Open AccessDesign of a reduced‐order non‐linear observer for vehicle velocities estimation
Author(s) -
Guo Hongyan,
Chen Hong,
Cao Dongpu,
Jin Weiwei
Publication year - 2013
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2013.0276
Subject(s) - control theory (sociology) , observer (physics) , alpha beta filter , kalman filter , yaw , computer science , linear matrix inequality , vehicle dynamics , state observer , engineering , extended kalman filter , mathematics , nonlinear system , mathematical optimization , automotive engineering , control (management) , moving horizon estimation , physics , quantum mechanics , artificial intelligence
This study presents a novel reduced‐order non‐linear observer for vehicle velocities estimation based on vehicle dynamics and Unified Exponential tire model. Yaw rate is chosen to construct the reduced‐order observer since it can be conceived as the function of vehicle velocities. The observer is designed such that the error dynamics system is input‐to‐state stability (ISS), where model errors including mass and CoG variation, and estimation or measurement error of the maximum tire–road friction coefficient are considered as additive disturbance inputs. Then, the condition of the observer gain satisfied is obtained by the ISS analysis and the lower observer gain is obtained through the convex optimisation described by the linear matrix inequalities. The proposed observer requires fewer tuning parameters and thus indicates an easier implementation compared with the existing extended Kalman filter. Simulation results demonstrate the effectiveness of the proposed reduced‐order non‐linear observer, which is also validated through experimental data from Hongqi vehicle HQ430. Furthermore, its computational efficiency is shown based on the laboratory Field Programmable Gate Array and System on a Programmable Chip testing platform.