Giordano Vincenzo, Naso David, Turchiano Biagio
Dipartimento di Elettrotecnica ed Elettronica, Politecnico di Bari, 70125 Bari, Italy.
IEEE Trans Syst Man Cybern B Cybern. 2006 Oct;36(5):1118-27. doi: 10.1109/tsmcb.2006.873187.
This paper proposes a hybrid approach for the design of adaptive fuzzy controllers (FCs) in which two learning algorithms with different characteristics are merged together to obtain an improved method. The approach combines a genetic algorithm (GA), devised to optimize all the configuration parameters of the FC, including the number of membership functions and rules, and a Lyapunov-based adaptation law performing a local tuning of the output singletons of the controller, and guaranteeing the stability of each new controller investigated by the GA. The effectiveness of the proposed method is confirmed using both numerical simulations on a known case study and experiments on a nonlinear hardware benchmark.
本文提出了一种用于设计自适应模糊控制器（FC）的混合方法，该方法将两种具有不同特性的学习算法合并在一起，以获得一种改进的方法。该方法结合了一种遗传算法（GA），用于优化FC的所有配置参数，包括隶属函数和规则的数量，以及一种基于李雅普诺夫的自适应律，对控制器的输出单点进行局部调整，并保证GA研究的每个新控制器的稳定性。通过对一个已知案例研究的数值模拟和对一个非线性硬件基准的实验，证实了所提方法的有效性。
