%0 Electronic Article
%A Hongyan Guo
%+ State Key Laboratory of Automotive Simulation and Control, Jilin University (Campus NanLing), Renmin Street 5988, Changchun 130025, People's Republic of China
%+ Department of Control Science and Engineering, Jilin University (Campus NanLing), Renmin Street 5988, Changchun 130025, People's Republic of China
%A Hong Chen
%+ State Key Laboratory of Automotive Simulation and Control, Jilin University (Campus NanLing), Renmin Street 5988, Changchun 130025, People's Republic of China
%+ Department of Control Science and Engineering, Jilin University (Campus NanLing), Renmin Street 5988, Changchun 130025, People's Republic of China
%A Dongpu Cao
%+ Department of Engineering, Lancaster University, Lancaster LA1 4YR, UK
%A Weiwei Jin
%+ Department of Control Science and Engineering, Jilin University (Campus NanLing), Renmin Street 5988, Changchun 130025, People's Republic of China
%K vehicle velocities estimation
%K linear matrix inequalities
%K CoG variation
%K maximum tire-road friction coefficient estimation
%K computational efficiency
%K mass variation
%K field programmable gate array
%K reduced-order nonlinear observer design
%K additive disturbance inputs
%K Hongqi vehicle HQ430
%K observer gain condition
%K system on a programmable chip testing platform
%K ISS analysis
%K input-to-state stability
%K measurement error
%K vehicle dynamics
%K extended Kalman filter
%K convex optimisation
%K yaw rate
%K unified exponential tire model
%K tuning parameters
%K error dynamics system
%X 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 tireâ€“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.
%@ 1751-8644
%T Design of a reduced-order non-linear observer for vehicle velocities estimation
%B IET Control Theory & Applications
%D November 2013
%V 7
%N 17
%P 2056-2068
%I Institution of Engineering and Technology
%U https://digital-library.theiet.org/;jsessionid=7p0aj8n2b6ej0.x-iet-live-01content/journals/10.1049/iet-cta.2013.0276
%G EN