Dynamic Event-Triggered Optimized Backstepping Control for Strict-Feedback Nonlinear Systems With Communication Constraints

In this work, a dynamic event-triggered optimized backstepping control approach based on neural networks is proposed to address communication limitations in strict-feedback nonlinear systems. Neural networks are employed to approximate the unknown system uncertainties, with their parameters updated via a gradient descent algorithm. To enhance the communication efficiency of both control inputs and neural network parameters, a dynamic event-triggering scheme is devised. This mechanism adaptively modifies its threshold parameters in real time, depending on the system’s tracking performance. Additionally, a disturbance observer is incorporated to mitigate the influence of external perturbations. By formulating a barrier-type performance index for subsystem optimization and integrating observer–actor–critic structures, both virtual and actual optimal controllers are derived within an inversion-based control framework. Lyapunov theory is utilized to demonstrate that all signals in the closed-loop system are uniformly ultimately bounded. Finally, the effectiveness and practicality of the control strategy are verified by numerical simulations and an application case of an electromechanical system.