Robust Disturbance Estimation-Based Control: Case Study on an Airship Attitude Tracking Problem.

Abstract

This paper presents a design methodology for the automatic control system of an airship, accounting for atmospheric disturbances and parametric modeling uncertainties. The derived design model is a linear stochastic system with multiplicative noise incorporating both the airship's uncertain dynamics and the wind model. First, an H∞ state feedback control law is derived for the stochastic system to ensure robust stability and trajectory tracking performance. Subsequently, a robust Kalman filter is designed to estimate the turbulence model states based on available measurements. It is demonstrated that the optimal gain of this robust filter depends on the solution to a coupled system of specific Riccati and Lyapunov equations. Numerical results indicate improved heading-tracking performance for the considered turbulent simulation when the estimated wind-gust states are incorporated into the feedback law. Under parametric uncertainty, the robust Kalman-type filter also exhibits lower sensitivity of the heading-estimation error than the classical Kalman filter.