Deep neural data-driven Koopman fractional control of a worm robot

This paper proposes an innovative approach for enhancing the locomotion control of a worm robot by integrating deep neural networks with Koopman operator theory and fractional order control techniques. Traditional control methods often struggle with the nonlinear dynamics and uncertainties inherent in such systems. The Koopman operator framework facilitates the representation of these dynamics in a linear and infinite-dimensional space, thereby enabling advanced control strategies. By leveraging deep neural networks, we approximate the Koopman operator efficiently, enhancing the system’s adaptability and performance. Additionally, fractional order control is introduced to provide robustness against uncertainties and disturbances, offering greater flexibility in capturing the complex dynamics of the worm robot. Extensive simulations validations demonstrate the effectiveness and efficiency of the proposed approach across various environmental conditions. Results show superior performance compared to traditional methods, affirming the viability of integrating deep neural networks with fractional order control for advanced robotic applications. This study contributes to the field by showcasing a novel methodology that not only achieves precise and robust control of worm robot locomotion but also sets a foundation for future research in adaptive and intelligent robotic systems.