In this study, we strive to combine the advantages of fuzzy logic control (FLC), genetic algorithms (GA), H∞ tracking control schemes, smooth control and adaptive laws to design an adaptive fuzzy sliding model controller for the rapid and efficient stabilization of complex and nonlinear systems. First, we utilize a reference model and a fuzzy model (both involving FLC rules) to describe and well-approximate an uncertain, nonlinear plant. The FLC rules and the consequent parameter are decided on via GA. A boundary-layer function is introduced into these updated laws to cover modeling errors and to guarantee that the state errors converge into a specified error bound. After this, a H∞ tracking problem is characterized. We solve an eigenvalue problem (EVP), and derive a modified adaptive neural network controller (MANNC) to simultaneously stabilize and control the system and achieve H∞ control performance. Furthermore, a stability criterion is derived utilizing Lyapunov’s direct method to ensure the stability of the nonlinear system. Finally, the control methodology is demonstrated via a numerical simulation.
Chen, Po-Chen; Chen, Cheng-Wu; Chiang, Wei-Ling; and Yeh, Ken
"A NOVEL STABILITY CONDITION AND ITS APPLICATION TO GA-BASED FUZZY CONTROL FOR NONLINEAR SYSTEMS WITH UNCERTAINTY,"
Journal of Marine Science and Technology: Vol. 17:
4, Article 6.
Available at: https://jmstt.ntou.edu.tw/journal/vol17/iss4/6