Search results

Filters

  • Journals
  • Authors
  • Keywords
  • Date
  • Type

Search results

Number of results: 3
items per page: 25 50 75
Sort by:
Download PDF Download RIS Download Bibtex

Abstract

This paper presents a novel sideslip angle estimator based on the pseudo-multi-sensor fusion method. The

kinematics-based and dynamics-based sideslip angle estimators are designed for sideslip angle estimation.

Also, considering the influence of ill-conditioned matrix and model uncertainty, a novel sideslip angle estimator

is proposed based on the wheel speed coupling relationship using a modified recursive least squares

algorithm. In order to integrate the advantages of above three sideslip angle estimators, drawing lessons

from the multisensory information fusion technology, a novel thinking of sideslip angle estimator design is

presented through information fusion of pseudo-multi-sensors. Simulations and experiments were carried

out, and effectiveness of the proposed estimation method was verified.

Go to article

Authors and Affiliations

Te Chen
Long Chen
Yingfeng Cai
Xing Xu
Download PDF Download RIS Download Bibtex

Abstract

To improve the curve driving stability and safety under critical maneuvers for four-wheel-independent drive autonomous electric vehicles, a three-stage direct yaw moment control (DYC) strategy design procedure is proposed in this work. The first stage conducts the modeling of the tire nonlinear mechanical properties, i.e. the coupling relationship between the tire longitudinal force and the tire lateral force, which is crucial for the DYC strategy design, in the STI (Systems Technologies Inc.) form based on experimental data. On this basis, a 7-DOF vehicle dynamics model is established and the direct yaw moment calculation problem of the four-wheel-independent drive autonomous electric vehicle is solved through the nonsingular fast terminal sliding mode (NFTSM) control method, thus the optimal direct yaw moment can be obtained. To achieve this direct yaw moment, an optimal allocation problem of the tire forces is further solved by using the trust-region interior-point method, which can effectively guarantee the solving efficiency of complex optimization problem like the tire driving and braking forces allocation of four wheels in this work. Finally, the effectiveness of the DYC strategy proposed for the autonomous electric vehicles is verified through the CarSim-Simulink co-simulation results.
Go to article

Bibliography

  1.  H. Wang, K. Xu, Y. Cai, and L. Chen, “Trajectory planning for lane changing of intelligent vehicles under multiple operating conditions”, J. Jiangsu Univ. Nat. Sci. 40(3), 255‒260 (2019).
  2.  Y. Li, B. Zhang, and X. Xu, “Robust control for permanent magnet in-wheel motor in electric vehicles using adaptive fuzzy neural network with inverse system decoupling”, Trans. Can. Soc. Mech. Eng. 42(3), 286‒297 (2018).
  3.  Y. Li, H. Deng, X. Xu, and W. Wang, “Modelling and testing of in-wheel motor drive intelligent electric vehicles based on co-simulation with Carsim/Simulink”, IET. Intell. Transp. Syst. 13(1), 115‒123 (2019).
  4.  D. Zhang, G. Liu, H. Zhou, and W. Zhao, “Adaptive sliding mode fault-tolerant coordination control for four-wheel independently driven electric vehicles”, IEEE. Trans. Ind. Electron. 65(11), 9090‒9100 (2018).
  5.  T. Chen, X. Xu, L. Chen, H. Jiang, Y. Cai, and Y. Li, “Estimation of longitudinal force, lateral vehicle speed and yaw rate for four-wheel independent driven electric vehicles”, Mech. Syst. Signal. Process. 101, 377‒388 (2018).
  6.  T. Chen, X. Xu, Y. Cai, H. Jiang, and X. Sun, “Reliable sideslip angle estimation of four-wheel independent drive electric vehicle by information iteration and fusion”, Math. Probl. Eng. 2018, 9075372 (2018).
  7.  H. Zhang, J. Liang, H. Jiang, Y. Cai, and X. Xu, “Stability research of distributed drive electric vehicle by adaptive direct yaw moment control”, IEEE Access. 7, 106225‒106237 (2019).
  8.  L.D. Novellis, A. Sorniotti, P. Gruber, J. Orus, J.R. Fortun, J. Theunissen and J. D. Smet, “Direct yaw moment control actuated through electric drivetrains and friction brakes: Theoretical design and experimental assessment”, Mechatronics. 26, 1‒15 (2015).
  9.  Y. Chen, J. Hedrick, and K. Guo, “A novel direct yaw moment controller for in-wheel motor electric vehicles”, Veh. Syst. Dyn. 51(6), 925‒942 (2013).
  10.  A. Goodarzi, F. Diba, and E. Esmailzadeh, “Innovative active vehicle safety using integrated stabilizer pendulum and direct yaw moment control”, J. Dyn. Syst-Trans. ASME. 136(5), DS-12-1335 (2014).
  11.  S. Ding and J. Sun, “Direct yaw-moment control for 4WID electric vehicle via finite-time control technique”, Nonlinear Dyn. 88(1), 239‒254 (2017).
  12.  S. Ding, L. Liu, and W. Zheng, “Sliding mode direct yaw-moment control design for in-wheel electric vehicles”, IEEE. Trans. Ind. Electron. 64(8), 6752‒6762 (2017).
  13.  W. Huang, P. Wong, K. Wong, C. Vong, and J. Zhao, “Adaptive neural control of vehicle yaw stability with active front steering using an improved random projection neural network”, Veh. Syst. Dyn. 59(3), 396‒414 (2021), doi: 10.1080/00423114.2019.1690152.
  14.  J. Wagner and J. Keane, “A strategy to verify chassis controller software-dynamics, hardware, and automation”, IEEE Trans. Syst. Man Cybern. Part A-Syst. Hum. 27(4), 480‒493 (1997).
  15.  M. Reiter and J. Wagner, “Automated automotive tire inflation system–effect of tire pressure on vehicle handling”, IFAC Proceedings 47(3), 638‒643 (2010).
  16.  Y. Shi, Q. Liu, and F. Yu, “Design of an adaptive FO-PID controller for an in-wheel-motor driven electric vehicle”, SAE Int. J. Commer. Veh. 10, 265‒274 (2017).
  17.  H. Guo, F. Liu, F. Xu, H. Chen, D. Cao, and Y. Ji, “Nonlinear model predictive lateral stability control of active chassis for intelligent vehicles and its FPGA implementation”, IEEE Trans. Syst. Man Cybern. Part A-Syst. Hum. 49(1), 2‒13 (2017).
  18.  Q. Meng, T. Zhao, C. Qian, Z. Sun, and P. Ge, “Integrated stability control of AFS and DYC for electric vehicle based on non-smooth control”, Int. J. Syst. Sci. 49(7), 1518‒1528 (2018).
  19.  J. Song, “Development and comparison of integrated dynamics control systems with fuzzy logic control and sliding mode control”, J. Mech. Sci. Technol. 27(6), 1853‒1861 (2013).
  20.  J. Wang and R. He, “Hydraulic anti-lock braking control strategy of a vehicle based on a modified optimal sliding mode control method”, Proc. Inst. Mech. Eng. Part D-J. Aut. 233(12), 3185‒3198 (2019).
  21.  X. Sun, Y. Cai, C. Yuan, S. Wang, and L. Chen, “Fuzzy sliding mode control for the vehicle height and leveling adjustment system of an electronic air suspension”, Chin. J. Mech. Eng. 31(25), (2018), doi. 10.1186/s10033-018-0223-8.
  22.  S. Chen, J. Wang, M. Yao, and Y. Kim, “Improved optimal sliding mode control for a non-linear vehicle active suspension system”, J. Sound. Vib. 395, 1‒25 (2017).
  23.  Z. Yang, D. Zhang, X. Sun, W. Sun, and L. Chen, “Nonsingular Fast Terminal Sliding Mode Control for a Bearingless Induction Motor”, IEEE Access. 5, 16656‒16664 (2017).
  24.  E. Mousavinejad, Q. Han, F. Yang, Y. Zhu, and L. Vlacic, “Integrated control of ground vehicles dynamics via advanced terminal sliding mode control”, Veh. Syst. Dyn. 55(2), 268‒294 (2017).
  25.  A. Asiabar and R. Kazemi, “A direct yaw moment controller for a four in-wheel motor drive electric vehicle using adaptive sliding mode control”, Proc. Inst. Mech. Eng. Part K-J. Multi-Body Dyn. 233(3), 549‒567 (2019).
  26.  J. Zhang and J. Li, “Integrated vehicle chassis control for active front steering and direct yaw moment control based on hierarchical structure”, Trans. Inst. Meas. Control. 41(9), 2428‒2440 (2019).
  27.  S. Yue and Y. Fan, “Hierarchical direct yaw-moment control system design for in-wheel motor driven electric vehicle”, Int. J. Automot. Technol. 19(4), 695‒703 (2018).
  28.  X. Chen, J. Yang, D. Zhang, and J. Liang, “Complete large margin linear discriminant analysis using mathematical programming approach”, Pattern Recogn. 46(6), 1579‒1594 (2013).
  29.  R.H. Byrd, M.E. Hribar, and J. Nocedal, “An interior point algorithm for large-scale nonlinear programming”, SIAM J. Optim. 9(4), 877‒900 (1999).
  30.  R.H. Byrd, J.C. Gilbert, and J. Nocedal, “A trust region method based on interior point techniques for nonlinear programming”, Math. Progr. 89(1), 149‒185 (2000).
  31.  K. Pan and Y. Lu, “Analysis on vehicle dynamic simulating sti tire model used in driving simulator”, Auto Eng. 2, 28‒30 (2009).
  32.  Q. Xia, L. Chen, X. Xu, Y. Cai, H. Jiang, T. Chen, and G. Pan, “Running states estimation of autonomous four-wheel independent drive electric vehicle by virtual longitudinal force sensors”, Math. Probl. Eng. 2019, 8302943 (2019), doi: 10.1155/2019/8302943.
  33.  J. Tian, J. Tong, and S. Luo, “Differential steering control of four-wheel independent-drive electric vehicles”, Energies 11(11), 2892 (2018).
  34.  T. Chen, X. Xu, Y. Li, W. Wang, and L. Chen, “Speed-dependent coordinated control of differential and assisted steering for in-wheel motor driven electric vehicles”, Proc. Inst. Mech. Eng. Part D-J. Automob. Eng. 232(9), 1206‒1220 (2018).
  35.  L. Chen, T. Chen, X. Xu, Y. Cai, H. Jiang, and X. Sun, “Multi-objective coordination control strategy of distributed drive electric vehicle by orientated tire force distribution method”, IEEE Access. 6, 69559‒69574 (2018).
  36.  P. Herman and W.Adamski, “Non-adaptive velocity tracking controller for a class of vehicles”, Bull. Pol. Acad. Sci. Tech. Sci. 65(4) 459‒468 (2017).
  37.  Y. Li, H. Wu, X. Xu, Y. Cai, and X. Sun, “Analysis on electromechanical coupling vibration characteristics of in-wheel motor in electric vehicles considering air gap eccentricity”, Bull. Pol. Acad. Sci. Tech. Sci. 67(5), 851‒862 (2019).
  38.  X. Zhang, H. He, J. Nie, and L. Chen, “Performance analysis of semi-active suspension with skyhook-inertance control”, J. Jiangsu Univ. Nat. Sci. 39(5), 497‒502 (2018).
  39.  Y.Li, B.Zhang, and X.Xu, “Decoupling control for permanent magnet in-wheel motor using internal model control based on back- propagation neural network inverse system”, Bull. Pol. Acad. Sci. Tech. Sci. 66(6), 961‒972 (2018).
  40.  S. Jiang, P. Wong, R. Guan, Y. Liang, and J. Li, “An efficient fault diagnostic method for three-phase induction motors based on incremental broad learning and non-negative matrix factorization”, IEEE Access 9, 17780‒17790 (2019).
  41.  H. Ye, G. Li, S. Ding, and H. Jiang, “Direct yaw moment control of electric vehicle based on non-smooth control technique”, J. Jiangsu Univ. Nat. Sci. 39(6), 640‒646 (2018).
  42.  H. Qiu, Z. Dong, and Z. Lei, “Simulation and experiment of integration control of ARS and DYC for electrical vehicle with four wheel independent drive”, J. Jiangsu Univ. Nat. Sci. 37(3), 268‒276 (2016).
  43.  S. Ding, L. Liu, and J. H. Park, “A novel adaptive nonsingular terminal sliding mode controller design and its application to active front steering system”, Int. J. Robust Nonlinear Control 29(12), 4250‒4269 (2019).
  44.  S. Ding and W. Zheng, “Nonsingular terminal sliding mode control of nonlinear second-order systems with input saturation”, Int. J. Robust Nonlinear Control 26(9) 1857‒1872 (2016).
  45.  H. Jiang, F. Cao, and W. Zhu, “Control method of intelligent vehicles cluster motion based on SMC”, J. Jiangsu Univ. Nat. Sci. 39(4), 385‒39 (2018).
  46.  B. Xu, G. Shi, W. Ji, F. Liu, and S. Ding, H. Zhu, “Design of an adaptive nonsingular terminal sliding model control method for a bearingless permanent magnet synchronous motor”, Trans. Inst. Meas. Control 39(12), 1821‒1828 (2017).
  47.  X. Yu, J. Yin, and S. Khoo, “Generalized Lyapunov criteria on finite-time stability of stochastic nonlinear systems”, Automatica 107,183‒189 (2019).
  48.  Y. Ma, Z. Zhang, Z. Niu, and N. Ding, “Design and verification of integrated control strategy for tractor-semitrailer AFS/DYC”, J. Jiangsu Univ. Nat. Sci. 39(5), 530‒536 (2018).
  49.  J. Wang, X. Yu, Z. Hui, and X. Hu, “Influence of running speed and lateral distance on vehicle transient aerodynamic characteristics during curve crossing”, J. Jiangsu Univ. Nat. Sci. 38(3), 249‒253 (2017).
  50.  C. Huang, L. Chen, C. Yun, H. Jiang, and Y. Chen, “Integrated Control of Lateral and Vertical Vehicle Dynamics Based on Multi-agent System”, Chin. J. Mech. Eng. 27(2), 304‒318 (2014).
  51.  W. Liu, R. Wang, C. Xie, and Q. Ye, “Investigation on adaptive preview distance path tracking control with directional error compensation”, Proc. Inst. Mech. Eng. Part I-J. Syst. Control Eng. 234(4), 484‒500 (2019), doi: 10.1177/0959651819865789.
  52.  T. Coleman and Y. Li, “A trust region and affine scaling interior point method for nonconvex minimization with linear inequality constraints”, Math. Progr. 88(1), 1‒31 (2000).
  53.  F. Leibfritz and E. Mostafa, “An interior point constrained trust region method for a special class of nonlinear semidefinite programming problems”, SIAM J. Optim. 12(4), 1048‒1074 (2002).
  54.  M. Rojas and T. Steihaug, “An interior-point trust-region-based method for large-scale non-negative regularization”, Inverse Probl. 18(5), 1291‒1307 (2002).
  55.  J. Bonnans and C. Pola, “A trust region interior point algorithm for linearly constrained optimization”, SIAM J. Optim. 7(3), 717‒731 (1997).
  56.  J. Erway and P. Gill, “A subspace minimization method for the trust-region step”, SIAM J. Optim. 20(3), 1439‒1461 (2010).
Go to article

Authors and Affiliations

Xiaoqiang Sun
1 2
Yujun Wang
1
Yingfeng Cai
1
Pak Kin Wong
3
Long Chen
2
ORCID: ORCID

  1. Automotive Engineering Research Institute, Jiangsu University, Zhenjiang Jiangsu, China
  2. State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, China
  3. Department of Electromechanical Engineering, University of Macau, Taipa, Macau
Download PDF Download RIS Download Bibtex

Abstract

Reliable estimation of longitudinal force and sideslip angle is essential for vehicle stability and active safety control. This paper presents a novel longitudinal force and sideslip angle estimation method for four-wheel independent-drive electric vehicles in which the cascaded multi-Kalman filters are applied. Also, a modified tire model is proposed to improve the accuracy and reliability of sideslip angle estimation. In the design of longitudinal force observer, considering that the longitudinal force is the unknown input of the electric driving wheel model, an expanded electric driving wheel model is presented and the longitudinal force is obtained by a strong tracking filter. Based on the longitudinal force observer, taking into consideration uncertain interferences of the vehicle dynamic model, a sideslip angle estimation method is designed using the robust Kalman filter and a novel modified tire model is proposed to correct the original tire model using the estimation results of longitudinal tire forces. Simulations and experiments were carried out, and effectiveness of the proposed estimation method was verified.

Go to article

Authors and Affiliations

Long Chen
Te Chen
Xing Xu
Yingfeng Cai
Haobin Jiang
Xiaoqiang Sun

This page uses 'cookies'. Learn more