Distributed Leader Follower Formation Control of Mobile Robots based on Bioinspired Neural Dynamics and Adaptive Sliding Innovation Filter
This work addresses formation control for mobile robots, offering incremental improvements in robustness and smoothness for applications like autonomous navigation.
The paper tackled the distributed leader-follower formation control problem for mobile robots by proposing a hybrid control method combining bioinspired neural dynamics, backstepping, and sliding mode control, which resolved speed jump and chattering issues while providing smooth, robust control inputs. It integrated an adaptive sliding innovation filter for accurate state estimation under noise and uncertainties, with simulations demonstrating its effectiveness.
This paper investigated the distributed leader follower formation control problem for multiple differentially driven mobile robots. A distributed estimator is first introduced and it only requires the state information from each follower itself and its neighbors. Then, we propose a bioinspired neural dynamic based backstepping and sliding mode control hybrid formation control method with proof of its stability. The proposed control strategy resolves the impractical speed jump issue that exists in the conventional backstepping design. Additionally, considering the system and measurement noises, the proposed control strategy not only removes the chattering issue existing in the conventional sliding mode control but also provides smooth control input with extra robustness. After that, an adaptive sliding innovation filter is integrated with the proposed control to provide accurate state estimates that are robust to modeling uncertainties. Finally, we performed multiple simulations to demonstrate the efficiency and effectiveness of the proposed formation control strategy.