stability control
Recently Published Documents





2022 ◽  
Vol 120 ◽  
pp. 105000
Fabrício Leonardo Silva ◽  
Ludmila C.A. Silva ◽  
Jony J. Eckert ◽  
Rodrigo Y. Yamashita ◽  
Maria A.M. Lourenço

Chenyu Zhou ◽  
Liangyao Yu ◽  
Yong Li ◽  
Jian Song

Accurate estimation of sideslip angle is essential for vehicle stability control. For commercial vehicles, the estimation of sideslip angle is challenging due to severe load transfer and tire nonlinearity. This paper presents a robust sideslip angle observer of commercial vehicles based on identification of tire cornering stiffness. Since tire cornering stiffness of commercial vehicles is greatly affected by tire force and road adhesion coefficient, it cannot be treated as a constant. To estimate the cornering stiffness in real time, the neural network model constructed by Levenberg-Marquardt backpropagation (LMBP) algorithm is employed. LMBP is a fast convergent supervised learning algorithm, which combines the steepest descent method and gauss-newton method, and is widely used in system parameter estimation. LMBP does not rely on the mathematical model of the actual system when building the neural network. Therefore, when the mathematical model is difficult to establish, LMBP can play a very good role. Considering the complexity of tire modeling, this study adopted LMBP algorithm to estimate tire cornering stiffness, which have simplified the tire model and improved the estimation accuracy. Combined with neural network, A time-varying Kalman filter (TVKF) is designed to observe the sideslip angle of commercial vehicles. To validate the feasibility of the proposed estimation algorithm, multiple driving maneuvers under different road surface friction have been carried out. The test results show that the proposed method has better accuracy than the existing algorithm, and it’s robust over a wide range of driving conditions.

2022 ◽  
Vol 2022 ◽  
pp. 1-15
Chao Zhang ◽  
Peisi Zhong ◽  
Mei Liu ◽  
Qingjun Song ◽  
Zhongyuan Liang ◽  

The K-Nearest Neighbor (KNN) algorithm is a classical machine learning algorithm. Most KNN algorithms are based on a single metric and do not further distinguish between repeated values in the range of K values, which can lead to a reduced classification effect and thus affect the accuracy of fault diagnosis. In this paper, a hybrid metric-based KNN algorithm is proposed to calculate a composite metric containing distance and direction information between test samples, which improves the discriminability of the samples. In the experiments, the hybrid metric KNN (HM-KNN) algorithm proposed in this paper is compared and validated with a variety of KNN algorithms based on a single distance metric on six data sets, and an HM-KNN application method is given for the forward gait stability control of a bipedal robot, where the abnormal motion is considered as a fault, and the distribution of zero moment points when the abnormal motion is generated is compared. The experimental results show that the algorithm has good data differentiation and generalization ability for different data sets, and it is feasible to apply it to the walking stability control of bipedal robots based on deep neural network control.

Yunxu Tong ◽  
Guihua Li

Aiming at the problems of poor control effect and poor stability of the mixed pulse system with the traditional method, this paper introduces the M-matrix to establish the pulse delay differential indefinite formula and realize stability control of the mixed pulse system. The synchronization problem of mixed-pulse systems in complex networks is analyzed using M matrix. The local coupling strength of the impulsive system is controlled according to the adaptive method. A class of Multi-Lyapunov functions is constructed for stability control of hybrid impulsive systems. The proposed method is proved to have better control effect through experiments.

Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 344
Anika Weber ◽  
Julian Werth ◽  
Gaspar Epro ◽  
Daniel Friemert ◽  
Ulrich Hartmann ◽  

Use of head-mounted displays (HMDs) and hand-held displays (HHDs) may affect the effectiveness of stability control mechanisms and impair resistance to falls. This study aimed to examine whether the ability to control stability during locomotion is diminished while using HMDs and HHDs. Fourteen healthy adults (21–46 years) were assessed under single-task (no display) and dual-task (spatial 2-n-back presented on the HMD or the HHD) conditions while performing various locomotor tasks. An optical motion capture system and two force plates were used to assess locomotor stability using an inverted pendulum model. For perturbed standing, 57% of the participants were not able to maintain stability by counter-rotation actions when using either display, compared to the single-task condition. Furthermore, around 80% of participants (dual-task) compared to 50% (single-task) showed a negative margin of stability (i.e., an unstable body configuration) during recovery for perturbed walking due to a diminished ability to increase their base of support effectively. However, no evidence was found for HMDs or HHDs affecting stability during unperturbed locomotion. In conclusion, additional cognitive resources required for dual-tasking, using either display, are suggested to result in delayed response execution for perturbed standing and walking, consequently diminishing participants’ ability to use stability control mechanisms effectively and increasing the risk of falls.

Sign in / Sign up

Export Citation Format

Share Document