Distributed Containment Control With Prescribed Accuracy for Nonstrict-Feedback Switched Nonlinear Multiagent Systems

Distributed containment control issue with prescribed accuracy is addressed in this article for nonstrict-feedback switched nonlinear multiagent systems with aperiodically time-varying parameters. First, a novel
congelation of variables
method is adopted to deal with time-varying parameters, which are fast-varying in an unknown compact set with only their radii known a priori. Further, by means of the Gaussian basis function property of the radial basis function neural network, the backstepping recursive design method can work normally for nonstrict-feedback systems. In order to handle the switched dynamics and obtain the desired prescribed performance, a common Lyapunov function is designed by incorporating a series of continuously differentiable switching functions. Then, under the proposed containment control scheme, containment control is achieved, and meanwhile, each containment error converges with a prescribed accuracy, which is given before the containment controller is implemented. Finally, two simulations are performed to validate the theoretical results.