Self-adjustable and flexible performance-based event-triggered asymptotic tracking control of nonlinear systems with unknown control directions

This study discusses the problem of event-triggered (ET) asymptotic tracking control for parametric strict feedback nonlinear systems (SFNSs) with time-varying disturbances and unknown control directions. A unified dynamic threshold method is proposed by combining a unified function with a self-adjustable performance function. In contrast to previous research, the results of this study provide a unified framework in which global or semi-global performance can be achieved through simple parameter selection while excluding the conservativeness of the constraint thresholds owing to the artificial selection of a uniform performance function. The basic lemma based on the Nussbaum function frequently is extended to adapt to the case in which the coefficients are multiple bounded functions. By fusing a first-order differentiator, the reduplicative derivation of the virtual controller in the backstepping process is obviated. Moreover, an ET mechanism with two dynamic variables is constructed to reduce the burden of data transmission. The developed controller can guarantee the boundedness of all signals in the closed-loop system, full-state constraints performance, and asymptotic tracking control performance. Finally, the feasibility of the proposed scheme is attested by two examples.