Fixed-Time Trajectory Tracking Control for Airships with Time-Varying Error Constraints

This study investigates the trajectory tracking control problem for airships with error constraints, considering uncertainties in model parameters and external wind disturbances. A fixed-time control strategy based on the radial basis function (RBF) neural network is proposed. Initially, the kinematic and dynamic models of the airship, along with the wind field model, are established, and the trajectory tracking error model is derived. Subsequently, a novel prescribed performance function, termed an error constraint function, is designed. Integrating a barrier Lyapunov function with a power integrator ensures that the tracking error remains within predefined constraints, and the airship converges to the desired trajectory in finite time, regardless of initial conditions. Furthermore, the RBF neural network is employed to estimate and compensate for parameter uncertainties and disturbances, enhancing system robustness. Finally, the fixed-time stability of the closed-loop system is proven using Lyapunov theory, ensuring that the tracking error converges to a small neighborhood around zero within a fixed time while consistently satisfying constraint conditions. Simulation results demonstrate that the proposed control scheme enables effective trajectory tracking of the airship without relying on precise model and disturbance knowledge, achieving high control accuracy and rapid error convergence.