基于分布式观测器的多自主水下机器人确定学习控制

针对多自主水下机器人的一致性跟踪问题,提出一种基于新型分布式观测器的一致性跟踪策略.对于具有未知非线性动态的引导者,首先利用确定学习理论将引导者的未知动态表示为具有常数权值的径向基函数神经网络;然后,设计一种新型的分布式观测器,并证明其观测误差能够指数收敛到零的小邻域内;接着,利用观测到的引导者状态信息,通过反步法和动态面技术为每个跟随者设计分布式跟踪控制器,通过Lyapunov稳定性分析,证明闭环系统中所有信号都是最终一致有界的,且跟随者的跟踪误差能够收敛到原点的小邻域内;最后,通过仿真验证所提出方案的有效性.

Deterministic learning control of multiple autonomous underwater vehicles based on a distributed observer

This paper focuses on the problem of the consensus tracking control for multiple autonomous underwater vehicles(AUVs) based on a novel distributed observer. To achieve a good estimate performance of a distributed observer without knowing the leader''s dynamics, firstly, using the deterministic learning theory, the uncertain nonlinearity of the leader is described by constant radial basis function(RBF) neural networks(NNs). Based on the constant RBF NNs, a novel deterministic learning-based distributed observer is proposed for multiple AUVs, and the observer error is proven to exponentially converge to a small neighborhood of the origin. By means of the observed leader''s output, a distributed tracking control scheme is proposed by backstepping and dynamic surface techniques. Lyapunov stability analysis is used to prove that all the signals in the closed-loop system are bounded and the consensus tracking errors converge to a small neighborhood of the origin. Finally, a simulation example is implemented to illustrate the effectiveness of the proposed scheme.