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In this paper, a new concept of formation learning control is introduced to the field of formation control of multiple autonomous underwater vehicles (AUVs), which specifies a joint objective of distributed formation tracking control and learning/identification of nonlinear uncertain AUV dynamics. A novel two-layer distributed formation learning control scheme is proposed, which consists of an upper-layer distributed adaptive observer and a lower-layer decentralized deterministic learning controller. This new formation learning control scheme advances existing techniques in three important ways: 1) the multi-AUV system under consideration has heterogeneous nonlinear uncertain dynamics; 2) the formation learning control protocol can be designed and implemented by each local AUV agent in a fully distributed fashion without using any global information; and 3) in addition to the formation control performance, the distributed control protocol is also capable of accurately identifying the AUVs' heterogeneous nonlinear uncertain dynamics and utilizing experiences to improve formation control performance. Extensive simulations have been conducted to demonstrate the effectiveness of the proposed results.

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This paper deals with the hybrid almost output regulation problem for a class of linear systems with average dwelltime impulses. The proposed hybrid output regulator is constructed as a linear impulsive system that undergoes synchronous impulses with the controlled plant. Lyapunov-based sufficient conditions of the output regulability and weighted L 2 performance for the linear impulsive systems are first derived. Based on the analysis results, the hybrid synthesis problem is formulated in terms of linear matrix equations plus a set of linear matrix inequalities (LMIs). With this hybrid synthesis scheme, both flow and jump dynamics of the hybrid regulator can be jointly designed by solving a convex optimization problem in minimizing the weighted L 2 gain from the perturbation signal to the error output. A numerical example is used to demonstrate the proposed approach.