Dynamic friction modelling and parameter identification for electromagnetic valve actuator

Abstract

A new modified LuGre friction model is presented for electromagnetic valve actuator system. The modification to the traditional LuGre friction model is made by adding an acceleration-dependent part and a nonlinear continuous switch function. The proposed new friction model solves the implementation problems with the traditional LuGre model at high speeds. An improved artificial fish swarm algorithm (IAFSA) method which combines the chaotic search and Gauss mutation operator into traditional artificial fish swarm algorithm is used to identify the parameters in the proposed modified LuGre friction model. The steady state response experiments and dynamic friction experiments are implemented to validate the effectiveness of IAFSA algorithm. The comparisons between the measured dynamic friction forces and the ones simulated with the established mathematic friction model at different frequencies and magnitudes demonstrate that the proposed modified LuGre friction model can give accurate simulation about the dynamic friction characteristics existing in the electromagnetic valve actuator system. The presented modelling and parameter identification methods are applicable for many other high-speed mechanical systems with friction.

Keywords

LuGre friction model / artificial fish swarm algorithm / Gauss mutation / chaotic search / parameter identification / electromagnetic valve actuator

References

[1] KamilM, RahmanM M, BakarR A. An integrated model for predicting engine friction losses in internal combustion engines [J]. Journal of Clinical Immunology, 2014, 4(1): 18-22

[2] YongJ, GaoF, DingN, HeYu. Pressure-tracking control of a novel electro-hydraulic braking system considering friction compensation [J]. Journal of Central South University, 2017, 24(8): 1909-1921

[3] LiZ, ZhouQ, ZhangZ, ZhangL, FanDa. Prestiction friction compensation in direct-drive mechatronics systems [J]. Journal of Central South University, 2013, 20(11): 3031-3041

[4] de WitC C, OlssonH, AstromK J, LischinskyP. A new model for control of systems with friction [J]. IEEE^Transactions on Automatic Control, 1995, 40(3): 419-425

[5] LiZhiLuGre-model-based friction compensation in direct-drive inertially stabilization platforms [C]//Mechatronic Systems, 2013, Hangzhou, IFAC Proceedings Volumes

[6] YangH, ZhaoY, LiM, ZhouYongStudy on the friction torque test and identification algorithm for gimbal axis of an inertial stabilized platform [J], 2015, Proceedings of the Institution of Mechanical Engineers Part G, Journal of Aerospace Engineering

[7] ZhouX, ZhaoB, LiuW, YueH, YuR, ZhaoYu. A compound scheme on parameters identification and adaptive compensation of nonlinear friction disturbance for the aerial inertially stabilized platform [J]. ISA^Transactions, 2017, 67: 293-305

[8] FreidovichL, RobertssonA, ShiriaevA, JohanssonR. LuGre-model-based friction compensation [J]. IEEE^Transactions on Control Systems Technology, 2010, 18(1): 194-200

[9] LuL, YaoB, WangQ, ChenZheng. Adaptive robust control of linear motors with dynamic friction compensation using modified LuGre model [J]. Automatica, 2009, 45(12): 2890-2896

[10] GuoK, ZhangX, LiH, MengGuang. Non-reversible friction modeling and identification [J]. Archive of Applied Mechanics, 2008, 78(10): 795-809

[11] SahaA, WahiP, WiercigrochM, StefańskiA. A modified LuGre friction model for an accurate prediction of friction force in the pure sliding regime [J]. International Journal of Non-Linear Mechanics, 2016, 80: 122-131

[12] StefańskiA, WojewodaJ, WiercigrochM, KapitaniakT. Chaos caused by non-reversible dry friction [J]. Chaos, Solitons & Fractals, 2003, 16(5): 661-664

[13] TranX B, HafizahN, YanadaH. Modeling of dynamic friction behaviors of hydraulic cylinders [J]. Mechatronics, 2012, 22(1): 65-75

[14] TranX B, DaoH T, TranK D. A new mathematical model of friction for pneumatic cylinders [J]. Proceedings of the Institution of Mechanical Engineers Part C: Journal of Mechanical Engineering Science, 2016, 230(14): 2399-2412

[15] LiY, PanQ, HuangMing. Model-based parameter identification of comprehensive friction behaviors for giant forging press [J]. Journal of Central South University, 2013, 20(9): 2359-2365

[16] TakrouriM HNonlinear friction identification of a linear voice coil DC motor [D], 2015, Sharjah, American University of Sharjah

[17] LinC J, YauH T, TianY C. Identification and compensation of nonlinear friction characteristics and precision control for a linear motor regime [J]. IEEE/ASME^Transactions on Mechatronics, 2013, 18(4): 1385-1396

[18] WuG, RuL, LiuXiang. Parameters identification of valve dynamic damping system based on LuGre model and adaptive chaotic particle swarm algorithm [J]. Procedia Engineering, 2012, 29: 3732-3736

[19] LuY, YanD L D. Friction coefficient estimation in servo systems using neural dynamic programming inspired particle swarm search [J]. Applied Intelligence, 2015, 43(1): 1-14

[20] YuY, LiY, LiJian. Parameter identification and sensitivity analysis of an improved LuGre friction model for magnetorheological elastomer base isolator [J]. Meccanica, 2015, 50(11): 2691-2707

[21] WangX, WangShao. New approach of friction identification for electro-hydraulic servo system based on evolutionary algorithm and statistical logics with experiments [J]. Journal of Mechanical Science and Technology, 2016, 30(5): 2311-2317

[22] ChengX, JiangMingAn improved artificial bee colony algorithm based on Gaussian mutation and chaos disturbance [C]//International Conference on Advances in Swarm Intelligence, 2012, Shenzhen, Springer-Verlag

[23] NeshatM, SepidnamG, SargolzaeiM, ToosiA N. Artificial fish swarm algorithm: a survey of the state-ofthe-art, hybridization, combinatorial and indicative applications [J]. Artificial Intelligence Review, 2014, 42(4): 965-997

[24] OlssonHControl systems with friction [D], 1996, Lund, Lund Institute of Technology

[25] de WitC C, ÅströmK J, SorinMSlides of the workshop on control of systems with friction [C]//IEEE^Conference on Decision and Control, 1997, USA, Florida

[26] LiXiaoA new intelligent optimization-artificial fish swarm algorithm [D], 2003, Hangzhou, Zhejiang University of Zhejiang