Rodriguez-Cortes, Hugo | Instituto Tecnológico Autónomo de México |
Gonzalez-Olvera, Marcos A. | Universidad Autónoma de la Ciudad de México |
https://doi.org/10.58571/CNCA.AMCA.2023.054
Resumen: In this work, the principle of Immersion and Invariance (I&I) is used in the design of an observer/estimator for a closed-loop magnetic levitation system in order to reconstruct the magnetic levitator speed, the internal resistance of the coil and the mass of the levitating ball. By relying on Lyapunov function theory and analysis around a neighborhood of the operation point of the closed-loop dynamics, the stability and convergence of the observed states and estimated parameters to actual ones are guaranteed. Experimental results are shown to demonstrate the effectiveness of the proposed method.
¿Cómo citar?
Rodriguez-Cortes, Hugo; Gonzalez-Olvera, Marcos A. Parametric Reconstruction and State Observation in a Maglev System Via I&I. Memorias del Congreso Nacional de Control Automático, pp. 333-338, 2023. https://doi.org/10.58571/CNCA.AMCA.2023.054
Palabras clave
Control de Sistemas No Lineales; Robótica y Mecatrónica; Sistemas Adaptables
Referencias
- Aghazadeh, A., Niazazari, I., and Askarian Abyaneh, H. (2019). Tuned parameters of pid for optimization of losses in magneticlevitation system. International Journal of Railway Research, 6(1), 29-37.
- Astolfi, A., Karagiannis, D., and Ortega, R. (2008). Nonlinear and Adaptive Control with Applications, volume 187. Springer.
- Astolfi, A. and Ortega, R. (2003). Immersion and invariance: A new tool for stabilization and adaptive control of nonlinear systems. IEEE Transactions on Automatic control, 48(4), 590-606.
- Bhaduri, R., Banerjee, S., and Sarkar, M.K. (2012). Genetic algorithm based optimization of controller parameters for an electromagnetic levitation system. In Advanced Materials Research, volume 403, 3900-3908. Trans Tech Publ.
- Bojan-Dragos, C.A., Precup, R.E., Hergane, S., Teban, T.A., and Petriu, E.M. (2017). Fuzzy logic-based adaptive control scheme for magnetic levitation systems. In 2017 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS), 160-165. IEEE.
- Guerrero Tejada, C., Gonz´alez-Olvera, M.A., D´avila, J., and Fabián-Pliego, J.C. (2014). Maglev tracking control by a statefeedback with integral action and robust velocity reconstruction.
- Kumar E, V. and Jerome, J. (2016). Algebraic riccati equation based q and r matrices selection algorithm for optimal lqr applied to tracking control of 3rd order magnetic levitation system. Archives of Electrical Engineering, 65(1), 151-168.
- Levant, A. (1998). Robust exact differentiation via sliding mode technique. Automatica, 34(3), 379-384.
- Levant, A. (2003). Higher-order sliding modes, differentiation and output-feedback control. International journal of Control, 76(9-10), 924-941.
- Liu, J., Liu, X., and Su, H. (2020). Robust i&i adaptive dsc with disturbance observer for maglev system with output constraint. In 2020 39th Chinese Control Conference (CCC), 1909-1914. IEEE.
- Mekky, A.E.E. and Alberts, T. (2012). Modeling, identification, validation and control of a hybrid maglev ball. In Dynamic Systems and Control Conference, volume 45295, 423-432. American Society of Mechanical Engineers.
- Morales, R., Feliu, V., and Sira-Ramirez, H. (2010). Nonlinear control for magnetic levitation systems based on fast online algebraic identification of the input gain. IEEE Transactions on control systems technology, 19(4), 757-771.
- Rodr´ıguez, H., Ortega, R., and Siguerdidjane, H. (2000). Passivity – based control of magnetic levitation systems : theory and experiments. In 14th International Conference on Mathematical Theory and Network Systems.
- Sathiyavathi, S. et al. (2019). Design of sliding mode controller for magnetic levitation system. Computers & Electrical Engineering, 78, 184-203.
- Yang, W., Meng, F., Sun, M., and Liu, K. (2020). Passivitybased control design for magnetic levitation system. Applied Sciences, 10(7), 2392. doi:10.3390/app10072392. URL http://dx.doi.org/10.3390/app10072392.