通用进化自组织神经模糊控制用于仿生无人空中车辆
Generic Evolving Self-Organizing Neuro-Fuzzy Control of Bio-inspired Unmanned Aerial Vehicles
AI总结：
本文提出通用控制器G-controller，结合滑模控制理论和GENEFIS算法，实现在线学习和自组织控制，无需离线训练，通过Lyapunov函数保证稳定性，提升对不确定性和环境扰动的鲁棒性。
摘要：
近年来，随着全自主系统需求的增加，学习机器基于智能、自组织和进化控制器的研究受到广泛关注。本文提出一种新的进化和自组织控制器，即通用控制器（G-controller）。该控制器在完全在线模式下运行，仅需极少量专家领域知识，通过结合滑模控制（SMC）理论基于学习算法和先进的增量学习机GENEFIS。控制器从空规则集开始运行，因此无需离线训练。为应对系统脆弱行为，控制器可根据需求添加或修剪规则。控制律和后件适应律从SMC算法推导，以建立稳定的闭环系统，其中通过Lyapunov函数保证控制器的稳定性。通过辅助鲁棒化控制项的引入，观察到跟踪误差趋于零的统一渐近收敛。此外，多变量高斯函数的实现帮助控制器处理非轴对齐数据，从而提高对不确定性和环境扰动的鲁棒性。最后，通过观察无人空中车辆模拟系统（即仿生扑翼微型空中车辆BIFW MAV和六旋翼无人机）在多种轨迹上的跟踪性能，评估了控制器的性能。
英文摘要：
At recent times, with the incremental demand of the fully autonomous system, a huge research interest is observed in learning machine based intelligent, self-organizing, and evolving controller. In this work, a new evolving and self-organizing controller namely Generic-controller, G-controller, is proposed. The G-controller that works in the fully online mode with very minor expert domain knowledge is developed by incorporating the sliding model control, SMC, theory based learning algorithm with an advanced incremental learning machine namely Generic Evolving Neuro-Fuzzy Inference System , GENEFIS. The controller starts operating from scratch with an empty set of fuzzy rules, and therefore, no offline training is required. To cope with the plant vulnerable behavior, the controller can add, or prune the rules on demand. Control law and adaptation laws for the consequents are derived from the SMC algorithm to establish a stable closed-loop system, where the stability of the G-controller is guaranteed using the Lyapunov function. The uniform asymptotic convergence of tracking error to zero is witnessed through the implication of an auxiliary robustifying control term. In addition, the implementation of the multivariate Gaussian function helps the controller to handle the non-axis parallel data from the plant and consequently enhances the robustness against the uncertainties and environmental perturbations. Finally, the controller performance has been evaluated by observing the tracking performance in controlling simulated plants of unmanned aerial vehicle namely bio-inspired flapping wing micro air vehicle BIFW MAV and hexacopter for a variety of 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。
