Adaptive neural tracking control of strict-feedback nonlinear systems with event-triggered state measurement

This paper investigates the adaptive neural tracking control of the strict-feedback nonlinear systems, where the states are measured in an event-triggered manner so as to save the communication resources. As the neural networks (NNs) account for the unknown dynamics of the system, the minimum learning parameters (MLPs) are extracted from the weights of the NNs and the upper bounds of the disturbances. The estimates of the MLPs are updated in an event-triggered manner to ensure the approximation ability of the NNs and the stability of the closed-loop system. An adaptive neural model is established to substitute for the original strict-feedback system and direct the design of the backstepping-based control laws. The states of this adaptive model are reset to the measured states of the original system when the triggering condition is violated. The triggering condition is constructed in the compound form and with the adaptive threshold. The dead-zone operator is involved to avoid the accumulation of triggering instants. In this paper, we notice the problem of "jumps of virtual control laws" for the event-triggered control (ETC) in the backstepping frame, and a detailed formulaic definition is given in section 2.2. To solve this problem, the first-order filters are fabricated to provide the continuous substitutes for virtual control laws. In addition, the "complexity explosion" generated by direct differentiating of virtual control laws can be averted. Through the proposed scheme, the closed-loop system can be viewed as an impulsive dynamic system, and the semi-globally uniformly ultimate boundedness (SGUUB) of all the errors is proved. Finally, two examples validate the feasibility of the proposed control scheme.