Fraction Dynamic-Surface-Based Neuroadaptive Finite-Time Containment Control of Multiagent Systems in Nonaffine Pure-Feedback Form

In this paper, the problem of containment control of networked multiagent systems is considered with special emphasis on finite-time convergence. A distributed neural adaptive control scheme for containment is developed, which, different from the current state of the art, is able to achieve dynamic containment in finite time with sufficient accuracy despite unknown nonaffine dynamics and mismatched uncertainties. Such a finite-time feature, highly desirable in practice, is made possible by the fraction dynamic surface control design technique based on the concept of virtual fraction filter. In the proposed containment protocol, only the local information from the neighbor followers and the local position information from the neighbor leaders are required. Furthermore, since the available information utilized is local and is embedded into the control scheme through fraction power feedback, rather than direct linear or regular nonlinear feedback, the resultant control scheme is truly distributed. In addition, although mismatched uncertainties and external disturbances are involved, only one single generalized neural parameter needs to be updated in the control scheme, making its design and implementation straightforward and inexpensive. The effectiveness of the developed method is also confirmed by numerical simulation.