Advanced Transportation Equipment

Precise Compound Control of Loading Force for Electric Load Simulator of Electric Power Steering Test Bench

  • Changhua Dai ,
  • Guoying Chen ,
  • Changfu Zong ,
  • Buyang Zhang
展开
  • 1. College of Automotive Engineering, Jilin University, Changchun, 130022, China;
    2. State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, 130022, China

收稿日期: 2020-06-08

  修回日期: 2021-04-23

  网络出版日期: 2022-06-30

基金资助

Supported by National Natural Science Foundation of China (Grant No. 51505178) and China Postdoctoral Science Foundation (Grant No. 2014M561289)

Precise Compound Control of Loading Force for Electric Load Simulator of Electric Power Steering Test Bench

  • Changhua Dai ,
  • Guoying Chen ,
  • Changfu Zong ,
  • Buyang Zhang
Expand
  • 1. College of Automotive Engineering, Jilin University, Changchun, 130022, China;
    2. State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, 130022, China

Received date: 2020-06-08

  Revised date: 2021-04-23

  Online published: 2022-06-30

Supported by

Supported by National Natural Science Foundation of China (Grant No. 51505178) and China Postdoctoral Science Foundation (Grant No. 2014M561289)

摘要

Electric load simulator (ELS) systems are employed for electric power steering (EPS) test benches to load rack force by precise control. Precise ELS control is strongly influenced by nonlinear factors. When the steering motor rapidly rotates, extra force is directly superimposed on the original static loading error, which becomes one of the main sources of the final error. It is key to achieve ELS precise loading control for the entire EPS test bench. Therefore, a three-part compound control algorithm is proposed to improve the loading accuracy. First, a fuzzy proportional–integral plus feedforward controller with force feedback is presented. Second, a friction compensation algorithm is established to reduce the influence of friction. Then, the relationships between each quantity and the extra force are analyzed when the steering motor rapidly rotates, and a net torque feedforward compensation algorithm is proposed to eliminate the extra force. The compound control algorithm was verified through simulations and experiments. The results show that the tracking performance of the compound control algorithm satisfies the demands of engineering practice, and the extra force in the ELS system can be suppressed by the net torque corresponding to the actuator's acceleration.

本文引用格式

Changhua Dai , Guoying Chen , Changfu Zong , Buyang Zhang . Precise Compound Control of Loading Force for Electric Load Simulator of Electric Power Steering Test Bench[J]. Chinese Journal of Mechanical Engineering, 2022 , 35(1) : 8 -8 . DOI: 10.1186/s10033-021-00670-4

Abstract

Electric load simulator (ELS) systems are employed for electric power steering (EPS) test benches to load rack force by precise control. Precise ELS control is strongly influenced by nonlinear factors. When the steering motor rapidly rotates, extra force is directly superimposed on the original static loading error, which becomes one of the main sources of the final error. It is key to achieve ELS precise loading control for the entire EPS test bench. Therefore, a three-part compound control algorithm is proposed to improve the loading accuracy. First, a fuzzy proportional–integral plus feedforward controller with force feedback is presented. Second, a friction compensation algorithm is established to reduce the influence of friction. Then, the relationships between each quantity and the extra force are analyzed when the steering motor rapidly rotates, and a net torque feedforward compensation algorithm is proposed to eliminate the extra force. The compound control algorithm was verified through simulations and experiments. The results show that the tracking performance of the compound control algorithm satisfies the demands of engineering practice, and the extra force in the ELS system can be suppressed by the net torque corresponding to the actuator's acceleration.

参考文献

[1] H O Xiang. Development and verification of hardware-in-loop test bench for electrically controlled steering system. Changchun:Jilin University, 2014.
[2] C Wang, Y L Hou, R Z Liu, et al. The identification of electric load simulator for gun control systems based on variable-structure WNN with adaptive differential evolution. Applied Soft Computing, 2016, 38:164-175.
[3] G Y Chen, L He, C F Zong, et al. A Research on the resistance loading strategy for steering test bench based on motor serco system. Automotive Engineering, 2018, 40(2):226-233.
[4] L S Wang, M Y Wang, B Guo. Analysis and design of a speed controller for electric load simulators. IEEE Transactions on Industrial Electronics, 2016, 63(12):7413-7422.
[5] L S Wang, M Y Wang, B Guo, et al. A loading control strategy for electric load simulators based on proportional resonant control. IEEE Transactions on Industrial Electronics, 2018, 56(6):4608-4618.
[6] X J Wang, S P Wang, X D Wang. Electrical load simulator based on velocity-loop compensation and improved fuzzy-PID. IEEE International Symposium on Industrial Electronics, Seoul, South Korea, 5-8 July, 2009:238-243.
[7] N Ullah, S P Wang, J Aslam. Adaptive robust control of electrical load simulator based on fuzzy logic compensation. Proceedings of 2011 International Conference on Fluid Power and Mechatronics, Beijing, China, 17-20 Aug., 2011.
[8] N Ullah, S P Wang. Torque controller design for electrical load simulator with estimation and compensation of parametric uncertainty. Proceedings of 2012 9th International Bhurban Conference on Applied Sciences & Technology (IBCAST), Islamabad, Pakistan, 9-12 Jan., 2012:15-21.
[9] X J Wang, S P Wang, B Yao. Adaptive robust torque control of electric load simulator with strong coupling disturbance. International Journal of Control, Automation and Systems, April 2013, 11(2):325-332.
[10] X J Wang, S P Wang. High performance torque controller design for electric load simulator. Industrial Electronics & Applications, 2011:2499-2505.
[11] X J Wang, S P Wang, P Zhao. Adaptive fuzzy torque control of passive torque servo systems based on small gain theorem and input-to-state stability. Chinese Journal of Aeronautics, 2012, 25(6):906-916.
[12] L Wang, L F Qian, Q Guo. Torque control of servo load simulator with generalized dynamic fuzzy neural network based on grey prediction. Applied Mechanics and Materials, December 2011, 148-149:707-712.
[13] B Guo, M Y Wang, J Zhang. A dynamic fuzzy neural networks controller for dynamic load simulator. International Conference on Machine Learning and Cybernetics, Dalian, China, 13-16 Aug., 2006.
[14] B Yang, H T Han, R Bao. An intelligent CMAC-PD torque with anti-over-learning scheme for electric load simulator. Transactions of the Institute of Measurement & Control, Feb. 2016, 38(2):192-200.
[15] B Yang, R Bao, H T Han. Robust hybrid control based on PD and novel CMAC with improved architecture and learning scheme for electric load simulator. IEEE Transactions on Industrial Electronics, 2014, 61(10):5271-5279.
[16] N Ullah, S P Wang. Higher order error dynamics based backstepping controller design for electrical load simulator. Proceedings of 2013 10th International Bhurban Conference on Applied Sciences & Technology (IBCAST), Islamabad, Pakistan, 15-19 Jan. 2013.
[17] S L Han, K S Lee. Robust friction state observer and recurrent fuzzy neural network design for dynamic friction compensation with backstepping control. Mechatronics, 2010, 20(3):384-401.
[18] I B Tijani, R Akmeliawati. Support vector regression-based friction modeling and compensation in motion control system. Engineering Application of Artificial Intelligence, August 2012, 25(5):1043-1052.
[19] R Li. Research on control system of motor-driven load simulator. Taiyuan:North University of China, 2013.
[20] R Ghimire, C Zhang, K Pattipati. A rough set theory-based fault diagnosis method for an electric power steering system. IEEE/ASME Transactions on Mechatronics, 2018, 23(5):2042-2053.
[21] D Blana, R Kirsch, E K Chadwick. Combined feedforward and feedback control of a redundant, nonlinear, dynamic musculoskeletal system. Medical & Biological Engineering & Computing, 2009, 47(5):533-542.
[22] X Zhe, X W Min, W Yang, et al. Design of direct yaw moment control system to enhance vehicle stability based on fuzzy logic. Advanced Materials Research, 2012, 383-390:1326-1332.
[23] R Fruqon, Y J Chen, M Tanaka. An SOS-based control Lyapunov function design for polynomial fuzzy control of nonlinear systems. IEEE Transactions on Fuzzy Systems, 2017, 25(4):775-787.
[24] C H Lu, J Zhang. Design and simulation of a fuzzy-PID composite parameters' controller with MATLAB. 2010 International Conference on Computer Design and Applications, Qinhuangdao, China, 25-27 June, 2010.
[25] H J Lin, Z S Teng, T Chen, et al. Improved fuzzy control method for temperature in water tank of intelligent viscometer. 2008 International Conference on Information and Automation, Changsha, China, 20-23 June, 2008.
[26] L Lu, B Yao, Q F Wang, et al. Adaptive robust control of linear motor systems with dynamic friction compensation using modified LuGre model. 2008 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Xian, China, 2-5 July 2008.
[27] D Y Hou. Integrated direct/indirect adaptive robust control of turntable servo system based on LuGre model friction compensation. 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), Wuhan, China, 11-13 Nov. 2016.
文章导航

/