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Containment Maneuvering for a Class of Uncertain Nonlinear Systems Based on Concurrent Learning

In this paper, we investigate a containment maneuvering problem for uncertain nonlinear systems in MIMO strict-feedback form. The outputs of followers are driven to converge to a convex hull spanned by multiple parameterized paths and path variables need to satisfy a given dynamic task. A containment maneuvering controller is proposed based on a modular design approach. First, an estimation module is developed based on an RBF network, and adaption laws are proposed based on a concurrent learning method. Then, a controller module is proposed based on a modified dynamic surface control method using a second-order nonlinear tracking differentiator. At last, a path update law is designed by using a distributed maneuvering error feedback. Input-to-state stability theory and cascade theory are utilized to analyze the stability of the closed-loop system. The proposed design is a distributed method and attains adaption without the persistent excitation condition.