Prescribed Performance Event-Triggered Trajectory Tracking Control for Stratospheric Airship

This study presents a novel trajectory tracking controller for a stratospheric airship, focusing on event-triggered control and prescribed performance. The proposed controller, based on the framework of prescribed performance backstepping method, combines a second-order derivative filter and a radial basis function neural network. The controller design incorporates an event-triggered strategy to achieve prescribed performance objectives. The derivative filter addresses the computational complexity associated with the virtual control law, while the radial basis function neural network estimates unknown terms. Through Lyapunov analysis, the stability and non-Zeno behavior of the system are established. Simulation results confirm the effectiveness of the designed controller in achieving the desired trajectory tracking objectives.