Disturbance Observer–Based Adaptive Dynamic Event‐Triggered Control for a Class of Nonlinear Systems With Input Saturation

ABSTRACT For the adaptive tracking control of a class of nonlinear systems with asymmetric input saturation and external disturbances, a novel dynamic event‐triggered neural network control scheme on the foundation of full‐state constraints is proposed. Firstly, by adding a compensation term into the traditional dynamic surface control framework, the filtering error ignored generally is eliminated. Secondly, a nonlinear disturbance observer is constructed to observe the compound interference united by external disturbances and approximation errors based on the approximation ability of neural network. Thirdly, an auxiliary variable is combined with a smooth function used to deal with the saturated input function to offset the influence of input saturation constraints. In addition, for ensuring that all states of the system are constrained by preset time‐varying ranges, the adaptive control method and dynamic event‐triggered condition are posed with the help of barrier Lyapunov function. Then, the tracking performance and the boundness of all signals in the closed‐loop system can be guaranteed by employing the presented adaptive control approach with dynamic event‐triggered mechanism, as well as the Zeno behavior does not appear. Finally, the validity of the designed control method can be demonstrated in simulation examples.