Optimization-based Integration of ANFIS and PID Controllers for Networked Controlled Systems

Problem Definition

Through the analysis of the existing literature on time domain optimal tuning of Fuzzy PID controllers in Networked Control System applications, it is evident that the main challenges lie in stochastically varying network delays and packet dropouts. These issues can significantly impact the performance of feedback control mechanisms within the network communication control system. While fuzzy adaptive PID controllers have been employed to address these challenges, their limitations in handling undefined cases have been identified as a major drawback. The existing work has focused on adjusting PID parameters online, but there is a need for further optimization to improve accuracy and reduce learning time. Previous studies have explored different optimization algorithms, but there is still a gap in developing a more effective method to enhance the overall performance of the control loop in networked control systems.

Objective

The objective of the proposed project is to enhance the control performance of Networked Control System (NCS) applications by introducing a novel approach using an ANFIS-PID controller. This controller aims to address the limitations of traditional fuzzy PID controllers in handling stochastically varying network delays and packet dropouts. By combining fuzzy logic and neural networks, the ANFIS system provides more accurate control in both defined and undefined cases. To further optimize the ANFIS-PID controller, the Gray Wolf Optimization (GWO) algorithm will be employed to fine-tune the system and improve accuracy and stability. The goal is to develop a controller that can make intelligent decisions in various scenarios, overcoming the drawbacks of previous approaches and offering a more efficient, accurate, and stable control system for NCS applications.

Proposed Work

As illustrated in the problem definition, the existing work on fuzzy PID controllers for Networked Control System applications has shown limitations in handling stochastically varying network delays and packet dropouts. To address this gap, the objective of this proposed project is to introduce a novel approach using an ANFIS-PID controller for NCS systems. The ANFIS system combines the advantages of fuzzy logic and neural networks to provide more accurate control in both defined and undefined cases compared to traditional fuzzy systems. However, to further optimize the performance of the ANFIS-PID controller, the Gray Wolf Optimization algorithm will be employed. This algorithm will help in improving the accuracy and stability of the system by fine-tuning the ANFIS controller.

The proposed work aims to enhance the control performance of NCS systems by replacing the traditional fuzzy PID controller with an ANFIS-PID controller optimized using the GWO algorithm. By leveraging the capabilities of ANFIS and the optimization power of GWO, the proposed controller can effectively handle network delays and packet dropouts, making intelligent decisions in various scenarios. The GWO algorithm, inspired by the hunting behavior of gray wolves, provides a systematic approach to fine-tune the ANFIS controller for optimal performance. The proposed GWO tuned ANFIS-PID controller is expected to overcome the limitations of the previous fuzzy PID controllers and offer a more efficient, accurate, and stable control system for NCS applications.

Application Area for Industry

This project can be applied in various industrial sectors where Networked Control Systems (NCS) are utilized, such as manufacturing plants, robotics, automation systems, and process industries. The proposed GWO tuned ANFIS-PID Controller can address specific challenges faced by these industries, such as stochastically varying network delays and packet dropouts. By replacing the traditional fuzzy system with the more accurate ANFIS system, the controller can make efficient decisions in both defined and undefined cases, making the system more intelligent and adaptive. Additionally, the optimization using GWO can enhance the system's stability, speed, and efficiency, leading to improved overall performance in industrial applications. This innovative solution can provide significant benefits in terms of optimized control, reduced learning time, and enhanced accuracy for NCS applications, ultimately improving productivity and reliability in industrial processes.

Application Area for Academics

The proposed project on using GWO-ANFIS-PID controller for Networked Control Systems can significantly enrich academic research, education, and training in the field of control systems and optimization. By addressing the limitations of traditional fuzzy controllers and introducing advanced neuro-fuzzy systems along with optimization algorithms, the project offers a novel approach for improving control accuracy and stability in NCS applications. Researchers in the field of control systems, specifically those working on networked control systems, can benefit from the code and literature of this project to explore innovative research methods for optimizing controller performance in the presence of network delays and packet dropouts. MTech students and PHD scholars can use this project to develop advanced control strategies for real-time applications, enhancing their understanding of complex control systems and optimization techniques. The relevance of this project lies in its potential to revolutionize the way NCS are designed and implemented, by combining the strengths of ANFIS, PID controllers, and GWO optimization.

The project's applications in simulation experiments and data analysis can offer valuable insights into the performance of control systems under varying network conditions, paving the way for more robust and efficient control strategies. In the future, the project can be extended to explore other optimization algorithms or hybrid control techniques for NCS applications, further enhancing the adaptability and intelligence of control systems in dynamic environments. The research findings from this project can contribute significantly to the advancement of control theory and its practical applications in various industry domains.

Algorithms Used

The project utilizes the GWO-ANFIS, PID, and Fuzzy system algorithms to address the issues of traditional control mechanisms in a Neuro Control System (NCS). The Fuzzy system is replaced with the more advanced Artificial Neuro Inference Fuzzy System (ANFIS) to improve accuracy in both defined and undefined cases. ANFIS combines fuzzy logic and neural networks to provide more precise results. The Gray Wolf Optimization (GWO) algorithm is employed to optimize the ANFIS system, enhancing its accuracy and efficiency. GWO utilizes swarm intelligence based on the hunting behavior of gray wolves to optimize the system.

The proposed GWO tuned ANFIS-PID Controller offers improved decision-making capabilities, making the NCS more intelligent, faster, and stable. This combination of algorithms contributes to achieving the project's objectives by overcoming the limitations of traditional control mechanisms and improving the overall performance of the NCS.

Keywords

network impacts, control systems, distributed environments, networked control systems, network latency, network delays, network reliability, performance optimization, networked control architecture, communication protocols, real-time systems, feedback control, network congestion, network synchronization, control system design, Fuzzy PID controllers, stochastically varying delays, packet dropouts, Networked Control System applications, loop feedback control system, network communication control system, fuzzy adaptive PID controller, time delay optimization, optimization algorithms, Artificial Neuro Inference Fuzzy System, ANFIS, neural networks, gray wolf optimization, GWO method, swarm intelligence, optimization techniques, GWO tuned ANFIS-PID Controller, intelligent decision making, system optimization, efficient control mechanisms.

SEO Tags

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