Approximation-free control based on the bioinspired reference model for suspension systems with uncertainty and unknown nonlinearity

Uncertainty and unknown nonlinearity are often inevitable in the suspension systems, which were solved using fuzzy logic system or neural networks. However, these methods restricted by structural complexity of controller huge computing cost. Meanwhile, estimation error such approximators is affected adopted adaptive laws learning gains. Thus, view above problem, this paper proposes approximation-free control based on bioinspired reference model for a class uncertain systems with nonlinearity. The proposed method integrates superior vibration suppression advantage prescribed performance function control. Then, improved, calculation burden relieved, transient analyzed theoretically to provide guidelines parameter setting. Finally, simulation results validate approach, comparisons show advantages terms good suppression, fast convergence, less burden.