Multirotors are widely used in applications ranging from surveillance to precision agriculture, yet conventional designs remain limited by their under-actuation. Tilt-rotor configurations overcome this limitation by enabling full actuation. This paper investigates neural-network-based control strategies for a fully actuated tilt-rotor system with four thrust-vectoring inputs. Our work is structured in two parts. First, we deliberately present a negative result by evaluating a direct input-output control approach. In this method, multilayer perceptrons (MLPs), long short-term memory (LSTM) networks, and transformer models are trained to map system states and their desired values directly to control signals. We show that this strategy fails to stabilize the system, highlighting the inherent difficulty of applying direct input-output learning to highly unstable plants. Second, as the main contribution, we propose a neural-network-enhanced sliding mode controller (SMC). The method decomposes the system dynamics into input-independent and input-dependent components, with the former learned from a small dataset using lightweight networks, thereby reducing real-time computational demands. Moreover, the proposed method can be trained using flight logs collected from low-performance controllers, and the resulting dynamic model learned from real-world data can be used in simulation. We further compare MLP- and LSTM-based implementations under model uncertainties and external disturbances, demonstrating the robustness and effectiveness of the proposed approach; in particular, the controller with the LSTM plant dynamics predictor achieves superior performance to its MLP-based counterpart while also exhibiting lower runtime.

翻译：多旋翼飞行器广泛应用于从监视到精准农业等多个领域，然而传统设计的欠驱动特性限制了其性能。倾转旋翼构型通过实现全驱动克服了这一局限。本文研究基于神经网络的全驱动倾转旋翼系统（具有四个推力矢量输入）控制策略。我们的工作分为两部分：首先，通过评估直接输入-输出控制方法，刻意呈现一个负面结果。该方法训练多层感知机（MLP）、长短期记忆（LSTM）网络和Transformer模型，直接将系统状态及其期望值映射至控制信号。我们证明，该策略无法稳定系统，凸显了将直接输入-输出学习应用于高度不稳定对象的固有问题。其次，作为主要贡献，我们提出一种神经网络增强的滑模控制器（SMC）。该方法将系统动力学分解为输入无关分量与输入相关分量，前者通过轻量级网络从小规模数据集中学习，从而降低实时计算需求。此外，所提方法可利用低性能控制器采集的飞行日志进行训练，且从真实数据习得的动力学模型可用于仿真。我们进一步比较了基于MLP和LSTM的实现在模型不确定性与外部扰动下的表现，验证了所提方法的鲁棒性与有效性；特别地，采用LSTM植物动力学预测器的控制器在性能上优于基于MLP的对应方案，同时运行时开销更低。