Hybrid GA and GWO Approach for Enhanced DC Motor Position Control with PID Controller

Problem Definition

The literature review on DC motor control techniques reveals that the merging of PID controller and Fuzzy Logic Controller (FLC) to create a Fuzzy self-tuning PID controller presented promising results in terms of response quality. However, the use of Genetic Algorithm (GA) for tuning the PID gains had its limitations. The GA-tuned PID controller showed a low overshoot percentage but was slow in action, failing to always provide the exact solution. Additionally, the complexity and challenges in tuning GA-based controllers further hindered the performance efficiency of the system. These drawbacks highlight the need to update the existing system and explore alternative optimization algorithms to determine if there are better-suited solutions that can address the shortcomings observed with GA.

By reevaluating the approach and considering other optimization techniques, it may be possible to enhance the response quality and efficiency of DC motor control systems beyond the limitations encountered with the previous methodology.

Objective

The objective of the proposed work is to enhance the performance and response quality of DC motor control systems by introducing a hybrid optimization model that combines genetic algorithm (GA) with grey wolf optimizer (GWO). This hybrid approach aims to overcome the limitations of using GA alone for tuning PID controller parameters, ultimately improving the efficiency and effectiveness of position control in DC motors. The goal is to leverage the strengths of both algorithms to achieve optimal results, including preventing local minima, high convergence speed, simplicity in implementation, and high flexibility, leading to better system performance and response time compared to the previous methodology.

Proposed Work

The proposed work aims to address the limitations of the existing system for controlling and monitoring DC motors by introducing a hybrid optimization model. By combining the genetic algorithm (GA) with the grey wolf optimizer (GWO), the new approach will leverage the advantages of both algorithms to overcome the drawbacks of GA. The hybridization of the algorithms is crucial in capturing the best features of each one, resulting in a more efficient and effective tuning of PID controller parameters for position control in DC motors. The choice of GWO for hybridization was made based on its various advantages such as preventing local minima, high convergence speed, simplicity in implementation, and high flexibility. By implementing the GWOGA approach, the proposed system is expected to deliver optimal results and outperform the previous system in terms of performance and response time.

Application Area for Industry

This project can be utilized in various industrial sectors where DC motors are used extensively, such as manufacturing, robotics, automotive, and aerospace industries. The proposed solutions offer a way to efficiently control and monitor DC motors by addressing the limitations of existing methods, such as slow action, tuning challenges, and inefficiency. By hybridizing GA with GWO algorithm, the system can achieve optimal results in terms of response time, convergence speed, and simplicity of implementation. This approach can benefit industries by providing a more reliable and accurate control system for DC motors, leading to improved performance and productivity in their operations.

Application Area for Academics

The proposed project can greatly enrich academic research, education, and training in the field of control and monitoring of DC motors. By merging the advantages of PID controller and Fuzzy Logic Controller (FLC) to create a Fuzzy self-tuning PID controller, the project offers a more refined response. By hybridizing the GA algorithm with the GWO algorithm, the limitations of GA can be overcome, leading to more efficient and optimal results. This project has the potential to revolutionize research methods in the field by introducing a dynamic system with improved performance. The hybridization of algorithms allows for the advantages of both GA and GWO to be captured, leading to a more effective approach for tuning PID controllers.

This innovative research method can open up new possibilities for exploring different optimization algorithms and their applications in various conditions. MTech students, PhD scholars, and field-specific researchers can benefit greatly from the code and literature developed in this project. They can use it as a reference for their own work, implement the proposed GWOGA approach with PID controller in their experiments, and further enhance their understanding of control systems for DC motors. The relevance of this project lies in its application in real-world scenarios where efficient control and monitoring of DC motors are crucial. It can be applied in industries, robotics, automation, and other fields where precise control mechanisms are required.

By exploring different algorithms and their hybridization, this project opens up new avenues for research and education in the field of control systems. Future scope for this project includes exploring additional optimization algorithms, conducting more extensive simulations, and testing the efficiency of the proposed approach in different practical scenarios. By continuously improving and evolving the GWOGA approach with PID controller, researchers can further enhance the performance and applicability of control systems for DC motors.

Algorithms Used

In the proposed work, the system with dynamic nature is developed by hybridizing the GA (Genetic Algorithm) with the GWO (Grey Wolf Optimizer) algorithm. This hybrid approach aims to overcome the limitations of the individual algorithms and combine their advantages to create a more efficient and effective system. The GWO algorithm is chosen for hybridization due to its advantages such as preventing local minima, high convergence speed, being derivative-free, having few parameters for simplicity of implementation, and offering high flexibility. By combining the strengths of GA and GWO, the GWOGA approach with the PID controller is expected to deliver optimal results and improve the overall performance of the system. Overall, the hybridization of GA and GWO, along with the integration of the PID controller, contributes to achieving the project's objectives by enhancing accuracy, overcoming the limitations of previous approaches, and improving the efficiency of the system.

Keywords

SEO-optimized keywords: PID Controller, DC Motor, Position Control, GWO, Grey Wolf Optimization, Optimization Algorithm, Gain Tuning, Performance Criteria, Overshoot, Settling Time, Rise Time, GA-PID Controller, ITAE, Integral Time Absolute Error, Fitness Function, Control System Tuning, Control System Optimization, DC Motor Position Control, GWO-based PID Tuning, GA-PID Control, PID Controller Gain Optimization, Control System Performance, DC Motor Control, Position Control in Motors, Optimization Algorithms in Control Systems, GWO in PID Control, GA in PID Control, Control System Comparison, Control System Effectiveness

SEO Tags

PID Controller, DC Motor, Position Control, GWO, Grey Wolf Optimization, Optimization Algorithm, Gain Tuning, Performance Criteria, Overshoot, Settling Time, Rise Time, GA-PID Controller, ITAE, Integral Time Absolute Error, Fitness Function, Control System Tuning, Control System Optimization, PID Tuning, DC Motor Position Control, GWO-based PID Tuning, GA-PID Control, PID Controller Gain Optimization, Control System Performance, Motor Control, Position Control, Optimization Algorithms, PID Control, Control System Comparison, Control System Effectiveness