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Uncertainty and unknown nonlinearity are often inevitable in the suspensionsystems, which were often solved using fuzzy logic system (FLS) or neuralnetworks (NNs). However, these methods are restricted by the structuralcomplexity of the controller and the huge computing cost. Meanwhile, theestimation error of such approximators is affected by adopted adaptive laws andlearning gains. Thus, in view of the above problem, this paper proposes theapproximation-free control based on the bioinspired reference model for a classof uncertain suspension systems with unknown nonlinearity. The proposed methodintegrates the superior vibration suppression of the bioinspired referencemodel and the structural advantage of the prescribed performance function (PPF)in approximation-free control. Then, the vibration suppression performance isimproved, the calculation burden is relieved, and the transient performance isimproved, which is analyzed theoretically in this paper. Finally, thesimulation results validate the approach, and the comparisons show theadvantages of the proposed control method in terms of good vibrationsuppression, fast convergence, and less calculation burden.

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