Improved Proximal Policy Optimization Algorithm for Controller Design in Hybrid UAVs*

Drones have become an indispensable tool in our daily lives. While fixed-wing and rotary-wing drones are common types, each comes with its own set of advantages and disadvantages. Hybrid drones, however, combine the strengths of both types, enabling vertical takeoff and landing, hovering, and remote flight. Nonetheless, the aerodynamics of hybrid drones are exceedingly intricate, leading to a sluggish pace in their development. In this article, we propose a neural network controller design, employing the reinforcement learning Proximal Policy Optimization (PPO) algorithm to train the controller. Additionally, we integrate an attention mechanism into the network's input section to emphasize the speed variable, thereby enhancing the data processing and improving the model performance and efficiency. Experimental results demonstrate that our approach yields a controller with superior stability and optimal value.