Kalman-Like Disturbance Observer-Based Control (I)

Resumen: State estimation using the Kalman Filter (KF) is one of the principal topics in areas such as guidance, navigation, and control. The disturbance observer (DOB) is a control scheme with a proven efficiency reported in the literature. In this article, we develop an alternative method, in which we estimated the compensation of unknown disturbances with the Kalman filter. We use the estimation to cancel the effects of the disturbance in the system. It gave a simulation example to show the effectiveness of the proposed method against classical algorithms.

¿Cómo citar?

Jorge A. Ortega-Contreras, Miguel A. Vazquez-Olguin, Jose A. Andrade-Lucio & Yuriy S. Shmaliy. Kalman-Like Disturbance Observer-Based Control (I). Memorias del Congreso Nacional de Control Automático, pp. 352-357, 2021.

Palabras clave

Disturbance Observer, Kalman Filter

• Chen, W.H., Yang, J., Guo, L., and Li, S. (2016). Disturbance-observer-based control and related methodsan overview. IEEE Transactions on Industrial Electronics, 63(2), 1083–1095. doi: 10.1109/TIE.2015.2478397.
• Cui, M. (2019). Observer-based adaptive tracking control of wheeled mobile robots with unknown slipping parameters. IEEE Access, 7, 169646169655. doi: 10.1109/access.2019.2955887.
• González, G.D. (2010). Soft Sensing, 143212. Springer London. doi:10.1007/978-1-84996-106-6-4.
• Hosseinnajad, A. and Loueipour, M. (2021). Design of a robust observer-based dp control system for an rov with unknown dynamics including thruster allocation. In 2021 7th International Conference on Control, Instrumentation and Automation (ICCIA). IEEE. doi: 10.1109/iccia52082.2021.9403543.
• Li, S., Yang, J., Chen, W.H., and Chen, X. (2017). Disturbance Observer-Based Control: Methods and Applications. CRC Press, Inc., USA, 1st edition.
• Lin, M.T., Yau, H.T., Nien, H.W., and Tsai, M.S. (2008). Fpga-based motion controller with real-time look-ahead function. IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM. doi:10.1109/AIM.2008.4601868.
• Ljung, L. (2010). Perspectives on system identification. Annual Reviews in Control, 34(1), 1–12. doi: https://doi.org/10.1016/j.arcontrol.2009.12.001. Ohishi, K., Ohnishi, K., and Miyachi, K. (1983). Torque – speed regulation of dc motor based on load torque estimation method. 1209–1218.
• Phuong, T.T., Ohishi, K., Mitsantisuk, C., Yokokura, Y., Ohnishi, K., Oboe, R., and Sabanovic, A. (2018). Disturbance observer and kalman filter based motion control realization. IEEJ Journal of Industry Applications, 7(1), 1–14. doi:10.1541/ieejjia.7.1.
• Preitl, S., Precup, R.E., Preitl, Z., Vaivoda, S., Kilyeni, S., and Tar, J.K. (2007). Iterative feedback and learning control. servo systems applications. IFAC Proceedings Volumes, 40(8), 16–27. doi: https://doi.org/10.3182/20070709-3-RO4910.00004. 1st IFAC Workshop on Convergence of Information Technologies and Control Methods with Power Plants and Power Systems.
• Radke, A. and Gao, Z. (2006). A survey of state and disturbance observers for practitioners. In 2006 American Control Conference. IEEE. doi: 10.1109/acc.2006.1657545.
• Rhudy, M.B. and Gu, Y. (2013). Online stochastic convergence analysis of the kalman filter. International Journal of Stochastic Analysis, 2013, 1–9. doi:10.1155/2013/240295. URL https://doi.org/10.1155/2013/240295.
• Sariyildiz, E. and Ohnishi, K. (2013). A guide to design disturbance observer. Journal of Dynamic Systems, Measurement, and Control, 136(2). doi: 10.1115/1.4025801.
• Su, J., Li, B., and Chen, W.H. (2015). On existence, optimality and asymptotic stability of the kalman filter with partially observed inputs. Automatica, 53, 149–154. doi: https://doi.org/10.1016/j.automatica.2014.12.044.
• Wang, Y., Lin, Q., Huang, J., Zhou, L., Cao, J., Qiao, G., and Shi, X. (2020). Sliding mode robust control of a wire-driven parallel robot based on hji theory and a disturbance observer. IEEE Access, 8, 215235215245. doi:10.1109/access.2020.3040652.
• Zhang, Q. (2017). On stability of the kalman filter for discrete time output error systems. Systems and Control Letters, 107, 84–91.
• Zhu, W., Du, H., and Li, S. (2019). Observer-based output feedback stabilization for perturbed secondorder uncertain system with finite-time convergence. In 2019 Chinese Control Conference (CCC). IEEE. doi: 10.23919/chicc.2019.8866067.
• Ziegler, J.G. and Nichols, N. (1942). Optimum settings for automatic controllers. Journal of Dynamic Systems Measurement and Control-transactions of The Asme, 115, 220–222.