniques like quantitative feedback theory, bacterial foraging technique-based particle swarm optimization algorithm, and genetic algorithm. The corresponding controller gains resulting from the three algorithms were used to control the pH using a reconfigurable I/O device, NI myRIO-1900. Finally, the output time domain specifications and the servo and regulatory responses, resulting from the three algorithms, were compared in simulation and in real-time to deduce the appropriate tuning algorithm for this system.

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