EJEEE
https://doi.org/10.62909/ejeee.2025.001 Edison Journal for Electrical and Electronics Engineering
Review
PID Control Perspective: Techniques and Uses
Wisam Subhi Al-Dayyeni 1, *, , and Hussam Saleh Mahmood 2,
1 School of IT and Engineering, ADA University, Baku, Azerbaijan; wdayyeni@ada.edu.az
2 Faculty of Science, Alexandria university, Alexandria, Egypt; Hussam.saleh_PG@alexu.edu.eg
* Correspondence: Tel.: +994 589 1446
Abstract: This article covers both conventional and modern methods for PID tuning and its appli-
cations in an array of fields. Because of its simple layout, ease of use, and ongoing research into PID
tuning, PID control is used in the vast majority of control systems that are now in use. The following
is the order of the tackles addressed in the paper: PID tuning occurs utilizing optimization rules that
range from traditional to modern. In an age of control systems and biomedical applications, this
work aims to examine the literature on PID control. A study of the evolution of conventional PID
and its integration with intelligent control has been executed, taking into account a number of ap-
plication fields. This document's main goal is to provide readers with an in-depth understanding of
PID commands in many application areas.
Keywords: Process control; PID; Auto-tuning; Optimal PID control
1. Introduction
The PID controller has a longstanding history in the area of computerized control. By
[1, 2] created by the steam engine and governor, which was recognized as the sooner ad-
verse feedback mechanism. Moreover, a mathematical model was developed for the gov-
ernor's control of the steam engine. It categorized the governors into two groups: moder-
ators and authentic governors. In contemporary terminology, he characterizes moderators
as controllers that utilize solely proportional control action, whereas authentic governors
are defined as controllers that employ both proportional and integral control actions.
Then, by [3], it presented a theoretical study of the derivative of mistake and its instanta-
neous rate of change. Their contribution, first dismissed by naval operators owing to per-
sonnel resistance, facilitated the later development of contemporary PID controllers.
The result of these two actions was that both controllers were provided with PID
control. After several years, the issue of steady-state error in the proportional controller
was mitigated by calibrating the setpoint to an arbitrary number until the error reached
zero. This resetting "integrated" the error and became recognized as the proportional-in-
tegral controller [4]. The inaugural inflatable controller including derivative action was
created, effectively reducing overshoot issues. However, the designers could not ascertain
the suitable values for the PID controllers, when the adjustments and restrictions pro-
posed by were adopted. Artificial PID controllers were widely utilized in factories around
in [5, 6]. In subsequent phases, experts have concentrated on the adjustment of PID con-
trol, including self-tuning and auto-tuning [5, 7], genetic tuning of PID [8, 9], as well as
strong and optimum adjustment [10], among others. Additionally, Smart PID and PID-
based control strategies are presented in [11, 12], fuzzy PID in [13, 14], optimal PID con-
troller design in [15, 16], adaptive PID control in [17], and fractional order PID in [18, 19].
Figure 1 illustrates that PID control utilizes several algorithmic ways: proportional,
integral, and derivative. The proportional component integrates suitable proportional
Citation: Al-Dayyeni, W.S. and
Mahmood, H.S., PID Control Per-
spective: Techniques and Uses. Edi-
son Journal for Electrical and Elec-
tronics Engineering, 2025. 3: p. 1-8
Academic Editor: Asst. Prof. Wurod
Qasim Mohamed
Received: 29/11/2024
Revised: 28/12/2024
Accepted: 12/1/2025
Published: 17/1/2025
Copyright: © 2024 by the authors.
Submitted for possible open access
publication under the terms and
conditions of the Creative Commons
Attribution (CC BY) license
(https://creativecommons.org/license
s/by/4.0/).
EJEEE 2025, Vol.3 2 of 8
adjustments for mistake, defined as the discrepancy between the setpoint and the method
factor, into the control response.
The integrated component analyzes the procedure constant throughout duration and
adjusts the result to minimize the divergence from the processing constant. Derivative
control method observes the pace of alteration of the process parameter and then adjusts
the result in response to atypical fluctuations. The customer modifies every setting of each
of the control mechanisms to get what they want from the entire procedure. Owing to its
straightforward architecture, ease of execution, and repair, PID controllers are the most
often utilized controllers in motion control, process control, power electronics, hydraulics,
pneumatics, and industrial sectors, among others [20]. The PID controller provides out-
standing functionality with a balance of costs and benefits that is challenging for other
controller types to match. They are also ubiquitous in contemporary uses, such as auton-
omous vehicles, unmanned helicopters, and robotic systems, for analogous purposes [21].
In the majority of automation applications, 91-96% of control loops are of the PID config-
uration.
The present article originally concentrated on the fundamentals of PID and the PID
tuning methodologies presented in prior publications. Subsequently, the research exam-
ines multiple fields in which the expressly utilized PID controller is analyzed, with the
corresponding PID control methodologies employed in those areas. This article discusses
the newest progress in PID, rendering it intelligent. The prospective research trajectory of
PID control methodologies has been delineated.
Figure.1 A schematic representation of industrial regulation with PID
2. Design and Adjustment Methodologies of PID Control Systems
2.1 Configurations of PID controllers with parameters
The series and parallel types of topologies are the most prevalent kinds of topologies
used for PID devices: Type of Parallelism: Within this particular shape, the action of pro-
portional P, integral I, or derivative D takes place in distinct solution phrases, and the total
is created by the combined impact of these three actions. Individual parameters in this
kind are not dependent on any other parameters, and the control rule that corresponds to
them is shown as follows [21]:
A PID controller receives the corresponding error signal (e), and its controller deter-
mines either the derivative of it and the total of this failure signal with regard to period.
This error signal is subsequently transmitted to the controller. The proportional gain (kp)
multiplied by the magnitude of the oversight, the integral gain (ki) multiplied by the in-
tegral of the mistake, and the gain from the derivative (kd) multiplied by the derivatives
of the mistake are all components that make up the regulation signal (u) that is sent to the
facility.
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Series Sort: The series the formula, also known as an involved the formula, is primar-
ily derived from the features of hydraulic and analogue electrical circuits. As is the case
with an ideal PID the formula, changing it in has an effect on each of the activities; how-
ever, the impact on proportional action is exerted by both derivative and integral varia-
bles. For both types, figure 2 (a) and (b) can present series and parallel type respectively.
Figure.2 Configuration of PID (a) Series Type
(b) Parallel Type
2.2 Tuning Methods
2.2.1 Traditional adjusting techniques
History identifies classical approaches for PID controller tuning, including the Zieg-
ler–Nichols Frequency Response Method [22], Relay Tuning Method [23], and Cohen-
Coon Procedure [24]. It has been discovered that about the proportion of processes dead-
time to time stable, together with the cancellation of processes poles, mostly employs the
PID controller. Classical tuning approaches rely on specific beliefs regarding the plant and
the intended output, aiming to derive logical or visual characteristics of the process to
inform the controller choices. These algorithms are straightforward to implement and ex-
hibit rapid calculation. These strategies are effective in the early phase but fail to yield the
intended outcomes consistently owing to underlying assumptions, necessitating further
refinement.
2.2.2 Cognitive optimization techniques
The intricate structures and efficiency requirements of the controller architect need
the development of novel adjusting design methodologies subsequent to the advent of
traditional PID controller adjusting methods. Over decades of time, several. Significant
understandings were acquired regarding PID tuning methodologies for enhanced perfor-
mance-specific parameters and to address additional. Complex systems. Previously, tra-
ditional tuning approaches were exclusively applicable to first-order and second-order
types. This is a disadvantage of conventional tuning approaches for PID regulation.
In Ref. [7], the authors suggested a more rapid tuning strategy and offered a Newton
Raphson examination approach, which is to be easy, and it destroys the complicated root
systems of the typical equation. in [25], it has been executed in different adjusting tech-
niques for single-input single-output and multiple-input multiple-output process. The
strategies are examined in many cases, and the results are time-delay systems and allo-
cated parameter methods. then, some investigators donated the tuning techniques like
auto-tuning [26], genetic algorithm strategy [9], model matching strategy [27], and unex-
plored PID tuning strategy [27], which were the essential elements of the mentioned strat-
egy produced transfer function, reference standard, and linear equations which rely on
Markov parameters. In ref. [28], it has also represented the auto-tuning method for choos-
ing the tilting for the two-input two-output system. This approach has dual relay regula-
tors employed to complete the required commonness as well as improvement of the
method. In ref. [29], it has concentrated on combining and dangerous operations. They
presented a systemic PI and PID tuning process, which relies on an optimum resonance
specification to easy terms of the parameters. In Ref. [30], he offered unique PID tuning
management based on scenario research and fuzzy logic tuning directions. In [31], he
EJEEE 2025, Vol.3 4 of 8
stated that improperly tuned, and tries to execute attack this issue systematically which
easily manipulated and to analyze an irregular most delinquent efficient method. Lastly,
an adaptive control technique relevant to these systems was presented with online tuning
[32].
3. PID Programs
3.1 PID for Industrial Regulation
In industrial regulation, PID regulators are popularly employed and consequently
recorded in the plurality of computerized regulation readers. It has proposed a strategy
as a design support for inaccurate ON/OFF switches [33]. In refs [34, 35], a Self-tuning has
been executed for status regulation of moisture tank and Aluminum. Ref. [36] has pre-
sented a recent multivariable self-tuning method for a class plus temperature regulation
strategy. Additionally, [37] has donated by creating a relative evaluation of other adaptive
regulation strategies for temperature techniques related on a Z to N strategy. In [38] These
techniques are employed in eclectic topics for high-order dynamics. The proposed tuning
method in this reference depends on the view of the system. The regard ideal for an easy
second-order method was described as:
Y(s) =
(2)
(3)
(4)
(5)
(6)
(7)
For all parameters in Eq.(s) are founded in ref. [39], see them there for more infor-
mation. Also, a lot of investigators carried out forward PID control techniques for differ-
ent approaches and their applications in [40, 41] and in [42, 43]. In [44] presents dynamic
disorder denial power for the creation of a maximum PID controller for a related tank
design.
3.2 PID for Automated Operators
Notwithstanding advancements in current concepts of control, robot manipulation
circuits frequently employ traditional PD or PID methods, mainly owing to their concep-
tual elegance and straightforward adjustment processes. There are a lot of researchers
work in this fields of automated operation like [45] based on distributed signal derived
through the calculated energy, [46] best PID numerically executed, [47, 48] the constancy
of PID regulator for commercial robotic manipulators, [49] complicated simulation exhib-
iting analogous behaviors, [50, 51] flexible configuration.
Investigation concerning PID controllers used to operate robots is divided into three
domains. The initial field encompasses the adjustment of PID advantages by the imple-
mentation of smart control methods, such as fuzzy logic control, neural networks, and
genetic methods [52, 53]. The next study field focuses on PID increase selection techniques
utilizing control approaches, including optimum policies [54, 55]. The final category of
study focuses on PID obtain choices employing explicit analysis of stability through Lya-
punov resilience [56, 57].
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3.3 PID Control for Engines and Energy Uses
Briefly, the execution of PID regulation for the converter from DC to DC was con-
ducted by ref [58, 59]. PID rely on control techniques are successfully used in power plant
and electrical energy plant operations [60, 61].
3.4 PID Control for Medical Devices
Diverse PID rely on control methodologies have been suggested for medical uses,
including blood vessel pressure infusion and legislation, reducing muscle tension in sur-
gical recipients, angle of motion control for deliberately triggered muscles, transplanted
livers, and blood sugar oversight
An advanced PID controller and enhanced PID changing control for managing blood
sugar are presented in [62, 63]. A PID controller is employed to mimic a kidney [64].
Strong PID control is presented for the management of This drug sedation for kids [65].
3.5 PID Control for Dynamical Processes
In business, the majority of machinery are regulated by PID control methods. Inves-
tigations conducted on PID control methodologies relevant to dynamic magnetism brake,
cutting processes, quadrotors, static tension management systems, gravity structures, pi-
loting cranes, and grippers.
PID control algorithms are implemented for levitating in references [66, 67]. A satu-
ration relies on adjusting approach for a fuzzy PID controller is developed to regulate arm
spin [68]. A new study has developed optimum partial fuzzy PID control [69].
4. Conclusions and Prospective Study Directions
An exhaustive review indicates that the PID controller is probably the most prevalent
controller across all areas due to its straightforward form and ease of deployment. The
novel characteristics of automated adjustment have significantly streamlined the applica-
tion of PID control. A while ago, fractional-order PID integrated with fuzzy logic systems,
IMC-PID controller design, optimum PID control, and the integration of PID-observer
structures have garnered increasing interest.
In the near term, PID-based control methods, including optimum fractional order
PID, fractional fuzzy PID, and self-tuning PID, will be extended for use in arithmetic man-
agement systems. Furthermore, it might be stated that the PID has an autonomous tuning
capability, that has garnered increased interest from businesses. The optimization of PID
controllers is a significant study domain.
Acknowledgments: The authors declare, there is no any grant that support this work.
Conflicts of Interest: Declare conflicts of interest or state “The authors declare no conflict of inter-
est.”
EJEEE 2025, Vol.3 6 of 8
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