Distributed Robust Learning based Formation Control of Mobile Robots based on Bioinspired Neural Dynamics
This addresses formation control challenges for mobile robots in real-world applications, representing an incremental improvement.
This paper tackles distributed formation control for multiple mobile robots by developing a novel approach that eliminates derivative information needs and resolves speed jump issues, with simulation studies demonstrating its effectiveness.
This paper addresses the challenges of distributed formation control in multiple mobile robots, introducing a novel approach that enhances real-world practicability. We first introduce a distributed estimator using a variable structure and cascaded design technique, eliminating the need for derivative information to improve the real time performance. Then, a kinematic tracking control method is developed utilizing a bioinspired neural dynamic-based approach aimed at providing smooth control inputs and effectively resolving the speed jump issue. Furthermore, to address the challenges for robots operating with completely unknown dynamics and disturbances, a learning-based robust dynamic controller is developed. This controller provides real time parameter estimates while maintaining its robustness against disturbances. The overall stability of the proposed method is proved with rigorous mathematical analysis. At last, multiple comprehensive simulation studies have shown the advantages and effectiveness of the proposed method.