Autonomous Systems Control Design Using Neuro Evolution

This paper explores the application of neuro-evolution techniques in the design of control systems for autonomous systems. Neuro-evolution merges neural networks with evolutionary algorithms to develop adaptive, real-time control strategies for autonomous platforms. This approach is particularly effective in dynamic and complex environments, where traditional control methods struggle to ensure optimal performance. By evolving neural network structures, neuro-evolution provides robust and flexible control solutions, with promising applications in fields such as robotics, autonomous vehicles, and drone navigation. The design of autonomous systems has rapidly advanced, fueled by the need for intelligent, adaptive control in environments that are complex and unpredictable. Traditional control systems often lack the flexibility required to handle dynamic changes, making them less effective in highly variable conditions. Neuro-evolution, which combines neural networks with evolutionary algorithms, offers a promising solution by enabling autonomous systems to develop and refine control strategies on their own. This approach leverages evolutionary principles to optimize neural network architectures, producing control systems that adapt in real-time and improve through experience. Neuro-evolution has gained significant attention for applications in robotics, self-driving vehicles, and aerospace, where autonomous, robust control is essential. This paper explores the role of neuro-evolution in enhancing control design, focusing on its advantages over conventional methods and its potential to drive further innovations in autonomous systems.