Calculation Algorithm of Tire‐Road Friction Coefficient Based on Limited‐Memory Adaptive Extended Kalman Filter

Abstract

In this paper, a limited-memory adaptive extended Kalman Filter (LM-AEKF) to estimate tire-road friction coefficient is proposed. By combining extended Kalman filter (EKF) with the limited-memory filter, this algorithm can reduce the effects of old measurement data on filtering and improve the estimation accuracy. Self-adaptive regulatory factors were introduced to weigh covariance matrix of evaluated error. Meanwhile, measured noise covariance matrix was adjusted dynamically by fuzzy inference to accurately track the breaking status of system. Therefore, problems, including large filter error and divergence caused by incorrect model, can be solved. Joint simulation was conducted for the proposed algorithm with Carsim and Matlab/Simulink. Under the different road conditions, real-vehicle road tests were conducted in various working conditions for contrast with traditional EKF results. Simulation and real-vehicle road tests show that this algorithm can enhance the filter stability, improve the estimation accuracy of algorithm, and increase algorithm robustness.

An adaptive cascade predictive control strategy for connected and automated vehicles

“…To verify the robustness of the proposed control strategy, each scenario is characterized by disturbances on some particularly impactful measurement. More in detail, a random delay in a range $$$ \left[0,40\right] $$$ms on GPS signals ($$$ {X}_{CAV},{Y}_{CAV} $$$) has been assumed as in Reference 58, and two noises as measurement errors are modeled through uniform random distributions in the following ranges: (i) $$$ \left[-0.5,0.5\right] $$$m every $$$ 100 $$$ms on the detection of the longitudinal and lateral positions; 59 (ii) $$$ \left[-0.01,0.01\right] $$$rad/s on the yaw‐rate sensor 60 . Furthermore, for every signal that appears in each scenario, two types of trends will be evaluated: (i) the nominal one, in which no delay and noise on GPS signals and yaw rate occur; (ii) perturbed conditions for the robustness test, indicated with the label noise+delay in the following figures, in which both the delay and noises described above have been considered.…”

Tire Road Friction Coefficient Estimation: Review and Research Perspectives

“…[103,104]. To reduce the influence of old measurement data on the filtering in the EKF algorithm, a limited-memory adaptive extended Kalman Filter [105] was proposed to solve the problem. Also, UKF [106] can obtain higher accuracy when dealing with nonlinear system state estimation, and it has also been used for TRFC estimation in recent years.…”

Road adhesion coefficient estimation by multi-sensors with LM-MMSOFNN algorithm

“…5 Commonly used methods include design observer 6,7 and Kalman filter. 8,9 A 6-DOF tire-road friction coefficient estimation method based on the longitudinal, lateral and vertical accelerations of each tire was proposed, then the recursive least square method is applied to estimate the tire-road friction coefficient. 10 Cheng et al 11 designed a monitoring system which can simultaneously observe tire-road friction coefficient and tire force by mode switching observer under different working conditions, such as normal running, braking and steering.…”