Grey FNN control and robustness design for practical nonlinear systems
To ensure the asymptotic stability and improve the ride comfort of vehicles, this paper develops a fuzzy neural network (NN) based evolved bat algorithm (EBA) to design adaptive backstepping controllers with grey signal predictors. The Lyapunov theory and backstepping method were employed to evaluate the nonlinearity of controlled systems and derive the evolved control law for the signal tracking. The discrete grey model DGM (2,1) is utilized to obtain future movement 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 criterion is demonstrated in the range of mechanical elastic wheel (MEW) and lays a feasible mathematical framework for the matching of new wheels.