Grey FNN control and robustness design for practical nonlinear systems

• Published in: Maǧallaẗ al-abḥath al-handasiyyaẗ Vol. 11; no. 1 A; pp. 108 - 125
• Main Authors: Chen, Zhiyong, Chen, Timothy, Meng, Yahui, Wang, Ruei-yuan
• Format: Journal Article
• Language: English
• Published: Kuwait Kuwait University, Academic Publication Council 01-03-2023
• ISSN: 2307-1877; 2307-1885
• DOI: 10.36909/jer.11273

To ensure asymptomatic stability and improve vehicle ride comfort, this paper develops a fuzzy neural network (FNN) based on the evolved bat algorithm (EBA) to design adaptive backstepping controllers with gray signal predicators. A recoil method is used to evaluate the nonlinearity of the controlled systems and to derive the control law which is evolved for the tracking of the signals. A group of grey differential equations are applied for the grey model (GM) (n, h), which is an active model where h is the number of considered variables and n is the order of the grey differential equations. In the article, the Discrete GM (2.1) is used to obtain the advanced motion of the nonlinear system, so that the command controller can prove the Lyapunov stability and feasibility of the entire scheme through the Lyapunov-like lemma. The controller design criteria are demonstrated for mechanical elastic wheels (MEW) to establish a viable mathematical framework for the new wheels.