A Fuzzy Modified Elastoplastic Friction Model for Parameter Estimation with MHE
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- 英文论文题名：A Fuzzy Modified Elastoplastic Friction Model for Parameter Estimation with MHE
- 论文作者：Shipeng Fang ; Changhua Hu ; Xiaoxiang Hu ; Hongzeng Li
- 英文论文作者：Shipeng Fang ; Changhua Hu ; Xiaoxiang Hu ; Hongzeng Li ; Hi-Tech Institute of Xian
- 年：2017
- 作者机构：Hi-Tech Institute of Xian;
- 英文论文关键词：Friction ; Modified Elastoplastic Model ; Fuzzy System ; Moving Horizon Estimation
- 会议召开时间：2017-07-26
- 会议录名称：第36届中国控制会议论文集（B）
- 英文会议录名称：Proceedings of the 36th Chinese Control Conference（B）
- 语种：英文
- 分类号：O313.5
- 学会代码：KZLL
- 会议名称：第36届中国控制会议
- 会议地点：中国辽宁大连
- 主办单位：中国自动化学会控制理论专业委员会
- 学会名称：中国自动化学会控制理论专业委员会
- 页数：6
- 文件大小：711k
- 原文格式：D
- 会议级别：全国
摘要
Friction modeling plays a very important role in control and simulation systems. A lot of dynamic models are proposed to capture most of the friction behavior observed experimentally. Most dynamic models are typically considered to be dependent only on relative speed and relative displacement of contacting surfaces. However, it is known that dynamic characteristics of friction are affected by other factors besides speed and displacement. In this paper, a modified Elastoplastic model is presented in order to closely approximate the Elastoplastic model. In contrast to the Elastoplastic model, the modified model consists of an infinitely differentiable transition function. Such a model is well suited for gradient-based state and parameter estimation methods such as moving horizon estimation. To consider the friction changes caused by many other factors(e.g. load, temperature, lubrication, etc., but without speed and displacement), a fuzzy system is designed to establish a mapping between the other factors and the modified Elastoplastic model parameters. The complex effects of other factors on friction are fitted through the fuzzy system, while the dynamic characteristics of friction are not lost. Simulation results verify the effectiveness of the proposed model.
Friction modeling plays a very important role in control and simulation systems. A lot of dynamic models are proposed to capture most of the friction behavior observed experimentally. Most dynamic models are typically considered to be dependent only on relative speed and relative displacement of contacting surfaces. However, it is known that dynamic characteristics of friction are affected by other factors besides speed and displacement. In this paper, a modified Elastoplastic model is presented in order to closely approximate the Elastoplastic model. In contrast to the Elastoplastic model, the modified model consists of an infinitely differentiable transition function. Such a model is well suited for gradient-based state and parameter estimation methods such as moving horizon estimation. To consider the friction changes caused by many other factors(e.g. load, temperature, lubrication, etc., but without speed and displacement), a fuzzy system is designed to establish a mapping between the other factors and the modified Elastoplastic model parameters. The complex effects of other factors on friction are fitted through the fuzzy system, while the dynamic characteristics of friction are not lost. Simulation results verify the effectiveness of the proposed model.
Friction modeling plays a very important role in control and simulation systems. A lot of dynamic models are proposed to capture most of the friction behavior observed experimentally. Most dynamic models are typically considered to be dependent only on relative speed and relative displacement of contacting surfaces. However, it is known that dynamic characteristics of friction are affected by other factors besides speed and displacement. In this paper, a modified Elastoplastic model is presented in order to closely approximate the Elastoplastic model. In contrast to the Elastoplastic model, the modified model consists of an infinitely differentiable transition function. Such a model is well suited for gradient-based state and parameter estimation methods such as moving horizon estimation. To consider the friction changes caused by many other factors(e.g. load, temperature, lubrication, etc., but without speed and displacement), a fuzzy system is designed to establish a mapping between the other factors and the modified Elastoplastic model parameters. The complex effects of other factors on friction are fitted through the fuzzy system, while the dynamic characteristics of friction are not lost. Simulation results verify the effectiveness of the proposed model.
引文
[1]Jun Young Yoon,David L.Trumper,Friction modeling,identification,and compensation based on friction hysteresis and Dahl resonance,Mechatronics,24(6):734-741,2014.
[2]F.Al-Bender and J.Swevers,Characterization of friction force dynamics,IEEE Control Systems Magazine,28(6):64-81,2008.
[3]Armstrong-Helouvry,B.,Dupont,P.,De Wit,C.,A survey of models,analysis tools and compensation method for the control of machines with friction,Automatic,30(7):1083-1138,1994.
[4]A.K.Padthe,J.Oh,D.S.Bernstein,On the Lu Gre model and friction-induced hysteresis,Proceedings of the American Control Conference,2006:3247-3252.
[5]Canudas de Wit C,Olsson H,Lischinsky P,A new model for control of systems with friction,IEEE Transactions on Automatic Control,40(3):419-425,1995.
[6]Dupont P,Hayward V,Armstrong B,Single state elastoplastic friction models,IEEE Transaction on Automatic Control,47(5):787-792,2002.
[7]F.Al-Bender,V.Lampaert,J.Swevers,The generalized Maxwell-slip model:a novel model for friction simulation and compensation,IEEE Transactions on Automatic Control,50(11):1883-1897,2005.
[8]Z.Jamaludin,H.Van Brussel,and J.Swevers,Quadrant glitch compensation using friction model-based feedforward and an inverse-model-based disturbance observer,IEEE Int.Workshop Adv.Motion Control,2008:212-217.
[9]AndréCarvalho Bittencourt,Erik Wernholt,Shiva SanderTavallaey and Torgny Broga?rdh,An Extended Friction Model to capture Load and Temperature effects in Robot Joints,The 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems,2010:6161-6167.
[10]AndréCarvalho Bittencourt,Patrik Axelsson,Modeling and Experiment Design for Identification of Wear in a Robot Joint Under Load and Temperature Uncertainties Based on Friction Data,IEEE/ASME Transactions on Mechatronics,19(5):1694-1705,2014.
[11]Quintero S A P,Robust UAV coordination for target tracking using output-feedback model predictive control with moving horizon estimation,2015 American Control Conference Palmer House Hilton,2015:3758-3764.
[12]T.A.Johansen,Introduction to nonlinear model predictive control and moving horizon estimation,in Selected Topics on Constrained and Nonlinear Control,M.Huba,S.Skogestad,M.Fikar,M.Hovd,T.A.Johansen,and B.Rohal-Ilkiv,Eds.,STU Bratislava-NTNU Trondheim,2011:187-239.
[13]M.Diehl,H.G.Bock,J.Schl?der,R.Findeisen,Z.Nagy,and F.Allg?wer,Real-time optimization and nonlinear model predictive control of processes governed by differentialalgebraic equations,Journal of Process Control,12(4):577-585,2002.
[14]B.Houska,H.J.Ferreau,and M.Diehl,An auto-generated real-time iteration algorithm for nonlinear MPC in the microsecond range,Automatica,47(10):2279-2285,2011.
[15]Boris H,Joachim F H,Moritz D,ACADO toolkit—An open-source framework for automatic control and dynamic optimization,Optimal Control Applications&Methods,32:298-312,2011.
[2]F.Al-Bender and J.Swevers,Characterization of friction force dynamics,IEEE Control Systems Magazine,28(6):64-81,2008.
[3]Armstrong-Helouvry,B.,Dupont,P.,De Wit,C.,A survey of models,analysis tools and compensation method for the control of machines with friction,Automatic,30(7):1083-1138,1994.
[4]A.K.Padthe,J.Oh,D.S.Bernstein,On the Lu Gre model and friction-induced hysteresis,Proceedings of the American Control Conference,2006:3247-3252.
[5]Canudas de Wit C,Olsson H,Lischinsky P,A new model for control of systems with friction,IEEE Transactions on Automatic Control,40(3):419-425,1995.
[6]Dupont P,Hayward V,Armstrong B,Single state elastoplastic friction models,IEEE Transaction on Automatic Control,47(5):787-792,2002.
[7]F.Al-Bender,V.Lampaert,J.Swevers,The generalized Maxwell-slip model:a novel model for friction simulation and compensation,IEEE Transactions on Automatic Control,50(11):1883-1897,2005.
[8]Z.Jamaludin,H.Van Brussel,and J.Swevers,Quadrant glitch compensation using friction model-based feedforward and an inverse-model-based disturbance observer,IEEE Int.Workshop Adv.Motion Control,2008:212-217.
[9]AndréCarvalho Bittencourt,Erik Wernholt,Shiva SanderTavallaey and Torgny Broga?rdh,An Extended Friction Model to capture Load and Temperature effects in Robot Joints,The 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems,2010:6161-6167.
[10]AndréCarvalho Bittencourt,Patrik Axelsson,Modeling and Experiment Design for Identification of Wear in a Robot Joint Under Load and Temperature Uncertainties Based on Friction Data,IEEE/ASME Transactions on Mechatronics,19(5):1694-1705,2014.
[11]Quintero S A P,Robust UAV coordination for target tracking using output-feedback model predictive control with moving horizon estimation,2015 American Control Conference Palmer House Hilton,2015:3758-3764.
[12]T.A.Johansen,Introduction to nonlinear model predictive control and moving horizon estimation,in Selected Topics on Constrained and Nonlinear Control,M.Huba,S.Skogestad,M.Fikar,M.Hovd,T.A.Johansen,and B.Rohal-Ilkiv,Eds.,STU Bratislava-NTNU Trondheim,2011:187-239.
[13]M.Diehl,H.G.Bock,J.Schl?der,R.Findeisen,Z.Nagy,and F.Allg?wer,Real-time optimization and nonlinear model predictive control of processes governed by differentialalgebraic equations,Journal of Process Control,12(4):577-585,2002.
[14]B.Houska,H.J.Ferreau,and M.Diehl,An auto-generated real-time iteration algorithm for nonlinear MPC in the microsecond range,Automatica,47(10):2279-2285,2011.
[15]Boris H,Joachim F H,Moritz D,ACADO toolkit—An open-source framework for automatic control and dynamic optimization,Optimal Control Applications&Methods,32:298-312,2011.