Robust Fault Estimation of Vehicular Yaw Rate Sensor Using a Type-2 Fuzzy Approach

Abstract

In this article, a proportional integral (PI) fault observer is introduced due to its accuracy and expeditiousness. Considering the time-varying vehicle speed and other uncertain parameters in vehicle dynamics system, a type-2 fuzzy model is proposed to describe the system nonlinearity. Moreover, it could also address the problem of uncertainty in the membership function, which resulted from the parameter uncertainty. In addition, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {H}_{\infty }$</tex-math></inline-formula> technique is studied to attenuate the effect of disturbance on the estimation performance and a set of linear matrices inequalities are obtained. The particle swarm optimization (PSO) approach is then adopted to find solutions to the PI fault observer. Finally, a simulation test is carried out based on the experimental data, which are collected from an electric vehicle. The simulation results demonstrate the effectiveness of the proposed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {H}_{\infty }/PSO$</tex-math></inline-formula> method.