Advantages of Fuzzy Control While Dealing with Complex/ Unknown Model Dynamics: A Quadcopter Example
Advantages of Fuzzy Control While Dealing with Complex/ Unknown Model Dynamics: A Quadcopter Example
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模糊控制在处理复杂/未知模型动力学时的优势：四轴飞行器示例
DOI：
10.5772/62530
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
C. Webb
中科院分区：
文献类型：
--
作者：
Luis Ibarra;C. Webb
通常，复杂且不确定的对象无法通过众所周知的线性方法来处理。大多数情况下，需要复杂的控制器来实现预期的稳定性和鲁棒性；然而，它们通常缺乏简单的设计方法，且实际实现困难（如果不是不可能的话）。模糊逻辑控制是一种智能技术，在此基础上，它允许将逻辑语句转换为非线性映射。尽管它已被证明能有效处理复杂对象，但许多近期研究已经偏离了语言可解释性这一基本前提。在这项工作中，以一种清晰的方式设计了一个简单的模糊控制器，注重设计的简便性和语言算子的逻辑一致性。将其与一个具有附加执行器可变性的四轴飞行器非线性模型一起进行模拟，从而也证明了控制器的鲁棒运行。在四轴飞行器的电机之间施加不均匀的增益、带宽和时滞变化，因此模拟结果包含了在现实中可能出现的那些特性。由于这些变化可能与执行器的性能有关，因此可以根据电力电子驱动器或电机等数学模型中通常不包含的特性进行分析。这些考虑可能会缩小模糊控制器模拟与实际实现之间的差距。简而言之，本章介绍了一个简单的模糊控制器，它将四轴飞行器对象作为智能控制的第一种方法。
Commonly, complex and uncertain plants cannot be faced through well-known linear approaches. Most of the time, complex controllers are needed to attain expected stability and robustness; however, they usually lack a simple design methodology and their actual implementation is difficult (if not impossible). Fuzzy logic control is an intelligent technique which, on its basis, allows the translation from logic statements to a nonlinear mapping. Although it has been proven to effectively deal with complex plants, many recent studies have moved away from the basic premise of linguistic interpretability. In this work, a simple fuzzy controller is designed in a clear way, privileging design easiness and logical consistency of linguistic operators. It is simulated together to a nonlinear model of a quadcopter with added actuators variability, so the robust operation of the controller is also proven. Uneven gain, bandwidth, and time-delay variations are applied among quadcopter’s motors, so the simulations results enclose those characteristics which could be found in reality. As those variations can be related to actuators’ performance, an analysis can be driven in terms of the features which are not commonly included in mathematical models like power electronics drives or electric machinery. These considerations may shorten the gap between simulation and actual implementation of the fuzzy controller. Briefly, this chapter presents a simple fuzzy controller which deals with a quadcopter plant as a first approach to intelligent control.