Role of PID Control Techniques in Process Control System: A Review

Dubey, Vandana; Goud, Harsh; Sharma, Prakash Chandra

doi:10.1007/978-981-16-2641-8_62

Cited by 15 publications

(6 citation statements)

References 37 publications

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“…Therefore, temperature control is of utmost importance in industrial processes, especially in food processing during heating and cooling (e.g., baking, freezing, drying, and others). PID control is the most popular in process applications, due to its simplicity, stability, and low cost (Dubey et al, 2022). However, it has some limitations associated with parameter tuning, timing systems, etc.…”

Section: Introductionmentioning

confidence: 99%

CONVECTIVE DRYING KINETICS OF YACON (Smallanthus sonchifolius Poepp. Endl.) SLABS AND EVALUATION OF THE DRYER PID TEMPERATURE CONTROL SYSTEM

Baldeón,

Salas-Valerio,

Vidaurre-Ruiz

et al. 2024

Chil. j. agric. anim. sci.

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Yacon is a functional food that is rich in fructooligosaccharides (FOS) and highly perishable, which requires inexpensive methods for its preservation. The objectives of this research were to model the kinetics of convection drying of yacon slabs and to assess the effect of air velocity on the temperature record of the heating zone controlled by the Proportional-Integral-Derivative control system (PID). Different temperatures (60, 70, and 80 °C) at constant air velocity (0.87 m s-1) were evaluated in a convective tray drying system. To determine the effect of air velocity on the PID temperature controller, different air velocities (0.81-2.06 m s-1) at constant temperature (70 °C) were studied. The results showed that Page’s model was the best fitting of the data obtained, the effective diffusivity of convective drying of yacon ranged from 1.22x10-10 to 1.9x10-10 m2 s-1 and the activation energy was 21.76 kJ mol-1. With PID temperature control, it was observed that the best drying rate and lowest temperature signal error occurred at an air velocity of 1.46 m s-1, where the temperature signal was more stable and closer to the reference temperature. In conclusion, the kinetic modeling of the convective drying of yacon provides optimal drying parameters as well as behavior of mass and heat transfer phenomena, particularly considering that the effect of air velocity influences the drying kinetics and the PID temperature control of the dryer.

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Section: Introductionmentioning

confidence: 99%

CONVECTIVE DRYING KINETICS OF YACON (Smallanthus sonchifolius Poepp. Endl.) SLABS AND EVALUATION OF THE DRYER PID TEMPERATURE CONTROL SYSTEM

Baldeón,

Salas-Valerio,

Vidaurre-Ruiz

et al. 2024

Chil. j. agric. anim. sci.

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“…It optimizes control actions over a nite time horizon based on the predicted system response. On the other hand, PI and PID controllers are classical control strategies that rely on feedback control to adjust the control signal based on the error between the desired setpoint and the measured system response [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Optimal electrical vehicle-to-grid integration: three-phase three-level AC/DC converter with model predictive controller based bidirectional power management scheme

Srivastava,

Manas,

Dubey

2024

Electr Eng

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The control and modelling of an electric Vehicle charging station with a three-level converter are discussed in this study from both the grid side and the EV side. The primary subject of discussion is the control systems for charging stations with a bidirectional DC/DC charging regulator and a three-level AC/DC power conversion. In order to manage the duty cycle of the converter during the AC-to-DC conversion stage, three alternative controller techniques-proportional-integral, proportional-integral-derivative, and model predictive controller are developed in this study. The goal of the study is to understand how these controller techniques impact the battery's charging and discharging processes in a bidirectional charging system. To evaluate and compare the different controller schemes, the researchers use simulation results and assess their performance based on various criteria including charging/discharging e ciency, voltage and current, battery state-of-charge regulation, and response time. The simulation results show that in terms of peak overshoot and settling time, the MPC controller performs better than the conventional Proportional-Integral and Proportional-Integral-Derivative controllers. The signi cance of the controller selection in bidirectional charging systems, emphasizing the bene ts of using Model Predictive Control for better battery charging and discharging outcomes.

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“…Self‐tuning control strategies are useful to regulate systems with unknown dynamics, time‐variable parameters, and subjected to parameter variation. However, Proportional‐Integral‐Derivative (PID) and Proportional‐Integral (PI) controllers, which depends on plant model, still very popular in all sectors of industry due to its easy configuration, ease of realisation, and simple tuning 1‐3 . Naturally, the performance of these controllers is degraded when the controlled system has some difference between the model used for controller design and the real system or process.…”

Section: Introductionmentioning

confidence: 99%

“…However, Proportional-Integral-Derivative (PID) and Proportional-Integral (PI) controllers, which depends on plant model, still very popular in all sectors of industry due to its easy configuration, ease of realisation, and simple tuning. [1][2][3] Naturally, the performance of these controllers is degraded when the controlled system has some difference between the model used for controller design and the real system or process. Thereby, many researches investigate algorithms to perform an optimisation of PID controllers or to adjust them online, dividing the classical PID controller in three groups: plant-model-based controller, plant-model free strategy, or a hybrid of both perspectives.…”

Section: Introductionmentioning

confidence: 99%

A hybrid robust model reference adaptive controller and proportional integral controller without reference model for partially modeled systems

Evald

Hollweg

Tambara

et al. 2023

Adaptive Control & Signal

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Summary This work presents the discrete‐time stability analysis of a hybrid robust model reference adaptive controller and proportional‐integral controller (MRAC‐PI) controller using Lyapunov criterion. The control structure merges the classical PI controller with a robust model reference adaptive control, resulting in an adaptive and robust control strategy, where the gains unite both control techniques in a unique structure and it does not require the design of a reference model, being able to track straightforwardly the reference signal. The adaptive gains are updated continuously by a modified gradient algorithm regarding control action, regulation error, and the system output. The stability analysis provides the controller design constraints, while also demonstrate the tendency of regulation error to a residual set, and proves the boundedness of all closed‐loop signals. Furthermore, as the controller is robust to unmodeled dynamics, it is feasible to control partially modeled systems. Simulation results in a real‐world engineering application are presented to corroborate the robustness of this controller and show its fast response in the face of critical exogenous system perturbations.

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Role of PID Control Techniques in Process Control System: A Review

Cited by 15 publications

References 37 publications

CONVECTIVE DRYING KINETICS OF YACON (Smallanthus sonchifolius Poepp. Endl.) SLABS AND EVALUATION OF THE DRYER PID TEMPERATURE CONTROL SYSTEM

CONVECTIVE DRYING KINETICS OF YACON (Smallanthus sonchifolius Poepp. Endl.) SLABS AND EVALUATION OF THE DRYER PID TEMPERATURE CONTROL SYSTEM

Optimal electrical vehicle-to-grid integration: three-phase three-level AC/DC converter with model predictive controller based bidirectional power management scheme

A hybrid robust model reference adaptive controller and proportional integral controller without reference model for partially modeled systems

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