- -

Control Multimodal en Entornos Inciertos usando Aprendizaje por Refuerzos y Procesos Gaussianos

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Control Multimodal en Entornos Inciertos usando Aprendizaje por Refuerzos y Procesos Gaussianos

Mostrar el registro completo del ítem

De Paula, M.; Ávila, LO.; Sánchez Reinoso, C.; Acosta, GG. (2015). Control Multimodal en Entornos Inciertos usando Aprendizaje por Refuerzos y Procesos Gaussianos. Revista Iberoamericana de Automática e Informática industrial. 12(4):385-396. https://doi.org/10.1016/j.riai.2015.09.004

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/143715

Ficheros en el ítem

Metadatos del ítem

Título: Control Multimodal en Entornos Inciertos usando Aprendizaje por Refuerzos y Procesos Gaussianos
Otro titulo: Multimodal Control in Uncertain Environments using Reinforcement Learning and Gaussian Processes
Autor: De Paula, Mariano Ávila, Luis O. sánchez Reinoso, Carlos Acosta, Gerardo G.
Fecha difusión:
Resumen:
[ES] El control de sistemas complejos puede ser realizado descomponiendo la tarea de control en una secuencia de modos de control, o simplemente modos. Cada modo implementa una ley de retroalimentación hasta que se activa ...[+]


[EN] The control of complex systems can be done decomposing the control task into a sequence of control modes, or modes for short. Each mode implements a parameterized feedback law until a termination condition is activated ...[+]
Palabras clave: Multimodal Control , Dynamic Programming , Gaussian Processes , Uncertainty , Policy , Control multimodal , Programación dinámica , Procesos Gaussianos , Incertidumbre , Política
Derechos de uso: Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
Fuente:
Revista Iberoamericana de Automática e Informática industrial. (issn: 1697-7912 ) (eissn: 1697-7920 )
DOI: 10.1016/j.riai.2015.09.004
Editorial:
Universitat Politècnica de València
Versión del editor: https://doi.org/10.1016/j.riai.2015.09.004
Tipo: Artículo

References

Abate, A., Prandini, M., Lygeros, J., & Sastry, S. (2008). Probabilistic reachability and safety for controlled discrete time stochastic hybrid systems. Automatica, 44(11), 2724-2734. doi:10.1016/j.automatica.2008.03.027

Adamek, F., M Sobotka, O Stursberg. 2008. Stochastic optimal control for hybrid systems with uncertain discrete dynamics. Proceedings of the IEEE International Conference on Automation Science and Engineering, 23-28. Washington D.C.

Åström, Karl Johan, Bo Bernhardsson. 2003. System with Lebesgue Sampling. Directions in Mathematical Systems Theory and Optimization, LNCIS 268. LNCIS. Springer-Verlag Berlin Heidelberg. [+]
Abate, A., Prandini, M., Lygeros, J., & Sastry, S. (2008). Probabilistic reachability and safety for controlled discrete time stochastic hybrid systems. Automatica, 44(11), 2724-2734. doi:10.1016/j.automatica.2008.03.027

Adamek, F., M Sobotka, O Stursberg. 2008. Stochastic optimal control for hybrid systems with uncertain discrete dynamics. Proceedings of the IEEE International Conference on Automation Science and Engineering, 23-28. Washington D.C.

Åström, Karl Johan, Bo Bernhardsson. 2003. System with Lebesgue Sampling. Directions in Mathematical Systems Theory and Optimization, LNCIS 268. LNCIS. Springer-Verlag Berlin Heidelberg.

Axelsson, H., Wardi, Y., Egerstedt, M., & Verriest, E. I. (2007). Gradient Descent Approach to Optimal Mode Scheduling in Hybrid Dynamical Systems. Journal of Optimization Theory and Applications, 136(2), 167-186. doi:10.1007/s10957-007-9305-y

Barton, P. I., Lee, C. K., & Yunt, M. (2006). Optimization of hybrid systems. Computers & Chemical Engineering, 30(10-12), 1576-1589. doi:10.1016/j.compchemeng.2006.05.024

Bemporad, A., & Di Cairano, S. (2011). Model-Predictive Control of Discrete Hybrid Stochastic Automata. IEEE Transactions on Automatic Control, 56(6), 1307-1321. doi:10.1109/tac.2010.2084810

Bemporad, A., & Morari, M. (1999). Control of systems integrating logic, dynamics, and constraints. Automatica, 35(3), 407-427. doi:10.1016/s0005-1098(98)00178-2

Bensoussan, A.,J. L. Menaldi. 2000. Stochastic hybrid control. Journal of Mathematical Analysis and Applications 249.

Bertsekas, Dimitri P. 2000. Dynamic Programming and Optimal Control, Vol. I. 2nd ed. Athena Scientific.

Blackmore, L., Ono, M., Bektassov, A., & Williams, B. C. (2010). A Probabilistic Particle-Control Approximation of Chance-Constrained Stochastic Predictive Control. IEEE Transactions on Robotics, 26(3), 502-517. doi:10.1109/tro.2010.2044948

Borrelli, F., Baotić, M., Bemporad, A., & Morari, M. (2005). Dynamic programming for constrained optimal control of discrete-time linear hybrid systems. Automatica, 41(10), 1709-1721. doi:10.1016/j.automatica.2005.04.017

Bryson, Jr Arthur E., Yu-Chi Ho. 1975. Applied optimal control: optimization, estimation and control. Revised. Taylor & Francis.

Busoniu, Lucian, Robert Babuska, Bart De Schutter,Damien Ernst. 2010. Reinforcement learning and dynamic programming using function approximators. 1.a ed. CRC Press.

Cassandras, Christos G., John Lygeros. 2007. Stochastic hybrid systems. Boca Raton: Taylor & Francis.

Deisenroth, Marc Peter. 2010. Efficient Reinforcement Learning Using Gaussian Processes. KIT Scientific Publishing.

Deisenroth, M. P., Rasmussen, C. E., & Peters, J. (2009). Gaussian process dynamic programming. Neurocomputing, 72(7-9), 1508-1524. doi:10.1016/j.neucom.2008.12.019

Di Cairano, S., Bemporad, A., & Júlvez, J. (2009). Event-driven optimization-based control of hybrid systems with integral continuous-time dynamics. Automatica, 45(5), 1243-1251. doi:10.1016/j.automatica.2008.12.011

Ding, X.-C., Wardi, Y., & Egerstedt, M. (2009). On-Line Optimization of Switched-Mode Dynamical Systems. IEEE Transactions on Automatic Control, 54(9), 2266-2271. doi:10.1109/tac.2009.2026864

Egerstedt, M., Wardi, Y., & Axelsson, H. (2006). Transition-Time Optimization for Switched-Mode Dynamical Systems. IEEE Transactions on Automatic Control, 51(1), 110-115. doi:10.1109/tac.2005.861711

Girard, Agathe. 2004. Approximate methods for propagation of uncertainty with gaussian process models. University of Glasgow.

Kuss, M. 2006. Gaussian process models for robust regression, classification, and reinforcement learning. Technische Universite Darmstadt.

Liberzon, Daniel. 2003. Switching in systems and control. Systems & Control: Foundations & Applications. Boston: Birkhäuser Boston Inc.

Lincoln, B., & Rantzer, A. (2006). Relaxing Dynamic Programming. IEEE Transactions on Automatic Control, 51(8), 1249-1260. doi:10.1109/tac.2006.878720

Lunze, J., & Lehmann, D. (2010). A state-feedback approach to event-based control. Automatica, 46(1), 211-215. doi:10.1016/j.automatica.2009.10.035

Mehta, Tejas,Magnus Egerstedt. 2005. Learning multi-modal control programs. Hybrid Systems: Computation and Control, 466-479. Lecture Notes in Computer Science. Springer Berlin.

Mehta, T. R., & Egerstedt, M. (2006). An optimal control approach to mode generation in hybrid systems. Nonlinear Analysis: Theory, Methods & Applications, 65(5), 963-983. doi:10.1016/j.na.2005.07.044

Mehta, T. R., & Egerstedt, M. (2008). Multi-modal control using adaptive motion description languages. Automatica, 44(7), 1912-1917. doi:10.1016/j.automatica.2007.11.024

Pajares Martin-Sanz, G., y De la Cruz Garcia J.M. 2010. Aprendizaje automático. Un enfoque práctico, Cap. 12, Aprendizaje por Refuerzos. RA-MA.

Rantzer, A. (2006). Relaxed dynamic programming in switching systems. IEE Proceedings - Control Theory and Applications, 153(5), 567-574. doi:10.1049/ip-cta:20050094

Rasmussen, Carl Edward,Christopher K. I. Williams. 2006. Gaussian processes for machine learning. MIT Press.

Rosenstein, Michael T.,Andrew G. Barto. 2004. Supervised Actor-Critic Reinforcement Learning. Handbook of Learning and Approximate Dynamic Programming, 359-380. John Wiley & Sons, Inc.

Song, C., & Li, P. (2010). Near optimal control for a class of stochastic hybrid systems. Automatica, 46(9), 1553-1557. doi:10.1016/j.automatica.2010.05.024

Sutton, Richard S.,Andrew G. Barto. 1998. Reinforcement learning: An introduction. MIT Press.

Verdinelli, I., & Kadane, J. B. (1992). Bayesian Designs for Maximizing Information and Outcome. Journal of the American Statistical Association, 87(418), 510-515. doi:10.1080/01621459.1992.10475233

Xu, Xuping,Panos J. Antsaklis. 2003. Results and perspectives on computational methods for optimal control of switched systems. Proceedings of the 6th international conference on Hybrid systems: computation and control, 540-555. Springer-Verlag.

Xu, Y.-K., & Cao, X.-R. (2011). Lebesgue-Sampling-Based Optimal Control Problems With Time Aggregation. IEEE Transactions on Automatic Control, 56(5), 1097-1109. doi:10.1109/tac.2010.2073610

[-]

recommendations

 

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro completo del ítem