Event-triggered adaptive control of switched nonlinear systems with full-state constraints, input saturation, and dead zone

This paper investigates an event-triggered adaptive control problem for switched nonlinear systems subject to simultaneous full-state constraints and actuator dead zone and saturation. A control framework based on log-type barrier Lyapunov functions is developed to rigorously enforce state constraints while accommodating unknown nonlinearities and external disturbances. To compensate for model uncertainties, mode-dependent radial basis function neural networks are employed within a backstepping-based design. Input saturation and dead zone are handled through a smooth approximation technique, enabling Lyapunov-based stability analysis. To reduce computational burden, an event-triggered adaptive mechanism is introduced, under which parameter updates are executed only at discrete triggering instants while excluding Zeno behaviour. Using a multiple Lyapunov function approach under average dwell-time switching, uniform ultimate boundedness of all closed-loop signals and strict constraint satisfaction are established. The effectiveness of the proposed method is demonstrated through numerical examples and a realistic Boeing CH-47 Chinook helicopter application, where a switched nonlinear model is explicitly mapped to longitudinal flight dynamics with physically meaningful actuator limitations.