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

TL;DR

This paper introduces an approximation-free control method for uncertain suspension systems that combines bioinspired reference models and prescribed performance functions, achieving effective vibration suppression with reduced computational complexity.

Contribution

It proposes a novel approximation-free control approach that leverages bioinspired models and prescribed performance functions, avoiding complex estimators and reducing computational load.

Findings

Enhanced vibration suppression demonstrated in simulations

Faster convergence compared to traditional methods

Reduced computational burden in control implementation

Abstract

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