Adaptive backstepping control for uncertain nonlinear strict-feedback systems with full state triggering

This paper studies the problem of adaptive backstepping control with full state triggering for a class of parametric uncertain nonlinear systems. The use of triggering mechanism discretizes measured states, which causes the issue of nondifferentiation for virtual control signals and hinders the design of adaptive estimation mechanism for unknown parameters in the traditional backstepping design. To conquer the difficulties, a group of adaptive chainlike filters are designed to generate necessarily smooth state estimates, with adaptive parameters online adjusted by the triggering state estimation errors. It is shown that the designed event-triggered mechanism allows the proposed adaptive backstepping control scheme to achieve asymptotic convergence even in the presence of state discretization.