A real-world data-free initialization method for neural network controllers: A case study of the multicopter interception control

Learning-based controller design methods, such as reinforcement learning and learning-based Lyapunov control, face significant challenges when applied to complex control tasks. Reinforcement learning methods typically demand substantial interaction data during initial training, often exhibiting slow convergence, high sample complexity, and unsafe exploration. Conversely, learning-based Lyapunov control methods, while providing formal stability guarantees, generally require a pre-existing, stable controller or expert demonstrations to bootstrap the learning process, which is often unavailable for novel control problems. To address this fundamental bottleneck, this article proposes a model-based rapid initialization method for neural network controllers. The core idea is to circumvent the need for prior expert knowledge or real-world interaction data. Instead, we leverage the system model to synthesize datasets that inherently satisfy Lyapunov stability conditions. A neural network policy is then trained via supervised learning on this synthetic data, yielding an initially stable controller. This policy serves as a high-quality starting point for subsequent fine-tuning via reinforcement learning or learning-based Lyapunov control methods, significantly improving sample efficiency and safety. We validate the proposed method through a challenging case study: image-based visual servoing control for high-speed multicopter interception. Both simulations and physical experiments demonstrate that the trained policy, without any online reinforcement learning fine-tuning, enables successful interception at a final speed of 15 m/s on a real quadcopter platform.