Sampled-data-based event-triggered adaptive prescribed performance control of uncertain strict-feedback nonlinear systems

This article studies the problem of global adaptive prescribed performance control (PPC) for a class of uncertain strict-feedback nonlinear systems based on the sampled-data-based event-triggering mechanism. A novel time-varying scaling function, whose reciprocal plays the role of a prescribed performance function, is constructed and utilized to remove the range restriction on the initial tracking error and achieve the global PPC. Meanwhile, to reduce the communication frequency and avoid the Zeno behavior in mechanism, a sampled-data-based event-triggering mechanism is introduced, and an event-triggered global adaptive prescribed performance controller is designed by employing the error transformation technique and the adaptive backstepping method. It is proved that all signals of the closed-loop system are bounded and the tracking error is confined within the prescribed performance boundary for arbitrary initial values. Since there is no need to continuously monitor whether the triggering condition is satisfied, the obtained result is conducive to physical implementation. Finally, a network-based robotic manipulator system is employed to verify the effectiveness of the proposed method.