In recent years, there has been an increasing demand for efficient and robust control algorithms to optimize the performance of autonomous systems. Traditional control techniques often struggle to handle the complexity and uncertainty associated with such systems. To address these challenges, hybrid neuro-fuzzy-genetic algorithms have emerged as a promising approach. This paper presents a comprehensive review of the application of hybrid neuro-fuzzy-genetic algorithms for optimal control of autonomous systems. The proposed algorithms combine the strengths of neural networks, fuzzy logic, and genetic algorithms to achieve adaptive and optimal control in real-time scenarios. The neuro-fuzzy component provides the ability to model and handle complex and uncertain systems, while the genetic algorithm component facilitates the optimization of control parameters. The combination of these techniques enables autonomous systems to adapt and optimize their control strategies based on changing environments and objectives. The paper discusses the underlying principles of hybrid neuro-fuzzy-genetic algorithms, their advantages, and challenges. It also provides a review of the state-of-the-art research in this field, highlighting successful applications and potential future directions. Overall, the integration of neuro-fuzzy-genetic algorithms in autonomous systems holds great promise for achieving optimal control in various domains, including robotics, aerospace, and autonomous vehicles.
Veeranan Arunprasad1, Brijendra Gupta2, T. Karthikeyan3, Muruganantham Ponnusamy4
Theni Kammavar Sangam College of Technology, India1, Siddhant College of Engineering, India2, University College of Technology and Applied Sciences - Salalah, Sultanate of Oman3, Indian Institute of Information Technology Kalyani, India 4
Hybrid Algorithms, Neuro-Fuzzy-Genetic Algorithms, Optimal Control, Autonomous Systems, Neural Networks, Fuzzy Logic, Genetic Algorithms, Real-Time Control, Adaptive Control, Uncertainty, Robotics, Aerospace, Autonomous Vehicles
| January | February | March | April | May | June | July | August | September | October | November | December |
| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 13 , Issue: 4 , Pages: 3015 - 3020 )
Date of Publication :
July 2023
Page Views :
367
Full Text Views :
11
|