Switching periodic event-triggered global prescribed performance control of uncertain strict-feedback systems with sensor faults

This study investigates the global adaptive prescribed performance control (PPC) of a class of uncertain strict-feedback nonlinear systems with sensor faults based on the switching periodic event-triggering mechanism (SPETM). Due to the existence of sensor faults, the controlled system is first remodeled by utilizing the available variables. To reduce the communication frequency, a novel SPETM is proposed by combining the advantages of the static and the dynamic event-triggering mechanisms. This mechanism can not only avoid continuously monitoring the event-triggering condition and avoid the Zeno phenomenon in mechanism, but also reduce the trigger frequency while ensuring the system performance by adjusting the event-triggering threshold dynamically. Meanwhile, a time-varying scaling function, whose reciprocal is considered as a prescribed performance function, is designed to achieve the global PPC by combining the nonlinear transformation technique. The adaptive control algorithm design is completed by employing the backstepping methodology, which can guarantee that all closed-loop signals are bounded and the actual system output signal evolves within the prescribed performance boundary for arbitrary initial values. The effectiveness and the advantages of the proposed control algorithm are illustrated through an application example of the network-based robotic manipulator system.