Abstract
In this paper, a trajectory tracking control system, which consists of a model predictive control unit and an active safety steering control unit, has been developed. A nonlinear bicycle vehicle model, including the longitudinal, lateral, yaw, and quasi-static roll motions, was derived as a predictive model to simulate and test the proposed model predictive control (MPC) system. A 4-DOF vehicle model was used to reflect the characteristics of vehicle dynamics to avoid rollover accidents of automobiles. Simulation was performed and experiment results demonstrated good performance of both MPC unit and active safety steering control unit. Finally, it was proved that the proposed trajectory tracking control system is easy to realize with low cost.
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References
Sun, Y., Xiong, G.M., Chen, H.Y.: Evaluation of the intelligent behaviors of unmanned ground vehicles based on fuzzy-EAHP scheme. J. Autom. Eng. 36, 22–27 (2014)
Sai, S., Altintas, O., Kenney, J., et al.: Current and future ITS. IEICE Trans. Inf. Syst. 38(2), 176–183 (2013)
Bell, M.G.H., Kaparias, I., Nocera, S., et al.: Presence of urban ITS architectures in Europe: results of a recent survey. Ingegneria Ferroviaria 67.5, 447–467 (2012)
Zuo, Z., Wang, C.: Adaptive trajectory tracking control of output constrained multi-rotors systems. Control Theory Appl. Iet 8.13, 1163–1174 (2014)
Xu, R., Özgüner, Ü: Brief paper: sliding mode control of a class of underactuated systems. Automatica 44.1, 233–241 (2008)
Leitner, J., Calise, A., Prasad, J.V.R.: Analysis of adaptive neural networks for helicopter flight control. J. Guid. Control. Dyn. 68.2, 251–261 (2012)
Schoellig, A.P., Mueller, F.L., D’Andrea, R.: Optimization-based iterative learning for precise quadrocopter trajectory tracking. Auton. Robot. 33, 103–127 (2012)
Graichen, K., Kugi, A.: Stability and incremental improvement of suboptimal MPC without terminal constraints. IEEE Trans. Autom. Control 55, 2576–2580 (2010)
Liu, J., Jayakumar, P., Overholt, J.L., et al.: The role of model fidelity in model predictive control based hazard avoidance in unmanned ground vehicles using LIDAR sensors. Dynamic Systems and Control Conference, pp. V003T46A005 (2013)
Falcone, P., Borrelli, F., Asgari, J., et al.: Predictive active steering control for autonomous vehicle systems. IEEE Trans. Control Syst. Technol. 15, 566–580 (2007)
Yakub, F., Lee, S., Mori, Y.: Comparative study of MPC and LQC with disturbance rejection control for heavy vehicle rollover prevention in an inclement environment. J. Mech. Sci. Technol. 30, 3835–3845 (2016)
Deets, D., Szalai, K.: Design and flight experience with a digital fly-by-wire control system using Apollo guidance system hardware on an F-8 aircraft. Aiaa Journal (1972)
Janbakhsh, A.A., Kazemi, R.: A new approach for simultaneous vehicle handling and path tracking improvement through SBW system. J. Cell Sci. 114, 3137–45 (2010)
Auguet, T., Sebe, M.: Vehicle steering control without mechanical connection between the steering wheel and the steered wheels. US US8036793 (2011)
Cetin, A.E., Adli, M.A., Barkana, D.E., et al.: Implementation and development of an adaptive steering-control system. IEEE Trans. Veh. Technol. 59, 75–83 (2010)
Wang, H., Liu, L., He, P., et al.: Robust adaptive position control of automotive electronic throttle valve using PID-type sliding mode technique. Nonlinear Dyn. 85, 1331–1344 (2016)
Li, L., Lu, Y., Wang, R., et al.: A 3-dimentional dynamics control framework of vehicle lateral stability and rollover prevention via active braking with MPC. IEEE Trans. Ind. Electron. 99, 1–12 (2016)
Palmieri, G., Falcone, P., Tseng, H.E., et al.: A preliminary study on the effects of roll dynamics in predictive vehicle stability control 16, 5354–5359 (2009)
Liao, C., Wu, X., Huang, H.: LMI-based sliding mode anti-rollover control algorithm of vehicle active suspension. Sensors Transd., 1726–5479 (2014)
Solmaz, S., Corless, M., Shorten, R.: A methodology for the design of robust rollover prevention controllers for automotive vehicles with active steering. In: IEEE Conference on Decision and Control, 2006, pp. 1739–1744. IEEE (2007)
Prestonthomas, J., Woodrooffe, J.: A Feasibility Study of a Rollover Warning Device for Heavy Trucks. Transport Canada Publication, Canada (1990)
Hyun, D., Langari, R.: Modeling to predict rollover threat of tractor-semitrailers. Veh. Syst. Dyn. 39(6), 401–414 (2003)
Kong, X.: Research of rollover warning system for heavy vehicles based on hidden Markov Model. Hebei University of Engineering (2013)
Xiaoguo, L., Wang, Z., Qian, F., et al.: Necessary conditions and application of establishing automial regression model. Math. Pract. Theory 38(16), 109–115 (2008)
Liu, J., Wang, S., He, G.G., et al.: On-line prediction system of vehicle attitude angle based on auto-regressive model. Comput. Eng. 37(13), 202–204 (2011)
Lu, S., Chon, K.H.: Nonlinear autoregressive and nonlinear autoregressive moving average model parameter estimation by minimizing hypersurface distance. IEEE Trans. Signal Process. 51(51), 3020–3026 (2003)
Muller, B., Deutscher, J., Grodde, S.: Continuous curvature trajectory design and feedforward control for parking a car. IEEE Trans. Control Syst. Technol. 15(3), 541–553 (2007)
Treacy, P.J., Jones, K., Mansfield, C.: Flipped out of control: single-vehicle rollover accidents in the Northern Territory. Med. J. Aust. 176(6), 260–263 (2002)
Piyabongkarn, D., Yuan, Q., Lew, J.Y.: Method of identifying predictive lateral load transfer ratio for vehicle rollover prevention and warning systems: WO US7873454 (2011)
Akaike, H.: Akaike’s information criterion. International Encyclopedia of Statistical Science, 25 (2011)
Yamaoka, K., Nakagawa, T., Uno, T.: Application of Akaike’s information criterion (AIC) in the evaluation of linear pharmacokinetic equations. J. Pharmacokinet. Biopharma. 6(2), 165 (1978)
Singh, B., Reddy, A.H.N., Murthy, S.S.: Hybrid fuzzy logic proportional plus conventional integral-derivative controller for permanent magnet brushless DC motor. In: IEEE International Conference on Industrial Technology, vol. 1, pp. 185–191. IEEE (2000)
Boada, B.L., Boada, M.J.L., DãAz, V.: Fuzzy-logic applied to yaw moment control for vehicle stability. Veh. Syst. Dyn. 43(10), 753–770 (2005)
Zhicheng, J., Yanxia, S., Jianguo, J.: A novel fuzzy PI intelligent control method of BLDCM speed servo system. Electric Mach. Control 7(3), 248–254 (2003)
Kumar, V., Rana, K.P.S., Mishra, P.: Robust speed control of hybrid electric vehicle using fractional order fuzzy PD and PI controllers in cascade control loop. J. Franklin Inst. 353(8), 1713–1741 (2016)
Guo, W., Wang, G., Yu, Q., et al.: Study on active steering control of vehicle based on adaptive fuzzy PI control. Agricultural Equipment & Vehicle Engineering (2015)
Mamdani, E.H., Gaines, B.R.: Mamdani Gaines: Fuzzy Reasoning and its Applications. Academic Press (1981)
NovAtel: Data Sheet. SPAN-CPT, February (2014)
Melexis, N.V.: Data Sheet MLX90316 (2007)
Zhou, H.S.: Steering-by-wire control strategy research based on BLDCM. Grad Thesis, Jiangsu University PRC (2014)
Liu, J., Zhou, H.S., Jia, L.X.: Steering-by-wire control strategy research for rollover warning. Mach. Des. Manuf. 6, 143–145 (2014)
Changfu, Z., Guo, K.: Objective evaluation index for handling and stability of vehicle. Nat. Sci. J. Jilin Univ. Technol. 30(1), 1–6 (2000)
Lu, X., Zhuoping, Y., Wei, J., et al.: Research on vehicle stability control of 4WD electric vehicle based on longitudinal force cintrol allocation. Nat. Sci. J. Tongji Univ. (Nat. Sci.) 38(3), 417–421 (2010)
Falcone, B.P., Tseng, H.E., Borrelli, F., et al.: MPC-based yaw and lateral stabilization via active front steering and braking. Vehicle System Dynamics (2010)
Zanten, A.T.V., Erhardt, R., Landesfeind, K., et al.: VDC systems development and perspective. Vacuum 28(12), 429 (1998)
Zanten, A.T.V., Erhardt, R., Bartels, H., et al.: Simulation for the development of the Bosch-VDC. In: Proceedings of the institute of natural sciences Nihon University, pp. 363–366 (1996)
Zhisheng, Y.: Automotive Theory. 5th edn. Machinery Industry Press (2009)
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The author(s) disclose receipt of the following financial support for the research, authorship, and/or publication of this article: This work was financially supported by The National Natural Science Fund (No. U1564201 and No. U51675235) and The Research Innovation Program for College Graduates of Jiangsu Province (No.4061120007).
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Cai, J., Jiang, H., Chen, L. et al. Implementation and Development of a Trajectory Tracking Control System for Intelligent Vehicle. J Intell Robot Syst 94, 251–264 (2019). https://doi.org/10.1007/s10846-018-0834-4
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DOI: https://doi.org/10.1007/s10846-018-0834-4