This paper addresses the spatial trajectory tracking problem for a stratospheric airship with state constraints, input saturation and unknown disturbances. First, a Laguerre-based model predictive kinematic controller (LMPC) is proposed to tackle the state constraints and generate the desired velocity signal. To reduce the complexity of online optimization, Laguerre functions are applied to decrease the number of optimization variables by approximating the predicted control sequence. Second, in the dynamic loop, a sliding mode controller (SMC) with fast power rate reaching law (FPRRL) is introduced to track the desired velocity signal. The unknown disturbances in the dynamic model of airship are estimated and compensated by reduced-order extended state observer (ESO). An anti-windup compensator is incorporated into the FPRRL-based SMC controller to deal with the input saturation. Stability analysis implies that the tracking errors converge to a small neighborhood of zero. Comparative simulations about spatial straight and curve trajectory tracking are provided to evaluate the effectiveness and robustness of the proposed control scheme.

In the application scenarios of quadrotors, it is expected that only part of the obstacles can be identified and located in advance. In order to make quadrotors fly safely in this situation, we present a deep reinforcement learning-based framework to realize autonomous navigation in semi-known environments. Specifically, the proposed framework utilizes the dueling double deep recurrent Q-learning, which can implement global path planning with the obstacle map as input. Moreover, the proposed framework combined with contrastive learning-based feature extraction can conduct real-time autonomous obstacle avoidance with monocular vision effectively. The experimental results demonstrate that our framework exhibits remarkable performance for both global path planning and autonomous obstacle avoidance.

Stratospheric airships have significant potential for remote sensing and communication, but their complex spatiotemporal environment poses significant path planning challenges. Our approach models the environment as the Cartesian product of diverse meteorological field time series and airship states, establishing a framework that accommodates more complex constraints in path planning. Additionally, by considering the impact of cold cloud fields on airship pressure differentials, our method enhances structural stability and safety by guiding the airship away from cold cloud regions. The method integrates the Soft Actor-Critic algorithm with the Long Short-Term Memory network, refining the model's exploration capabilities and its ability to capture temporal dependencies in sequential data. Comparative analyses indicate that the proposed model consistently outperforms alternative models across all evaluated metrics.