ICTACT Journal on Soft Computing

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Hybrid Neuro-fuzzy-genetic Algorithms for Optimal Control of Autonomous Systems


Affiliations
1 Department of Mechanical Engineering, Theni Kammavar Sangam College of Technology, India
2 Department of Information Technology, Siddhant College of Engineering, India
3 Department of Information Technology, University College of Technology and Applied Sciences - Salalah, Oman
4 Indian Institute of Information Technology Kalyani, India
     

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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.

Keywords

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.
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Abstract Views: 113

PDF Views: 2




  • Hybrid Neuro-fuzzy-genetic Algorithms for Optimal Control of Autonomous Systems

Abstract Views: 113  |  PDF Views: 2

Authors

Veeranan Arunprasad
Department of Mechanical Engineering, Theni Kammavar Sangam College of Technology, India
Brijendra Gupta
Department of Information Technology, Siddhant College of Engineering, India
T. Karthikeyan
Department of Information Technology, University College of Technology and Applied Sciences - Salalah, Oman
Muruganantham Ponnusamy
Indian Institute of Information Technology Kalyani, India

Abstract


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.

Keywords


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.

References