Initial conditions for airship simulator.

The robotic airship can be used as an aerostatic platform for many potential applications, for example, communication, hovering payload deliveries, data-gathering for research studies, etc. These applications require a fully autonomous perspective of an airship. One of the important aspects of airship autonomy is trajectory tracking control. An airship has complex and uncertain nonlinear dynamics which pose a major challenge for designing a precise trajectory tracking control. This paper addresses the airship trajectory tracking control problem under model uncertainties and wind disturbance. We propose a lumped model uncertainties and wind disturbance estimation approach based on an unscented Kalman filter. The estimated lumped uncertainty is used by the Sliding Mode Controller (SMC) for ultimate control of airship trajectory tracking. This comprehensive algorithm, Unscented Kalman filter-based Sliding Mode Controller (USMC), is used as a robust adaptive control solution to track the desired trajectory. The stability and convergence of the proposed method are investigated using the Lyapunov stability analysis. Simulation results show that the proposed method efficiently tracks the desired trajectory. The method solves the stability, convergence, and chattering problem of SMC without the bound constraint of model uncertainties and wind disturbance.

该机器人飞艇可作为浮空平台应用于诸多潜在场景，例如通信、悬停式载荷投递、科研数据采集等。此类应用要求飞艇具备全自主运行能力。飞艇自主运行的关键环节之一为轨迹跟踪控制。飞艇具有复杂且不确定的非线性动力学特性，这为高精度轨迹跟踪控制器的设计带来了极大挑战。本文针对模型不确定性与风扰动下的飞艇轨迹跟踪控制问题展开研究，提出了一种基于无迹卡尔曼滤波器(Unscented Kalman Filter, UKF)的集总模型不确定性与风扰动估计方法。所估计的集总不确定性将用于滑模控制器(Sliding Mode Controller, SMC)，以实现飞艇轨迹跟踪的精准控制。本文所提出的综合算法——基于无迹卡尔曼滤波器的滑模控制器(USMC)——被用作鲁棒自适应控制方案，用于跟踪期望轨迹。本文通过李雅普诺夫稳定性分析对所提方法的稳定性与收敛性进行了分析验证。仿真结果表明，所提方法可高效跟踪期望轨迹，且无需预先设定模型不确定性与风扰动的边界约束，即可解决滑模控制器存在的稳定性、收敛性与抖振问题。