Predefined-time adaptive neural output-feedback control with filtered compensation for switched systems via event-triggered communication

This study investigates the command filter-based adaptive neural predefined-time output-feedback control issue for nonlinear switched systems with arbitrary switching rule. Radial basis function neural networks (RBFNNs) are used to estimate uncertain nonlinearities, and a linear state observer is designed to estimate the unmeasurable states. Moreover, an event-triggered mechanism is utilized to alleviate the communication load. Specifically, a command filter technique is applied to tackle the computational complexity arising from the iterative differentiations of the indirect control functions. A command filter-based adaptive neural predefined-time output-feedback control strategy is formulated under the backstepping control framework, integrating the command filter control and the event-triggered mechanism. The developed control strategy guarantees that all the system signals are bounded and the tracking error converges to a little interval near origin within the predefined time. Finally, the simulation experiments reveal the validity of the devised control methodology.