INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025
Page 3330 www.rsisinternational.org
Development of a New Virtual Pneumatic Control Simulator for
Educational Purposes
*Khairuddin Osman
1
, Norzahirah Zainuddin
2
, Abdullah Haniff Kamal
3
1,3
Faculty of Electronic and Computer Technology and Engineering, Universiti Teknikal Malaysia
Melaka, 76100 Melaka, Malaysia
2
Kolej Komuniti Selandar, 77500 Melaka, Malaysia
DOI: https://dx.doi.org/10.47772/IJRISS.2025.910000272
Received: 14 October 2025; Accepted: 21 October 2025; Published: 10 November 2025
ABSTRACT
In order to generate force, convey power, and regulate motion, pneumatics which uses compressed air to transmit
and control energy is essential. Education must change to satisfy industry demands as automation technologies
proliferate. However, conventional hands-on pneumatic systems for teaching are frequently costly, logistically
difficult, and inaccessible, which prevents students from learning useful skills. This work discusses the design
and development of a free virtual pneumatic control simulator in order to address these issues. Students can
experiment with control strategies, visualize system reactions, and study pneumatic control concepts in an
interactive, software-based environment with the simulator. For simulation, the architecture combines Simscape
and MATLAB/Simulink, and real-time interaction and visualization are made possible using a Python/Flask-
Bootstrap interface. When compared to traditional techniques, the results show notable gains in students'
conceptual understanding and engagement. The potential of virtual labs to democratize engineering education is
demonstrated by this platform.
Keywords: Pneumatic, controller design, virtual simulator.
INTRODUCTION
Pneumatic systems use compressed air to transmit energy and control motion, they are used extensively in
automation, such as robotic actuation, material handling, and industrial processes. Because of their widespread
use in industry, engineering students must learn both theoretical and practical aspects of pneumatic dynamics,
actuators, and control algorithms. Despite their significance, hands-on pneumatic educational setups face
significant challenges, including high training kit and lab equipment costs, logistical challenges in setup and
maintenance, and limited accessibility for remote learners, which exacerbates the gap between theory and
practice. Additionally, traditional laboratory experiences frequently do not expose students to nonlinear system
behavior, such as valve dynamics, air compressibility, and frictional losses.
In order to facilitate pneumatic teaching, this paper presents the Virtual Pneumatic Control Simulator, a scalable
and cost-free system. In contrast to actual labs, the simulator gives students a way to simulate pneumatic systems,
change settings, and see how the systems react to various control schemes. By combining interactive
visualization and dynamic system modeling, it further connects theory to practice. The simulator offers a
Python/Flask-Bootstrap web-based interface for accessibility, a simulation framework for pneumatic actuators
based on MATLAB/Simulink and Simscape, and a comparative analysis of open-loop and closed-loop
controllers. Student feedback, which shows increased conceptual understanding and engagement, is also used in
the paper to support the platform. Compressors, actuators, cylinders, and directional control valves make up
pneumatic systems, which transform compressed air into mechanical motion. Physical lab kits have historically
been used in educational teaching; however, these setups are expensive and less scalable. Differential equations
are commonly used to express actuator displacement mathematically in dynamic modeling of pneumatic
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ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025
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systems. While Simscape adds realism by simulating multi-domain physical factors like compressibility and
valve delays, MATLAB/Simulink provides a platform for block-based modeling of pneumatic systems.
Both open-loop and closed-loop methods can be used to control pneumatic actuators. Although open-loop
systems are straightforward, they can become unstable when disturbed. PID control and state-space approaches
are two examples of closed-loop systems that use feedback. Because of its simplicity, PID control is still widely
used, yet state-space control provides improved stability and robustness.
The use of web-based or MATLAB-integrated simulation environments has been investigated in a number of
research. Although these technologies increase engagement, they frequently lack thorough integration of
complex control methods or user-friendly interfaces. Measurable gains in learning outcomes have been shown
with interactive platforms. By integrating Simscape's physical realism, real-time parameter adjustment, and
integration of numerous control strategies into a web-accessible platform, the simulator described in this work
builds on previous research.
BACKGROUND STUDY
Pneumatic Systems in Industrial Automation
Pneumatic systems have been a cornerstone of industrial automation for decades, offering a reliable, safe, and
cost-effective means of actuating machines in environments where hydraulic or electrical actuators may be
unsuitable. Their use spans diverse domains, including robotic grippers, automated conveyors, and material
handling systems. Pneumatic actuators provide clean operation since they do not involve lubricating fluids,
making them particularly suited for food processing and pharmaceutical industries. They are also preferred in
hazardous environments due to their intrinsic safety when compared to electrical drives [1].
From an educational perspective, pneumatics offers a valuable entry point into fluid power systems and control
theory. However, despite their simplicity in concept, pneumatic actuators exhibit nonlinear dynamics caused by
air compressibility, valve dead zones, and frictional effects. These nonlinearities make modeling and control
design more challenging than in purely mechanical systems [2], [3]. Students must therefore be exposed not only
to the mechanical construction of pneumatic circuits but also to dynamic modeling and control strategies that
account for such complexities.
Accurate modeling of pneumatic systems has been a subject of continuous research. Traditional approaches rely
on Newtonian mechanics and thermodynamic relations to derive governing equations. The force balance of a
piston-cylinder assembly, for example, is expressed as:
Mẍ(t) + Dx(t) + Cx(t) = A. P(t)̇ (1)
where M denotes the piston mass, D the damping coefficient, C the spring constant, A the piston area, and P(t)
the air pressure acting on the piston head. Such equations provide the foundation for control-oriented transfer
functions [4].
MATLAB/Simulink has become the dominant tool for simulating these models because of its graphical block-
based environment that allows easy representation of dynamic systems. Simscape, an extension of MATLAB,
offers physics-based components that incorporate compressibility, valve dynamics, and multi-domain
interactions. Researchers have highlighted that Simscape-based modeling better captures practical system
responses, enabling more accurate testing of control algorithms before deployment [5], [6].
Control Strategies for Pneumatic Systems
Control strategies in pneumatics can be broadly divided into open-loop and closed-loop approaches. Open-loop
systems, though simple, are prone to performance degradation under disturbances. They cannot compensate for
nonlinearities, resulting in poor accuracy and limited applicability in precision tasks [7].
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
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Closed-loop control introduces feedback, enabling corrective actions that improve stability and accuracy.
Among these, the PID controller remains the most widely used due to its intuitive structure and ease of tuning.
Nevertheless, PID control in pneumatics often suffers from large overshoots and oscillations unless carefully
tuned, as system nonlinearities make classical tuning rules insufficient [8].
Alternative methods include adaptive PID controllers, where gains are adjusted online based on operating
conditions [9]. State-space methods have also been employed for pole-placement control, offering superior
transient response and robustness [10]. More advanced research explores robust control, model predictive control
(MPC), and nonlinear controllers that can explicitly handle valve saturation and compressibility effects [11],
[12]. Despite their superior performance, these methods are often mathematically complex, making them less
accessible in an educational context.
Virtual Laboratories and Educational Simulators
The growing emphasis on e-learning and remote education has accelerated the adoption of virtual laboratories.
Virtual labs allow students to experiment with simulated equipment, reducing dependence on expensive
hardware while increasing accessibility. Studies have shown that virtual environments enhance conceptual
understanding and engagement compared to traditional lecture-only formats [13].
Several researchers have proposed simulation-based platforms for pneumatics. Montalvo-Lopez et al. [14]
developed a training environment using virtual reality for pneumatic circuits. Nasr and Kamel [15] employed
MATLAB Web Apps to deliver control experiments online, while Buhl [16] reported positive learning outcomes
using virtual control simulators. Pisano and Villani [17] emphasized the importance of interactivity, noting that
students learn more effectively when they can manipulate parameters and instantly observe responses.
Despite these advances, many existing simulators suffer from limitations. Some require expensive software
licenses or high-performance computing resources, restricting accessibility. Others provide simplified system
models that fail to capture nonlinear dynamics, limiting realism. Additionally, integration of advanced control
strategies into educational simulators remains limited.
Research Gaps and Motivation for This Work
A clear gap exists between industrial-grade pneumatic control and the resources available to students. While
physical labs provide realism, they are costly and difficult to scale for large classes. Existing virtual simulators
address cost but often compromise on either realism or user accessibility. Very few tools combine physics-based
realism, advanced control strategies, and web-based interactivity in a unified platform.
The Virtual Pneumatic Control Simulator presented in this work addresses these gaps by leveraging
MATLAB/Simulink and Simscape for accurate modeling, while integrating a Python/Flask-Bootstrap web
interface for universal access. Unlike previous works, the simulator supports both PID and state-space control
strategies, offers real-time parameter tuning, and has been validated with student feedback. This combination
provides a practical, cost-free, and pedagogically effective tool for pneumatic education, bridging the gap
between theoretical instruction and hands-on experience.
METHODOLOGY
The Virtual Pneumatic Control Simulator was developed using a multi-layered architecture that combines a web-
based interface, simulation environment, and system modeling.
Newton's laws were used to model the dynamics of the pneumatic actuator, and the transfer function may be
written as follows:
T(s) =
P(s)
X(s)
=
A
(Ms2+ Ds + C)
(2)
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025
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In this case, A stands for piston area, M for moving component mass, D for damping coefficient, and C for spring
constant. The model was enhanced in Simscape for physics-based realism and deployed in Simulink for control
analysis. There were two types of simulations created. While closed-loop models incorporated PID controllers
and state-space controllers with pole placement for stability optimization, the open-loop model was used as a
baseline reference.
HTML/CSS, JavaScript, and Bootstrap were used in the development of the web interface's front end. The
MATLAB Engine API was used to combine Python/Flask with MATLAB in the backend. With the use of this
interface, one can instantly visualize the responses of the system while adjusting parameters like mass, damping,
and air pressure in real time. Rise time, overshoot, settling time, and steady-state error were all evaluated in the
performance evaluation. Structured student feedback was used to gauge the success of the education. Fig. 1
shows the pneumatic system model, while Fig. 2 through 5 illustrate open-loop, PID, state-space, and user
interface views, respectively.
Fig. 1. Pneumatic System
Fig. 2. Open Loop System
Fig. 3. PID Controller
Fig. 4. State-Space (Pole Placement)
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025
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Fig. 5. User Interface for Real Time Simulation
RESULTS AND DISCUSSION
The response of the open-loop system is seen in Fig. 6. The curve displays a settling time of 2.68 seconds and a
significant overrun of 8.76%. This demonstrates the intrinsic drawback of open-loop pneumatic systems since
there is no feedback, making it impossible to remedy errors when nonlinearities or disturbances arise. In
industrial contexts, this kind of behaviour would actually result in poor repeatability, which is why feedback-
based control is crucial for pneumatic actuators
Fig. 6. Virtual Pneumatic Control Simulator
The Simscape simulation with PID control is shown in Fig. 7. The response displays a significant overshoot of
29.05%, despite the PID controller's successful elimination of steady-state error. This is caused by the
compressibility of air and valve dynamics that Simscape captures but that are not included in the idealized
Simulink model. Although this result shows students that PID controls are effective, they need to be carefully
adjusted in pneumatic applications to avoid instability. The modified PID achieves decreased overshoot and
increased stability.
Fig. 7. Result Open-Loop System
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The various PID structures (P, PI, PD, and PID) are contrasted in Table I. Despite producing a quick rise time,
the proportional controller (P) had residual steady-state inaccuracy. The PI controller lengthened the settling
time while eliminating steady-state error. Although it created a large overrun, the PD controller enhanced
transient response. The only controller that achieved balanced performance with 0% steady-state error and good
settling characteristics was the complete PID controller. This helps students understand the trade-offs that come
with controller design.
Table 1 COMPARISON BETWEEN EACH PID
Controller Rise Time(s) Settling
Time(s)
Overshoot (%) Steady-State
Error
P 0.354 2.43 30.74 0.049
PI 0.336 3.34 26.97 0
PD 0.093 0.85 39.89 0.063
PID 0.367 2.13 0 0
The state-space control response is depicted in Fig. 8. The state-space approach, in contrast to PID, achieved 0%
overshoot with faster settling. This demonstrates the benefit of using model-based techniques for pneumatic
control, especially when exact pole positioning is required. Students can see that state-space approaches are
crucial for sophisticated automation systems because they provide better performance and stability despite their
mathematical complexity.
Fig 8. Step Response for State-Space
Simulink and Simscape outputs are contrasted in Fig. 9. The difference is significant: Simscape shows about
10% overshoot because of real-world nonlinearities like friction and valve delay, whereas Simulink recommends
an ideal 0% overshoot. This highlights the importance of physics-based simulation and warns students about the
perils of depending just on idealized models. The graphic illustrates how instructional simulators can help close
the gap between realistic system behavior and simplified theory.
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
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Fig. 9. Step Response Between Simulink and Simscape
Students' comprehension of open-loop versus closed-loop control is seen in Fig. 10. The simulator greatly
improved conceptual clarity, according to the average assessment of 4.44/5. Since many students find it difficult
to visualize feedback effects while learning solely from textbooks, this result is crucial.
Figure 10. Understanding of Open-Loop vs. Closed-Loop
The efficiency of PID tuning exercises is seen in Fig. 11, which received a score of 4.51/5. Experimenting with
various gain levels and seeing the direct effects on system dynamics was beneficial to the students. This is
consistent with the ideas of active learning, which hold that experiential learning enhances retention more than
passive teaching.
Fig. 11. PID Tuning Educational Effectiveness
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025
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The total level of engagement between the simulator and conventional textbook learning is contrasted in Fig. 12.
More than 62% of participants gave the simulator a score of 4.47/5, the highest possible. This implies that
students are more successfully motivated by the platform's interactive and visual elements, which could result
in improved long-term learning outcomes.
Fig. 12. Engagement Compared to Traditional Learning Methods
CONCLUSION
By overcoming conventional obstacles in pneumatic control training, the new Virtual Pneumatic Control
Simulator effectively provides an interactive, cost-free learning environment. Deep experimentation with PID
and state-space control algorithms is made possible by the tool's integration of realistic actuator dynamics
modeling (via MATLAB/Simulink and Simscape) with an easy-to-use online interface for real-time parameter
adjustment and viewing. The extremely positive feedback from 45 engineering students, who indicated notable
gains in comprehension of fundamental concepts like system stability and overshoot compensation (4.51/5
rating), validates these skills. The simulator's usability rating of 4.51/5 attests to its capacity to convert theoretical
concepts into hands-on learning, democratizing industrial-grade pneumatic education free from hardware
limitations.
For suggestion, incorporating collaborative or multi-user functionality could support remote learning and
classroom integration. Performance benchmarking on various hardware setups and network conditions would
enhance accessibility and reliability for diverse users. Additionally, validation against real pneumatic lab setups
or industrial systems could demonstrate fidelity and improve confidence in simulator accuracy.
ACKNOWLEDGMENT
This work is part of a research project entitled “Data-Driven Modelling of Double-Pendulum Overhead Crane
with Distributed-Mass Payload”, funded by the MTUN grant (MTUN/2024/UTEM-FTKEK/CRG/MS0005) led
by Associate Professor Ir. Ts. Dr. Khairuddin bin Osman. The authors would like to express their gratitude to
the Universiti Teknikal Malaysia Melaka (UTeM) and the Faculty of Electronic & Computer Technology and
Engineering, UTeM for the unwavering support throughout this project.
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