Resilient-based Control Reconfiguration of Autonomous Systems
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Published
Jul 14, 2017
Sehwan Oh
Benjamin Lee
Michael Balchanos
Dimitri Mavris
George Vachtsevanos
Abstract
This paper introduces a design methodology for resilientbased
control reconfiguration of Unmanned Autonomous
Systems (UAS) when extreme disturbances, such as a largely
growing fault or component failure mode occur. It is
documented that more than 40% of Class air mishaps are
attributed to Unmanned Aerial Vehicles (UAVs). There is an
urgent need to improve the operational integrity, resilience
and reliability of such critical assets. An optimal control
approach with Differential Dynamic Programming (DDP)
and Model Predictive Control (MPC) is introduced in this
paper as a means for control authority redistribution and
reconfiguration; therefore, the system continues performing
its mission while compensating for the impact of the extreme
disturbances. Prognostic knowledge is considered in a
quadratic cost function of the optimal control problem as a
soft constraint. A trade-off parameter is introduced between
the prognostic constraint and the terminal cost. An
autonomous ground operable under-actuated hovercraft is
used to demonstrate the efficacy of the proposed
reconfiguration strategy, and it is extendable to other cyber
physical systems.
##plugins.themes.bootstrap3.article.details##
Keywords
PHM
References
Balchanos, M. G., (2012). A probabilistic technique for the assessment of complex dynamic system resilience. Doctoral dissertation. Georgia Institute of Technology, Atlanta, USA.
Bellman, R., (1957). Dynamic programming: Princeton Univ. press.
Bole, B., Tang, L., Goebel, K., & Vachtsevanos, G., (2011). Adaptive load allocation for prognosis-based risk management. Annual conference of the prognostics and health management society, pp. 1–10.
Bole, B. M., (2013). Load allocation for optimal risk management in systems with incipient failure modes. Doctoral dissertation, Georgia Institute of Technology, Atlanta, USA.
Brown, D. W., Georgoulas, G., Bole, B., Pei, H. L., Orchard, M., Tang, L., Saha, B., Saxena, A., Goebel, K., & Vachtsevanos, G., (2009). Prognostics enhanced reconfigurable control of electro-mechanical actuators. Annual conference of the prognostics and health management society.
Brown, D., Bole, B., & Vachtsevanos, G., (2010). A prognostics enhanced reconfigurable control architecture. Control & Automation (MED), 2010 18th Mediterranean Conference, IEEE, pp. 1061-1066. doi:10.1109/MED.2010.5547651
Clements, N. S., (2003). Fault tolerant control of complex dynamical systems. Doctoral dissertation, Georgia Institute of Technology, Atlanta, USA.
Dorf, R. C., & Bishop, R. H., (1998). Modern control systems: Addison-Wesley.
Downes, L., (2015). What’s wrong with the FAA’s new drone rules. Harvard Business Review, Harvard University.
Drozeski, G. R., Saha, B., & Vachtsevanos, G., (2005). A fault detection and reconfigurable control architecture for unmanned aerial vehicles. Aerospace Conference, IEEE. doi:10.1109/AERO.2005.1559597
Ge, J., Kacprzynski, G. J., Roemer, M. J., & Vachtsevanos, G., (2004). Automated contingency management design for UAVs. AIAA 1st Intelligent Systems Technical Conference, pp. 20–22. doi:10.2514/6.2004-6464
Han, B., & Zhao, G. L., (2004). Course-keeping control of underactuated hovercraft. Journal of Marine Science and Application, 3(1), 24-27. doi:10.1007/BF02918642
Hollnagel, E., Woods, D. D., & Leveson, N., (2007). Resilience engineering: Concepts and precepts: Ashgate Publishing, Ltd.
Jacobson, D. H., & Mayne, D. Q., (1970). Differential dynamic programming. American Elsevier, New York.
Kim, K., Lee, Y., Oh, S., Moroniti, D., Mavris, D., Vachtsevanos, G. J., Papamarkos, N., & Georgoulas, G., (2013). Guidance, navigation, and control of an unmanned hovercraft. Control & Automation (MED), 2013 21st Mediterranean Conference, IEEE, pp. 380–387. doi:10.1109/MED.2013.6608750
Orchard, M. E., (2007). A Particle filtering-based framework for on-line fault diagnosis and failure prognosis. Doctoral dissertation, Georgia Institute of Technology, Atlanta, USA.
Pannocchia, G., Rawlings, J. B., & Wright, S. J., (2011). Conditions under which suboptimal nonlinear MPC is inherently robust. Systems & Control Letters, 60(9), 747- 755. doi:10.1016/j.sysconle.2011.05.013
Patron, P., Miguelanez, E., Petillot, Y. R., Lane, D. M., & Salvi, J., (2008). Adaptive mission plan diagnosis and repair for fault recovery in autonomous underwater vehicles. OCEANS 2008, IEEE, pp. 1–9. doi:10.1109/OCEANS.2008.5151975
Sconyers, C., Lee, Y., Kim, K., Oh, S., Mavris, D., Oza, N., Mah, R., Martin, R., Raptis, I. A., & Vachtsevanos, G. J., (2013). Diagnosis of fault modes masked by control loops with an application to autonomous hovercraft systems. International Journal of Prognostics and Health Management.
Tang, L., Kacprzynski, G. J., Goebel, K., Saxena, A., Saha, B., & Vachtsevanos, G., (2008). Prognostics-enhanced automated contingency management for advanced autonomous systems. Prognostics and Health Management, International Conference, IEEE, pp. 1–9, IEEE. doi:10.1109/PHM.2008.4711448
Tang, L., Hettler, E., Zhang, B., & DeCastro, J., (2011). A testbed for real-time autonomous vehicle PHM and contingency management applications. Annual conference of the prognostics and health management society, pp. 1–11.
Tran, H. T., (2015). A complex networks approach to designing resilient system-of-systems. Doctoral dissertation. Georgia Institute of Technology, Atlanta, USA.
Vachtsevanos, G., Lewis, F. L., Roemer, M., Hess, A., &Wu, B., (2006). Intelligent fault diagnosis and prognosis for engineering systems. Hoboken, New Jersey: John Wiley and Sons, Inc.
Yan, Z., Zhao, Y., Chen, T., & Jiang, L., (2012). Fault recovery based mission scheduling of AUV for oceanographic survey. Intelligent Control and Automation (WCICA), 10th World Congress, IEEE, pp. 4071–4076. doi:10.1109/WCICA.2012.6359156
Zhang, Y. & Jiang, J., (2008). Bibliographical review on reconfigurable fault-tolerant control systems. Annual reviews in control, vol. 32, no. 2, pp. 229–252. doi: 10.1016/j.arcontrol.2008.03.008
Bellman, R., (1957). Dynamic programming: Princeton Univ. press.
Bole, B., Tang, L., Goebel, K., & Vachtsevanos, G., (2011). Adaptive load allocation for prognosis-based risk management. Annual conference of the prognostics and health management society, pp. 1–10.
Bole, B. M., (2013). Load allocation for optimal risk management in systems with incipient failure modes. Doctoral dissertation, Georgia Institute of Technology, Atlanta, USA.
Brown, D. W., Georgoulas, G., Bole, B., Pei, H. L., Orchard, M., Tang, L., Saha, B., Saxena, A., Goebel, K., & Vachtsevanos, G., (2009). Prognostics enhanced reconfigurable control of electro-mechanical actuators. Annual conference of the prognostics and health management society.
Brown, D., Bole, B., & Vachtsevanos, G., (2010). A prognostics enhanced reconfigurable control architecture. Control & Automation (MED), 2010 18th Mediterranean Conference, IEEE, pp. 1061-1066. doi:10.1109/MED.2010.5547651
Clements, N. S., (2003). Fault tolerant control of complex dynamical systems. Doctoral dissertation, Georgia Institute of Technology, Atlanta, USA.
Dorf, R. C., & Bishop, R. H., (1998). Modern control systems: Addison-Wesley.
Downes, L., (2015). What’s wrong with the FAA’s new drone rules. Harvard Business Review, Harvard University.
Drozeski, G. R., Saha, B., & Vachtsevanos, G., (2005). A fault detection and reconfigurable control architecture for unmanned aerial vehicles. Aerospace Conference, IEEE. doi:10.1109/AERO.2005.1559597
Ge, J., Kacprzynski, G. J., Roemer, M. J., & Vachtsevanos, G., (2004). Automated contingency management design for UAVs. AIAA 1st Intelligent Systems Technical Conference, pp. 20–22. doi:10.2514/6.2004-6464
Han, B., & Zhao, G. L., (2004). Course-keeping control of underactuated hovercraft. Journal of Marine Science and Application, 3(1), 24-27. doi:10.1007/BF02918642
Hollnagel, E., Woods, D. D., & Leveson, N., (2007). Resilience engineering: Concepts and precepts: Ashgate Publishing, Ltd.
Jacobson, D. H., & Mayne, D. Q., (1970). Differential dynamic programming. American Elsevier, New York.
Kim, K., Lee, Y., Oh, S., Moroniti, D., Mavris, D., Vachtsevanos, G. J., Papamarkos, N., & Georgoulas, G., (2013). Guidance, navigation, and control of an unmanned hovercraft. Control & Automation (MED), 2013 21st Mediterranean Conference, IEEE, pp. 380–387. doi:10.1109/MED.2013.6608750
Orchard, M. E., (2007). A Particle filtering-based framework for on-line fault diagnosis and failure prognosis. Doctoral dissertation, Georgia Institute of Technology, Atlanta, USA.
Pannocchia, G., Rawlings, J. B., & Wright, S. J., (2011). Conditions under which suboptimal nonlinear MPC is inherently robust. Systems & Control Letters, 60(9), 747- 755. doi:10.1016/j.sysconle.2011.05.013
Patron, P., Miguelanez, E., Petillot, Y. R., Lane, D. M., & Salvi, J., (2008). Adaptive mission plan diagnosis and repair for fault recovery in autonomous underwater vehicles. OCEANS 2008, IEEE, pp. 1–9. doi:10.1109/OCEANS.2008.5151975
Sconyers, C., Lee, Y., Kim, K., Oh, S., Mavris, D., Oza, N., Mah, R., Martin, R., Raptis, I. A., & Vachtsevanos, G. J., (2013). Diagnosis of fault modes masked by control loops with an application to autonomous hovercraft systems. International Journal of Prognostics and Health Management.
Tang, L., Kacprzynski, G. J., Goebel, K., Saxena, A., Saha, B., & Vachtsevanos, G., (2008). Prognostics-enhanced automated contingency management for advanced autonomous systems. Prognostics and Health Management, International Conference, IEEE, pp. 1–9, IEEE. doi:10.1109/PHM.2008.4711448
Tang, L., Hettler, E., Zhang, B., & DeCastro, J., (2011). A testbed for real-time autonomous vehicle PHM and contingency management applications. Annual conference of the prognostics and health management society, pp. 1–11.
Tran, H. T., (2015). A complex networks approach to designing resilient system-of-systems. Doctoral dissertation. Georgia Institute of Technology, Atlanta, USA.
Vachtsevanos, G., Lewis, F. L., Roemer, M., Hess, A., &Wu, B., (2006). Intelligent fault diagnosis and prognosis for engineering systems. Hoboken, New Jersey: John Wiley and Sons, Inc.
Yan, Z., Zhao, Y., Chen, T., & Jiang, L., (2012). Fault recovery based mission scheduling of AUV for oceanographic survey. Intelligent Control and Automation (WCICA), 10th World Congress, IEEE, pp. 4071–4076. doi:10.1109/WCICA.2012.6359156
Zhang, Y. & Jiang, J., (2008). Bibliographical review on reconfigurable fault-tolerant control systems. Annual reviews in control, vol. 32, no. 2, pp. 229–252. doi: 10.1016/j.arcontrol.2008.03.008
Section
Regular Session Papers