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کنترل مسیر حرکت و کنترل مقاوم بر اساس مدل دوچرخه غیرخطی جهت پایدارسازی خودرو الکتریکی موتور در چرخ در سناریو اضطراری | ||
| مکانیک هوافضا | ||
| مقاله 7، دوره 20، شماره 1 - شماره پیاپی 75، فروردین 1403، صفحه 107-122 اصل مقاله (1.15 M) | ||
| نوع مقاله: گرایش دینامیک، ارتعاشات و کنترل | ||
| نویسندگان | ||
| محمد امین قماشی1؛ رضا کاظمی* 2 | ||
| 1دانشجوی دکتری، دانشکده مهندسی مکانیک، دانشگاه صنعتی خواجه‌نصیرالدین طوسی، تهران، ایران | ||
| 2نویسنده مسئول: استاد، دانشکده مهندسی مکانیک، دانشگاه صنعتی خواجه‌نصیرالدین طوسی، تهران، ایران | ||
| تاریخ دریافت: 30 شهریور 1402، تاریخ بازنگری: 22 مهر 1402، تاریخ پذیرش: 30 آبان 1402 | ||
| چکیده | ||
| در طول فرآیند ردیابی مسیر حرکت خودرو، ملاحظات عدم‌قطعیت در مدل‌سازی خودرو شامل تغییرات پارامتر، خطای مدل‌سازی، اغتشاش خارجی که بر روی عملکرد ردیابی مسیر حرکت تأثیر بسزایی دارد، موجود می‌باشد؛ بنابراین، در این پژوهش، یک استراتژی کنترل ردیابی مسیر حرکت خودرو الکتریکی موتور در چرخ که دارای قابلیت بالایی می‌باشد، پیشنهاد می‌گردد. در ابتدا، نسبت به استفاده از یک مدل دینامیکی با دو درجه آزادی جهت ایجاد مدل خطا ردیابی مسیر حرکت و سپس تبدیل به مسئله ردیابی زاویه چرخشی خودرو حول محور یاو اقدام می‌گردد؛ بنابراین، میزان زاویه فرمان به‌عنوان ورودی کنترلر، با کنترل ردیابی زاویه چرخشی خودرو حول محور یاو حاصل می‌گردد. جهت تخمین و جبران عدم‌قطعیت‌های مرتبط با سیستم، در این پژوهش نسبت به طراحی مشاهده‌گر حالت غیرخطی اقدام می‌گردد. همچنین الگوریتم کنترلر جهت تشخیص ردیابی زاویه چرخشی خودرو حول محور یاو مورد طراحی واقع می‌گردد. در مرحله بعد، جهت پایدارسازی خودرو، نسبت به طراحی یک الگوریتم کنترل مود لغزان جهت دستیابی به گشتاور زاویه چرخشی مطلوب خودرو حول محور یاو اقدام می‌گردد. سپس با طراحی توزیع‌کننده گشتاور بهینه، نیروی بهینه تایرها تخصیص داده می‌شود. درنهایت، با استفاده از نرم‌افزارهای سیمولینک متلب/کارسیم نسبت به شبیه‌سازی اقدام می‌گردد. نتایج حاصل از شبیه‌سازی‌های انجام‌شده، کارایی و قابلیت‌های بالا الگوریتم کنترل پیشنهادی را در شرایط اضطراری و باوجود اغتشاش‌های خارجی به نمایش می‌گذارد. | ||
تازه های تحقیق | ||
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| کلیدواژه‌ها | ||
| شرایط اضطراری؛ موتور در چرخ؛ مود لغزان؛ سطح لغزش؛ مشاهده‌گر | ||
| عنوان مقاله [English] | ||
| Motion Trajectory Control and Robust Control Based on Nonlinear Bicycle Model to Stabilization for In-wheel Motor Electric Vehicle in Emergency Scenario | ||
| نویسندگان [English] | ||
| Mohammad Amin Ghomashi1؛ Reza Kazemi2 | ||
| 1Ph.D. Student, Faculty of Mechanical Engineering, K. N. Toosi University, Tehran, Iran | ||
| 2Corresponding author: Professor, Faculty of Mechanical Engineering, K. N. Toosi University, Tehran, Iran | ||
| چکیده [English] | ||
| During the vehicle motion trajectory tracking process, there are uncertainty considerations in vehicle modeling, including parameter changes, modeling error, and external disturbance that have a significant effect on the tracking performance. Therefore, in this research, a control strategy for motion trajectory tracking of the in-wheel motor electric vehicle, which has a high capability, is proposed. At first, a dynamic model with two degrees of freedom is used to create a trajectory tracking error model, and then it becomes the problem of tracking yaw. Therefore, the amount of the steering angle as the input of the controller is obtained by controlling yaw tracking. In order to estimate and compensate the uncertainties related to the system, in this research, the design the nonlinear mode observer is applied. And also, the controller algorithm is designed to detect yaw tracking. In the next step, in order to stabilize the in-wheel electric vehicle, a sliding mode control algorithm is designed to achieve the desired yaw torque. Then, by designing the optimal torque distributor, the optimal force of the tires is allocated. Finally, simulation is done using Simulink Matlab/Carsim software. The results of the performed simulations show the performance and high capabilities of the proposed control algorithm in emergency situations and despite external disturbances. | ||
| کلیدواژه‌ها [English] | ||
| Emergency Condition, In-wheel Motor, Sliding Mode, Sliding Surface, Observer | ||
| مراجع | ||
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[1] Hang P, Chen X. Integrated chassis control algorithm design for path tracking based on four-wheel steering and direct yaw-moment control. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering. 2019;233(6):625-41. DOI :10.1177/0959651818806075. [2] Chindamo D, Lenzo B, Gadola M. On the vehicle sideslip angle estimation: a literature review of methods, models, and innovations. Applied Sciences. 2018 Mar 1;8(3):355. DOI :10.3390/app8030355. [3] Chen T, Chen L, Xu X, Cai Y, Jiang H, Sun X. Sideslip Angle Fusion Estimation Method of an Autonomous Electric Vehicle Based on Robust Cubature Kalman Filter with Redundant Measurement Information. World Electric Vehicle Journal. 2019 May 30;10(2):34. DOI :10.3390/wevj10020034. [4] Imine H, Madani T. Sliding‐mode control for automated lane guidance of heavy vehicle. International Journal of Robust and Nonlinear Control. 2013;23(1):67-76. DOI :10.1002/rnc.1818. [5] Zhang N, Wang J, Li Z, Li S, Ding H. Multi-Agent-Based Coordinated Control of ABS and AFS for Distributed Drive Electric Vehicles. Energies. 2022;15(5):1919. DOI :10.3390/en15051919. [6] Zhai L, Hou R, Sun T, Kavuma S. Continuous steering stability control based on an energy-saving torque distribution algorithm for a four in-wheel-motor independent-drive electric vehicle. Energies. 2018;11(2):350. DOI :10.3390/en11020350. [7] Katsuyama E, Yamakado M, Abe M. A state-of-the-art review: toward a novel vehicle dynamics control concept taking the driveline of electric vehicles into account as promising control actuators. Vehicle System Dynamics. 2021;59(7):976-1025. DOI :10.1080/00423114.2021.1916048. [8] Wang RC, Wei ZD, Ye Q. A research on visual preview longitudinal and lateral cooperative control of intelligent vehicle. Automotive Engineering. 2019;41(7):763. [9] Kumarawadu S, Lee TT. Neuroadaptive combined lateral and longitudinal control of highway vehicles using RBF networks. IEEE Transactions on Intelligent Transportation Systems. 2006;7(4):500-12. DOI :10.1109/TITS.2006.883113. [10] Liang J, Lu Y, Yin G, Fang Z, Zhuang W, Ren Y, Xu L, Li Y. A distributed integrated control architecture of AFS and DYC based on MAS for distributed drive electric vehicles. IEEE transactions on vehicular technology. 2021;70(6):5565-77. DOI :10.1109/TVT.2021.3076105. [11] Li K, Bian Y, Li SE, Xu B, Wang J. Distributed model predictive control of multi-vehicle systems with switching communication topologies. Transportation Research Part C: Emerging Technologies. 2020;118:102717. DOI :10.1016/j.trc.2020.102717. [12] Peng H, Wang W, Xiang C, Li L, Wang X. Torque coordinated control of four in-wheel motor independent-drive vehicles with consideration of the safety and economy. IEEE Transactions on Vehicular Technology. 2019;68(10):9604-18. DOI :10.1109/TVT.2019.2935617. [13] Rahman MA, Masrur MA, Uddin MN. Impacts of interior permanent magnet machine technology for electric vehicles. In 2012 IEEE International Electric Vehicle Conference. 2012;1-5, IEEE. DOI :10.1109/IEVC.2012.6183226. [14] Hartani K, Merah A, Draou A. Stability enhancement of four-in-wheel motor-driven electric vehicles using an electric differential system. Journal of Power Electronics. 2015;15(5):1244-55. DOI :10.6113/JPE.2015.15.5.1244. [15] Hartani K, Merah A. Electric vehicle longitudinal stability control based on a new multimachine nonlinear model predictive direct torque control. Journal of Advanced Transportation. 2017. DOI :10.1155/2017/4125384. [16] Mousavinejad E, Han QL, Yang F, Zhu Y, Vlacic L. Integrated control of ground vehicles dynamics via advanced terminal sliding mode control. Vehicle System Dynamics. 2017;55(2):268-94. DOI :10.1080/00423114.2016.1256489. [17] Ahmed T, Kada H, Allali A. New DTC strategy of multi-machines single-inverter systems for electric vehicle traction applications. International Journal of Power Electronics and Drive Systems. 2020;11(2):641. DOI :10.11591/ijpeds.v11.i2.pp641-650. [18] Cabrera A, Gowal S, Martinoli A. A new collision warning system for lead vehicles in rear-end collisions. In2012 IEEE Intelligent Vehicles Symposium. 2012;674-679, IEEE. DOI :10.1109/IVS.2012.6232244. [19] Lee HK, Shin SG, Kwon DS. Design of emergency braking algorithm for pedestrian protection based on multi-sensor fusion. International Journal of Automotive Technology. 2017;18:1067-76. DOI :10.1007/s12239-017-0104-7. [20] Lopez A, Sherony R, Chien S, Li L, Qiang Y, Chen Y. Analysis of the braking behaviour in pedestrian automatic emergency braking. In 2015 IEEE 18th International Conference on Intelligent Transportation Systems. 2015; 1117-1122, IEEE. DOI :10.1109/ITSC.2015.185. [21] Wang X, Zhu M, Chen M, Tremont P. Drivers’ rear end collision avoidance behaviors under different levels of situational urgency. Transportation Research Part C: Emerging Technologies. 2016;71:419-33. DOI :10.1016/j.trc.2016.08.014. [22] Zheng Y, Li SE, Li K, Borrelli F, Hedrick JK. Distributed model predictive control for heterogeneous vehicle platoons under unidirectional topologies. IEEE Transactions on Control Systems Technology. 2016;25(3):899-910. DOI :10.1109/TCST.2016.2594588. [23] Zhou C, Liu XH, Xu FX. Intervention criterion and control strategy of active front steering system for emergency rescue vehicle. Mechanical Systems and Signal Processing. 2021;148:107160. DOI :10.1016/j.ymssp.2020.107160. [24] Mernone AV, Mazumdar JN, Lucas SK. A mathematical study of peristaltic transport of a Casson fluid. Mathematical and Computer Modelling. 2002;35(7-8):895-912. DOI :10.1016/S0895-7177(02)00058-4. [25] Liu J, Song J, Li H, Huang H. Direct yaw-moment control of vehicles based on phase plane analysis. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering. 2022;236(10-11):2459-74. DOI :10.1177/09544070211052375. [26] Yao X, Gu X, Jiang P. Coordination control of active front steering and direct yaw moment control based on stability judgment for AVs stability enhancement. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering. 2022;236(1):59-74. DOI :10.1177/09544070211018104. [27] Wang L, Zhu S, Liu Y, Du X, Zhu Z, Zhai Z. A novel path tracking method of tractor based on improved second-order sliding mode considering front wheel steering angle compensation. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering. 2023;237(9):2205-16. DOI :10.1177/09544070221108315. [28] Akermi K, Chouraqui S, Boudaa B. Novel SMC control design for path following of autonomous vehicles with uncertainties and mismatched disturbances. International Journal of Dynamics and Control. 2020;8(1):254-68. DOI :10.1007/s40435-018-0478-z. [29] Pacejka H. Tire and vehicle dynamics. Elsevier; 2005. [30] Zhai L, Wang C, Hou Y, Hou R, Ming Mok Y, Zhang X. Two-level optimal torque distribution for handling stability control of a four hub-motor independent-drive electric vehicle under various adhesion conditions. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering. 2023;237(2-3):544-59. DOI :10.1177/09544070221075508. [31] Jing C, Shu H, Shu R, Song Y. Integrated control of electric vehicles based on active front steering and model predictive control. Control Engineering Practice. 2022;121:105066. DOI :10.1016/j.conengprac.2022.105066. [32] Lin F, Qian C, Cai Y, Zhao Y, Wang S, Zang L. Integrated tire slip energy dissipation and lateral stability control of distributed drive electric vehicle with mechanical elastic wheel. Journal of the Franklin Institute. 2022;359(10):4776-803. DOI :10.1016/j.jfranklin.2022.04.024. [33] Ding S, Sun J. Direct yaw-moment control for 4WID electric vehicle via finite-time control technique. Nonlinear dynamics. 2017;88:239-54. DOI :10.1007/s11071-016-3240-0. [34] Xu B, Zhang L, Ji W. Improved non-singular fast terminal sliding mode control with disturbance observer for PMSM drives. IEEE Transactions on Transportation Electrification. 2021;7(4):2753-62. DOI :10.1109/TTE.2021.3083925. [35] Ding S, Mei K, Yu X. Adaptive second-order sliding mode control: A Lyapunov approach. IEEE Transactions on Automatic Control. 2021;67(10):5392-9. DOI :10.1109/TAC.2021.3115447.
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