A robust real-time estimation of the dynamic normal reaction for an open-link locomotion module with an E-drive

Abstract

Real-time information on the wheel static and dynamic normal reaction on various road and terrain conditions is extremely important for agile tyre dynamics to improve vehicle mobility. This article proposes a novel approach for reliable and real-time estimation of the dynamic normal reaction of an open-link locomotion module, as an essential constituent of electric vehicles. Nonlinear normal dynamics, including stochastic behaviour of suspension and nonlinearity of damping force caused by the terrain profile and damper design is considered. Robustness performance of the proposed approach is proved based on input-to-state stability theory. As demonstrated, the proposed approach is robust against model parameter uncertainties, and can keep the estimation error within the assigned boundaries. The proposed approach is indifferent to variation of the stiffness and damping coefficients of the tyre-surface patch and thus is applicable to different terrains. The approach was implemented in a new design of a sliding mode observer. A design method of the observer gains is also presented and numerical values of the gains are determined with application to the locomotion module. The estimation accuracy of the wheel normal reaction on different terrains and the robustness against parameter uncertainties are validated. Computational results confirm the real-time performance and effectiveness of the proposed observer design.

Keywords:

• Open-link locomotion module
• terrain mobility
• wheel dynamic normal reaction
• real-time observer
• input-to-state stability analysis

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

Dr Linhui Zhao's participation in this research article was supported by the National Natural Science Foundation of China [grant numbers 61790562 and U1564213], the Heilongjiang Provincial Natural Science Foundation of China [grant number LH2019F018], and the China Scholarship Council [grant number 201706125075].