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.

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Design of a reduced-order non-linear observer for vehicle velocities estimation

References

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