无人机轨迹跟踪使用比例整合衍生型-2模糊逻辑控制器与基因算法参数调整
Oumaïma Moali1, Dhafer Mezghani1, Abdelkader Mami1
1UR-LAPER, Faculty of Sciences of Tunis, University of Tunis El Manar, Tunis 2092, Tunisia.
Sensors (Basel, Switzerland)
|October 26, 2024
概括
本研究介绍了一种强大的2型迷糊逻辑控制器 (FLC),用于无人机 (UAV) 四旋翼飞机,在强风条件下性能优于传统方法. 优化的控制器提高了稳定性和轨迹跟踪,尽管不确定性.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 人工智能的人工智能
- 航空航天工程 航空航天工程
背景情况:
- 无人机 (UAV) 四旋翼是非线性系统,由于参数不确定性,建模错误和风力干扰,在户外控制具有挑战性.
- 现有的控制策略经常在动态,不可预测的环境中扎于稳定性,适应性和精确的轨迹跟踪.
- 需要先进的控制范式来解决这些局限性,并确保可靠的无人机操作.
研究的目的:
- 为在户外环境中导航的UAV四旋翼飞行器开发和验证一个强大的和最佳的控制策略,特别是解决风和系统不确定性带来的挑战.
- 为了比较拟议的Type-2模糊逻辑控制器 (FLC) 与传统控制器 (如Backstepping和Type-1 FLC) 的性能.
- 使用遗传算法 (GA) 优化Type-2 FLC,以提高强度和性能.
主要方法:
- 设计了一种与比例整合导数 (PID) 控制器集成的Type-2模糊逻辑控制器 (FLC),不需要先前了解无人机或噪音模型.
- 使用基因算法 (GA) 来优化PID-2型FLC的参数.
- 拟议的控制器经过严格的验证,并与在现实世界户外场景中与风暴条件下的Backstepping和PID-Type-1 FLC控制器进行了比较.
主要成果:
- 最佳的PID-Type-2 FLC与Backstepping和PID-Type-1 FLC控制器相比显示出更高的稳定性和性能.
- 定量分析显示了显著的改进:后退控制器的效率降低了12%,PID-Type-1 FLC的效率比拟的PID-Type-2 FLC低了51%.
- 深度稳定性分析证实了控制器对参数不确定性,建模错误和执行器故障的有效性.
结论:
- 建议的最佳PID-Type-2 FLC提供了一个非常强大的解决方案,用于在挑战性的户外和风环境中控制UAV四旋翼.
- 这种方法有效地减轻了由系统不确定性和外部干扰引起的性能下降.
- 这些发现强调了2型模糊逻辑控制器的显著优势,通过遗传算法进行优化,用于先进的无人机轨迹跟踪应用.
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