一种基于通用自进化神经模糊控制器的高性能六旋翼无人机高度控制系统
A Generic Self-Evolving Neuro-Fuzzy Controller based High-performance Hexacopter Altitude Control System
AI总结：
本文提出一种无需领域知识的通用自进化神经模糊控制器，用于六旋翼无人机的高度控制，通过自适应规则调整和滑模控制理论保证系统稳定性。
摘要：
如今，全自主系统如旋翼无人机（UAV）的应用迅速增加。由于非线性动态复杂，开发基于学习机器的智能自组织进化控制器成为研究热点。本文提出名为通用控制器（G-controller）的进化控制器，用于控制六旋翼无人机的高度。该控制器可与极少的领域知识一起工作。其进化架构基于先进的增量学习算法，即通用进化神经模糊推理系统（GENEFIS）。控制器不需要离线训练，因为它从空规则集开始运行，然后根据需求添加或删除规则。后件参数的适应律来源于滑模控制（SMC）理论。Lyapunov理论用于保证所提控制器的稳定性。此外，还实施了辅助鲁棒化控制项，以获得跟踪误差趋于零的均匀渐近收敛。最后，通过六旋翼无人机在各种轨迹上的高度跟踪来评估G控制器的性能。
英文摘要：
Nowadays, the application of fully autonomous system like rotary wing unmanned air vehicles (UAVs) is increasing sharply. Due to the complex nonlinear dynamics, a huge research interest is witnessed in developing learning machine based intelligent, self-organizing evolving controller for these vehicles notably to address the system's dynamic characteristics. In this work, such an evolving controller namely Generic-controller (G-controller) is proposed to control the altitude of a rotary wing UAV namely hexacopter. This controller can work with very minor expert domain knowledge. The evolving architecture of this controller is based on an advanced incremental learning algorithm namely Generic Evolving Neuro-Fuzzy Inference System (GENEFIS). The controller does not require any offline training, since it starts operating from scratch with an empty set of fuzzy rules, and then add or delete rules on demand. The adaptation laws for the consequent parameters are derived from the sliding mode control (SMC) theory. The Lyapunov theory is used to guarantee the stability of the proposed controller. In addition, an auxiliary robustifying control term is implemented to obtain a uniform asymptotic convergence of tracking error to zero. Finally, the G-controller's performance evaluation is observed through the altitude tracking of a UAV namely hexacopter for various trajectories.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
