This paper presents a hybrid obstacle avoidance architecture that integrates Optimal Control under clearance with a Fuzzy Rule Based System (FRBS) to enable adaptive constraint handling for unmanned aircraft. Motivated by the limitations of classical optimal control under uncertainty and the need for interpretable decision making in safety critical aviation systems, we design a three stage Takagi Sugeno Kang fuzzy layer that modulates constraint radii, urgency levels, and activation decisions based on regulatory separation minima and airworthiness guidelines from FAA and EASA. These fuzzy-derived clearances are then incorporated as soft constraints into an optimal control problem solved using the FALCON toolbox and IPOPT. The framework aims to reduce unnecessary recomputations by selectively activating obstacle avoidance updates while maintaining compliance with aviation procedures. A proof of concept implementation using a simplified aircraft model demonstrates that the approach can generate optimal trajectories with computation times of 2,3 seconds per iteration in a single threaded MATLAB environment, suggesting feasibility for near real time applications. However, our experiments revealed a critical software incompatibility in the latest versions of FALCON and IPOPT, in which the Lagrangian penalty term remained identically zero, preventing proper constraint enforcement. This behavior was consistent across scenarios and indicates a solver toolbox regression rather than a modeling flaw. Future work includes validating this effect by reverting to earlier software versions, optimizing the fuzzy membership functions using evolutionary methods, and extending the system to higher fidelity aircraft models and stochastic obstacle environments.

翻译：本文提出了一种混合避障架构，该架构将许可条件下的最优控制与模糊规则基系统（FRBS）相结合，以实现无人机的自适应约束处理。受经典最优控制在不确定性下的局限性以及安全关键航空系统中可解释决策需求的驱动，我们设计了一个三阶段Takagi-Sugeno-Kang模糊层，该层根据美国联邦航空管理局（FAA）和欧洲航空安全局（EASA）的法规间隔最低标准与适航指南，动态调节约束半径、紧急程度和激活决策。这些模糊导出的许可随后作为软约束被纳入最优控制问题，并使用FALCON工具箱和IPOPT求解器进行求解。该框架旨在通过选择性激活避障更新来减少不必要的重新计算，同时保持对航空程序的合规性。基于简化飞机模型的概念验证实现表明，该方法能在单线程MATLAB环境中生成最优轨迹，每次迭代计算时间为2-3秒，证明了其在近实时应用中的可行性。然而，实验发现最新版FALCON与IPOPT存在严重的软件兼容性问题：拉格朗日惩罚项恒为零，导致约束无法正常执行。该现象在所有测试场景中均一致出现，表明这是求解器工具箱的回归问题而非建模缺陷。未来工作包括通过回退至早期软件版本验证此效应、使用进化方法优化模糊隶属函数，并将系统扩展至高保真飞机模型与随机障碍环境。