Abstract
This paper considers an application of continuous-discrete unscented Kalman filter (or hybrid UKF) to estimate state and friction for a position control system with Stribeck + viscous friction. Adaptive compensation of friction is integrated to the system. The coefficients of Stribeck curve are estimated online from joint state and parameter model by using hybrid UKF algorithm. To select sampling time appropriately for estimator, the stability and accuracy at low velocity of deterministic system are analysed. This paper finally presents the simulation results of a torque-controlled DC motor model to illustrate the performance of the position control system with PID controller using friction compensation since conventional PID controller are easily modified. The results show that the system with friction compensation exhibits a much better performance.
Keywords
References
- 1. (2010). ‘Cubature Kalman filtering for continuous-discrete system systems: theory and simulation’. IEEE Trans. Signal Process. 58, 10, 4977-4993 Google Scholar
- 2. (1991). Control of Machines with Friction. Boston:Kluwer Academic Publisher Google Scholar
- 3. (1994). ‘A survey of models analysis tools and compensation methods for the control of machines with friction’. Automatica. 30, 7, 1083-1138 Google Scholar
- 4. (2005). ‘Friction compensation in robotics: an overview’. in Proc. of 44th IEEE Conf. on Decision and Control, and the European Control Conf. Google Scholar
- 5. (1997). ‘Adaptive friction compensation with partially known dynamic friction model’. Int. J. of Adaptive Control and Signal Processing. 11, 65-80 Google Scholar
- 6. (1995). ‘A new model for control system with friction’. IEEE Trans. on Automatic Control. 40, 3, 419-425 Google Scholar
- 7. (1999). ‘Adaptive control techniques for friction compensation’. Mechatronics. 9, 125-145 Google Scholar
- 8. (1992). ‘On adaptive friction compensation’. IEEE Trans. on Automatic Control. 37, 10, 1609-1612 Google Scholar
- 9. (2000). ‘A new method for the nonlinear transformation of means and covariances in filters and estimators’. IEEE Trans. Autom. Control. 45, 477-482 Google Scholar
- 10. (1985). ‘Computer simulation of stic-slip friction in mechanical systems’. J. of Dynamic Systems Measurement and Control. 107, 1, 100-103 Google Scholar
- 11. (2008).
‘Adaptive compensation of dynamic friction in an industrial robot’.
in Proc. 17th IEEE Int. Conf. on Control Applications, Part of 2008 IEEE Multi-Conf. on Systems and Control.
San Antonio, Texas, USA Google Scholar - 12. (2009). ‘Adaptive friction compensation in the presence of backlash’. Control Engineering and Applied Informatics. 11, 1, 3-9 Google Scholar
- 13. (1998). ‘Friction models and friction compensation’. European Journal of Control. 4, 176-195 Google Scholar
- 14. (2002). ‘Analysis and model-based control of servomechanisms with friction’. in Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and System. 3, 2109-2114 Google Scholar
- 15. (2004). ‘Observer-based compensation of discontinuous friction’. in Proc. 43rd IEEE Conf. in Decision and Control. 4940-4945 Google Scholar
- 16. (2001). ‘A nonlinear friction compensation method using adaptive control and its practical application to an in-parallel actuated 6-dof manipulator’. Control Engineering Practice. 9, 159-167 Google Scholar
- 17. (2007). ‘On unscented Kalman filtering for state estimation of continuous-time nonlinear system’. IEEE Trans. on Automatic Control. 52, 9, 1631-1641 Google Scholar
- 18. (2004). Sigma-point Kalman Filters for Probabilistic Inference in Dynamic State-space Modes. Portland, OR:OGI School of Sci. Eng., Oregon Health and Sci. Univ. , PhD Google Scholar
- 19. (2008). ‘Stochastic stability of the continuous-time unscented Kalman filter’. in Proc. 47th IEEE Conf. on Decision and Control. Google Scholar