Event-triggered compound learning tracking control of nonstrict-feedback nonlinear systems in sensor-to-controller channel

This paper investigates the event-triggered tracking control of nonstrict-feedback nonlinear system with time-varying disturbances. While fuzzy logic systems (FLSs) approximate unknown dynamics, an compound learning algorithm is originally developed to accurately estimate total uncertainties. By referring adaptive model, laws are derived without provoking problem “algebraic loop,” seeing Remark 3. The command filters employed generate continuous substitutes for both virtual and their derivatives, so as solve recently proposed “jumps laws” arising in backstepping-based (ETC). triggering condition constructed guarantee similarity between model original system. Estimation optimal weights disturbances follows from update laws. satisfactory performance achieved, scheme can semi-globally uniformly ultimate boundedness (SGUUB) all errors. Finally, a numerical experiment verifies effectiveness scheme.