REGISTRO DOI: 10.69849/revistaft/ch10202504040814
Gabriel Alexandre De Souza
Abstract
Fuzzy logic control has gained widespread attention in industrial automation due to its ability to handle nonlinearities and uncertainties in complex production environments. Traditional control methods, such as Proportional-Integral-Derivative (PID) controllers, often struggle with dynamic and highly variable processes. In contrast, fuzzy control systems leverage human-like reasoning and linguistic rules to optimize operational efficiency, adaptability, and robustness in manufacturing. This paper explores the implementation of fuzzy control systems in industrial automation, focusing on regulating critical variables such as temperature, speed, and pressure. A comprehensive literature review highlights recent advancements in fuzzy-based control, demonstrating their superiority over conventional methods in maintaining process stability and reducing operational costs. The study also presents real-world case studies illustrating how fuzzy logic enhances precision in industrial furnaces, conveyor belt systems, and hydraulic pressure control. Additionally, the integration of fuzzy logic with artificial intelligence and machine learning is examined as a pathway to further improve automation efficiency and predictive maintenance strategies. The findings suggest that fuzzy controllers offer significant advantages, including reduced energy consumption, improved product quality, and extended equipment lifespan. However, challenges such as expert knowledge requirements and computational complexity remain areas for future development. This research emphasizes the growing potential of fuzzy control systems in optimizing industrial automation and highlights the need for continued advancements to fully harness their capabilities in diverse manufacturing environments.
.Keywords: Fuzzy logic control, Industrial automation, Nonlinear systems, Process optimization.
Industrial automation has undergone a transformative evolution over the past few decades, driven by the increasing demand for efficiency, reliability, and adaptability in production environments. The need for highly responsive control systems that can regulate key process variables such as temperature, speed, and pressure is more critical than ever. Traditional Proportional-Integral-Derivative (PID) controllers have long been the industry standard for process regulation due to their straightforward implementation and ease of use. However, these conventional controllers often struggle when faced with complex, nonlinear systems, requiring frequent recalibration and human intervention. As industries advance toward higher levels of automation, the limitations of PID controllers become more apparent, necessitating the exploration of alternative approaches that can handle uncertainty and dynamic operational conditions more effectively.
The implementation of industrial automation has become a key strategy for companies aiming to streamline their operations. Across various sectors, from manufacturing facilities to logistics centers, businesses are increasingly leveraging automation technologies to boost efficiency, lower expenses, and enhance overall productivity.
Figure 1: How automation enhances efficiency.
Source: Faster Capital, 2025.
Fuzzy logic control (FLC) has emerged as a compelling alternative, particularly in applications where system behavior is difficult to model with precise mathematical equations. By leveraging linguistic rules and inference mechanisms similar to human reasoning, fuzzy logic provides a more adaptive and resilient approach to process control. This capability allows for improved regulation of nonlinear systems, reducing process variability and increasing system robustness. Additionally, the integration of fuzzy logic with artificial intelligence and machine learning techniques has opened new possibilities for intelligent automation systems that can continuously optimize operations with minimal human oversight.
This paper investigates the role of fuzzy logic controllers in industrial automation, focusing on their ability to enhance process efficiency and stability in manufacturing settings. Through a review of recent studies and real-world applications, this research aims to highlight the advantages of fuzzy-based control strategies over traditional methodologies. Furthermore, the study examines key challenges associated with fuzzy control implementation and explores future directions for integrating fuzzy logic into next-generation industrial automation frameworks.
Several studies in recent years have investigated the effectiveness of fuzzy logic controllers in industrial automation, demonstrating their superior performance over conventional control techniques.
Shah et al. (2013) conducted research on the application of fuzzy logic in optimizing conveyor belt speed regulation in production lines. Their study demonstrated that fuzzy-based control outperforms traditional speed controllers by dynamically adjusting to varying load conditions. The researchers developed a fuzzy inference system that processed real-time sensor data, ensuring smooth transitions in conveyor speed while minimizing mechanical wear. Their work highlights how fuzzy logic contributes to prolonging equipment lifespan and reducing maintenance costs, making it an attractive solution for automated manufacturing facilities.
Adepoju et al. (2014) investigated the use of fuzzy logic in motor control applications, specifically in regulating the speed of electric motors used in automated assembly lines. Their study found that fuzzy controllers provided superior performance in managing torque variations and load fluctuations, ensuring more consistent motor speed compared to PID controllers. By optimizing motor speed regulation, fuzzy logic contributed to improved synchronization of assembly processes, minimizing production delays and increasing overall throughput.
Zermane and Kasme (2020) explored the integration of fuzzy logic with machine learning techniques to enhance process automation in manufacturing environments. Their study emphasized how fuzzy controllers significantly improved response times and stability when compared to PID controllers, particularly in scenarios involving temperature and pressure regulation. Their findings suggest that incorporating adaptive fuzzy systems leads to greater energy efficiency and reduced material waste, making industrial processes more sustainable.
Chen et al. (2021) examined the role of fuzzy control systems in maintaining precise pressure regulation in hydraulic systems used in industrial presses and injection molding machines. Their study showed that fuzzy controllers effectively stabilized pressure levels under fluctuating operational demands, leading to consistent product quality and improved system reliability. By comparing the performance of fuzzy-based controllers with conventional PID controllers, they concluded that fuzzy logic provided better adaptability in handling nonlinearities and external disturbances, thereby reducing process variability.
Meng et al. (2022) explored the application of fuzzy logic in temperature control systems for industrial furnaces. Their research focused on developing an intelligent fuzzy control system that adjusts fuel supply based on temperature fluctuations and external factors. Their experimental results indicated that fuzzy controllers reduced fuel consumption while maintaining stable temperature conditions, which is crucial for metal fabrication processes. Their findings underscore the role of fuzzy logic in enhancing operational efficiency and achieving cost savings in energy-intensive industries.
The application of fuzzy logic controllers in industrial automation has demonstrated significant advantages in managing nonlinear and uncertain process variables such as temperature, speed, and pressure. Unlike traditional PID controllers, which require precise system modeling and constant recalibration, fuzzy controllers offer a more flexible and adaptive approach that aligns with real-world industrial conditions. Recent research has highlighted the effectiveness of fuzzy logic in various industrial applications, from conveyor speed regulation to temperature stabilization and hydraulic pressure control. These findings indicate that fuzzy control systems not only improve operational efficiency but also contribute to cost reductions and enhanced equipment longevity.
Despite these advantages, challenges remain in the implementation of fuzzy logic controllers, particularly in defining optimal membership functions and inference rules. Additionally, integrating fuzzy logic with advanced computational techniques, such as artificial intelligence and machine learning, remains an area of active research. Future studies should focus on refining self-learning fuzzy control systems that can autonomously adapt to changing industrial conditions without requiring extensive human intervention. By addressing these challenges, fuzzy logic has the potential to play an increasingly pivotal role in the future of industrial automation, enabling smarter, more resilient, and energy-efficient manufacturing processes.
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