Real-time preventive sensor maintenance using robust moving horizon estimation and economic model predictive control
Conducting preventive maintenance of measurement sensors in real‐time during process operation under feedback control while ensuring the reliability and improving the economic performance of a process is a central problem of the research area focusing on closed‐loop preventive maintenance of sensors...
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| Published in | AIChE journal Vol. 61; no. 10; pp. 3374 - 3389 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Blackwell Publishing Ltd
01.10.2015
American Institute of Chemical Engineers |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Conducting preventive maintenance of measurement sensors in real‐time during process operation under feedback control while ensuring the reliability and improving the economic performance of a process is a central problem of the research area focusing on closed‐loop preventive maintenance of sensors and actuators. To address this problem, a robust moving horizon estimation (RMHE) scheme and an economic model predictive control system are combined to simultaneously achieve preventive sensor maintenance and optimal process economic performance with closed‐loop stability. Specifically, given a preventive sensor maintenance schedule, a RMHE scheme is developed that accommodates varying numbers of sensors to continuously supply accurate state estimates to a Lyapunov‐based economic model predictive control (LEMPC) system. Closed‐loop stability for this control approach can be proven under fairly general observability and stabilizability assumptions to be made precise in the manuscript. Subsequently, a chemical process example incorporating this RMHE‐based LEMPC scheme demonstrates its ability to maintain process stability and achieve optimal process economic performance as scheduled preventive maintenance is performed on the sensors. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3374–3389, 2015 |
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| AbstractList | Conducting preventive maintenance of measurement sensors in real‐time during process operation under feedback control while ensuring the reliability and improving the economic performance of a process is a central problem of the research area focusing on closed‐loop preventive maintenance of sensors and actuators. To address this problem, a robust moving horizon estimation (RMHE) scheme and an economic model predictive control system are combined to simultaneously achieve preventive sensor maintenance and optimal process economic performance with closed‐loop stability. Specifically, given a preventive sensor maintenance schedule, a RMHE scheme is developed that accommodates varying numbers of sensors to continuously supply accurate state estimates to a Lyapunov‐based economic model predictive control (LEMPC) system. Closed‐loop stability for this control approach can be proven under fairly general observability and stabilizability assumptions to be made precise in the manuscript. Subsequently, a chemical process example incorporating this RMHE‐based LEMPC scheme demonstrates its ability to maintain process stability and achieve optimal process economic performance as scheduled preventive maintenance is performed on the sensors. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3374–3389, 2015 Conducting preventive maintenance of measurement sensors in real‐time during process operation under feedback control while ensuring the reliability and improving the economic performance of a process is a central problem of the research area focusing on closed‐loop preventive maintenance of sensors and actuators. To address this problem, a robust moving horizon estimation (RMHE) scheme and an economic model predictive control system are combined to simultaneously achieve preventive sensor maintenance and optimal process economic performance with closed‐loop stability. Specifically, given a preventive sensor maintenance schedule, a RMHE scheme is developed that accommodates varying numbers of sensors to continuously supply accurate state estimates to a Lyapunov‐based economic model predictive control (LEMPC) system. Closed‐loop stability for this control approach can be proven under fairly general observability and stabilizability assumptions to be made precise in the manuscript. Subsequently, a chemical process example incorporating this RMHE‐based LEMPC scheme demonstrates its ability to maintain process stability and achieve optimal process economic performance as scheduled preventive maintenance is performed on the sensors. © 2015 American Institute of Chemical Engineers AIChE J , 61: 3374–3389, 2015 Conducting preventive maintenance of measurement sensors in real-time during process operation under feedback control while ensuring the reliability and improving the economic performance of a process is a central problem of the research area focusing on closed-loop preventive maintenance of sensors and actuators. To address this problem, a robust moving horizon estimation (RMHE) scheme and an economic model predictive control system are combined to simultaneously achieve preventive sensor maintenance and optimal process economic performance with closed-loop stability. Specifically, given a preventive sensor maintenance schedule, a RMHE scheme is developed that accommodates varying numbers of sensors to continuously supply accurate state estimates to a Lyapunov-based economic model predictive control (LEMPC) system. Closed-loop stability for this control approach can be proven under fairly general observability and stabilizability assumptions to be made precise in the manuscript. Subsequently, a chemical process example incorporating this RMHE-based LEMPC scheme demonstrates its ability to maintain process stability and achieve optimal process economic performance as scheduled preventive maintenance is performed on the sensors. copyright 2015 American Institute of Chemical Engineers AIChE J, 61: 3374-3389, 2015 Conducting preventive maintenance of measurement sensors in real-time during process operation under feedback control while ensuring the reliability and improving the economic performance of a process is a central problem of the research area focusing on closed-loop preventive maintenance of sensors and actuators. To address this problem, a robust moving horizon estimation (RMHE) scheme and an economic model predictive control system are combined to simultaneously achieve preventive sensor maintenance and optimal process economic performance with closed-loop stability. Specifically, given a preventive sensor maintenance schedule, a RMHE scheme is developed that accommodates varying numbers of sensors to continuously supply accurate state estimates to a Lyapunov-based economic model predictive control (LEMPC) system. Closed-loop stability for this control approach can be proven under fairly general observability and stabilizability assumptions to be made precise in the manuscript. Subsequently, a chemical process example incorporating this RMHE-based LEMPC scheme demonstrates its ability to maintain process stability and achieve optimal process economic performance as scheduled preventive maintenance is performed on the sensors. |
| Author | Ellis, Matthew Christofides, Panagiotis D. Durand, Helen Lao, Liangfeng |
| Author_xml | – sequence: 1 givenname: Liangfeng surname: Lao fullname: Lao, Liangfeng organization: Dept. of Chemical and Biomolecular Engineering, University of California, CA, 90095-1592, Los Angeles – sequence: 2 givenname: Matthew surname: Ellis fullname: Ellis, Matthew organization: Dept. of Chemical and Biomolecular Engineering, University of California, CA, 90095-1592, Los Angeles – sequence: 3 givenname: Helen surname: Durand fullname: Durand, Helen organization: Dept. of Chemical and Biomolecular Engineering, University of California, CA, 90095-1592, Los Angeles – sequence: 4 givenname: Panagiotis D. surname: Christofides fullname: Christofides, Panagiotis D. email: pdc@seas.ucla.edu organization: Dept. of Chemical and Biomolecular Engineering, University of California, 90095-1592, Los Angeles, CA |
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| References | Christofides PD, El-Farra NH. Control of Nonlinear and Hybrid Process Systems: Designs for Uncertainty, Constraints and Time-Delays. Berlin, Germany: Springer-Verlag, 2005. Lao L, Ellis M, Christofides PD. Smart manufacturing: handling preventive actuator maintenance and economics using model predictive control. AIChE J. 2014;60:2179-2196. Ellis M, Zhang J, Liu J, Christofides PD. Robust moving horizon estimation-based output feedback economic model predictive control. Syst Control Lett. 2014;68:101-109. Muñoz de la Peña D, Christofides PD. Lyapunov-based model predictive control of nonlinear systems subject to data losses. IEEE Trans Autom Control. 2008;53:2076-2089. Rao CV, Rawlings JB, Mayne DQ. Constrained state estimation for nonlinear discrete-time systems: stability and moving horizon approximations. IEEE Trans Autom Control. 2003;48:246-258. Rawlings JB, Ji L. Optimization-based state estimation: current status and some new results. J Process Control. 2012;22:1439-1444. Ellis M, Christofides PD. Real-time economic model predictive control of nonlinear process systems. AIChE J. 2015;61:555-571. Khalil HK. Nonlinear Systems, 3rd ed. Upper Saddle River, NJ: Prentice Hall, 2002. Wächter A, Biegler LT. On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Math Program. 2006;106:25-57. Lao L, Ellis M, Christofides PD. Proactive fault-tolerant model predictive control. AIChE J. 2013;59:2810-2820. Lin Y, Sontag ED, Wang Y. A smooth converse Lyapunov theorem for robust stability. SIAM J Control Optim. 1996;34:124-160. Davis J, Edgar T, Porter J, Bernaden J, Sarli M. Smart manufacturing, manufacturing intelligence and demand-dynamic performance. Comput Chem Eng. 2012;47:145-156. Jäschke J, Yang X, Biegler LT. Fast economic model predictive control based on NLP-sensitivities. J Process Control. 2014;24:1260-1272. Sontag ED. A 'universal' construction of Artstein's theorem on nonlinear stabilization. Syst Control Lett. 1989;13:117-123. Ciccarella G, Dalla Mora M, Germani A. A Luenberger-like observer for nonlinear systems. Int J Control. 1993;57:537-556. Ahrens JH, Khalil HK. High-gain observers in the presence of measurement noise: a switched-gain approach. Automatica. 2009;45:936-943. Deshpande AP, Zamad U, Patwardhan SC. Online sensor/actuator failure isolation and reconfigurable control using the generalized likelihood ratio method. Ind Eng Chem Res. 2009;48:1522-1535. Grüne L, Stieler M. Asymptotic stability and transient optimality of economic MPC without terminal conditions. J Process Control. 2014;24:1187-1196. Ellis M, Durand H, Christofides PD. A tutorial review of economic model predictive control methods. J Process Control. 2014;24:1156-1178. Müller MA, Angeli D, Allgöwer F. On the performance of economic model predictive control with self-tuning terminal cost. J Process Control. 2014;24:1179-1186. Schenato L. Optimal estimation in networked control systems subject to random delay and packet drop. IEEE Trans Autom Control. 2008;53:1311-1317. El-Farra NH, Christofides PD. Bounded robust control of constrained multivariable nonlinear processes. Chem Eng Sci. 2003;58:3025-3047. Khalil HK, Esfandiari F. Semiglobal stabilization of a class of nonlinear systems using output feedback. IEEE Trans Autom Control. 1993;38:1412-1415. Liu J. Moving horizon state estimation for nonlinear systems with bounded uncertainties. Chem Eng Sci. 2013;93:376-386. Mhaskar P, Liu J, Christofides PD. Fault-Tolerant Process Control: Methods and Applications. London, England: Springer-Verlag, 2013. Kim W, Ji K, Srivastava A. Network-based control with real-time prediction of delayed/lost sensor data. IEEE Trans Control Syst Technol. 2006;14:182-185. Zhang J, Liu S, Liu J. Economic model predictive control with triggered evaluations: state and ouptut feedback. 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(e_1_2_8_30_1) 2002 e_1_2_8_24_1 e_1_2_8_25_1 e_1_2_8_26_1 e_1_2_8_27_1 e_1_2_8_3_1 e_1_2_8_2_1 e_1_2_8_5_1 e_1_2_8_7_1 e_1_2_8_6_1 e_1_2_8_9_1 e_1_2_8_8_1 e_1_2_8_20_1 e_1_2_8_21_1 e_1_2_8_22_1 e_1_2_8_23_1 e_1_2_8_17_1 e_1_2_8_18_1 e_1_2_8_19_1 Crowl DA (e_1_2_8_4_1) 2001 e_1_2_8_13_1 e_1_2_8_14_1 e_1_2_8_35_1 e_1_2_8_15_1 e_1_2_8_16_1 e_1_2_8_32_1 e_1_2_8_10_1 e_1_2_8_31_1 e_1_2_8_11_1 e_1_2_8_34_1 e_1_2_8_12_1 Christofides PD (e_1_2_8_28_1) 2005 e_1_2_8_33_1 |
| References_xml | – reference: Crowl DA, Louvar JF. Chemical Process Safety: Fundamentals with Applications, 3rd ed. Upper Saddle River, NJ: Prentice Hall, 2001. – reference: Lin Y, Sontag ED, Wang Y. A smooth converse Lyapunov theorem for robust stability. SIAM J Control Optim. 1996;34:124-160. – reference: Lao L, Ellis M, Christofides PD. Smart manufacturing: handling preventive actuator maintenance and economics using model predictive control. AIChE J. 2014;60:2179-2196. – reference: Kim W, Ji K, Srivastava A. Network-based control with real-time prediction of delayed/lost sensor data. IEEE Trans Control Syst Technol. 2006;14:182-185. – reference: Ahrens JH, Khalil HK. High-gain observers in the presence of measurement noise: a switched-gain approach. Automatica. 2009;45:936-943. – reference: Christofides PD, Davis JF, El-Farra NH, Clark D, Harris KRD, Gipson JN. Smart plant operations: vision, progress and challenges. AIChE J. 2007;53:2734-2741. – reference: Lao L, Ellis M, Christofides PD. Proactive fault-tolerant model predictive control. AIChE J. 2013;59:2810-2820. – reference: Schenato L. Optimal estimation in networked control systems subject to random delay and packet drop. IEEE Trans Autom Control. 2008;53:1311-1317. – reference: Ciccarella G, Dalla Mora M, Germani A. A Luenberger-like observer for nonlinear systems. Int J Control. 1993;57:537-556. – reference: Deshpande AP, Zamad U, Patwardhan SC. Online sensor/actuator failure isolation and reconfigurable control using the generalized likelihood ratio method. Ind Eng Chem Res. 2009;48:1522-1535. – reference: Muñoz de la Peña D, Christofides PD. Lyapunov-based model predictive control of nonlinear systems subject to data losses. IEEE Trans Autom Control. 2008;53:2076-2089. – reference: Ellis M, Durand H, Christofides PD. A tutorial review of economic model predictive control methods. J Process Control. 2014;24:1156-1178. – reference: Liu J. Moving horizon state estimation for nonlinear systems with bounded uncertainties. Chem Eng Sci. 2013;93:376-386. – reference: Rao CV, Rawlings JB, Mayne DQ. Constrained state estimation for nonlinear discrete-time systems: stability and moving horizon approximations. IEEE Trans Autom Control. 2003;48:246-258. – reference: Heidarinejad M, Liu J, Christofides PD. Economic model predictive control of nonlinear process systems using Lyapunov techniques. AIChE J. 2012;58:855-870. – reference: Davis J, Edgar T, Porter J, Bernaden J, Sarli M. Smart manufacturing, manufacturing intelligence and demand-dynamic performance. Comput Chem Eng. 2012;47:145-156. – reference: Mhaskar P, Gani A, McFall C, Christofides PD, Davis JF. Fault-tolerant control of nonlinear process systems subject to sensor faults. AIChE J. 2007;53:654-668. – reference: El-Farra NH, Christofides PD. Bounded robust control of constrained multivariable nonlinear processes. Chem Eng Sci. 2003;58:3025-3047. – reference: Khalil HK, Esfandiari F. Semiglobal stabilization of a class of nonlinear systems using output feedback. IEEE Trans Autom Control. 1993;38:1412-1415. – reference: Christofides PD, El-Farra NH. Control of Nonlinear and Hybrid Process Systems: Designs for Uncertainty, Constraints and Time-Delays. Berlin, Germany: Springer-Verlag, 2005. – reference: Grüne L, Stieler M. Asymptotic stability and transient optimality of economic MPC without terminal conditions. J Process Control. 2014;24:1187-1196. – reference: Mhaskar P, Liu J, Christofides PD. Fault-Tolerant Process Control: Methods and Applications. London, England: Springer-Verlag, 2013. – reference: Jäschke J, Yang X, Biegler LT. Fast economic model predictive control based on NLP-sensitivities. J Process Control. 2014;24:1260-1272. – reference: Sontag ED. A 'universal' construction of Artstein's theorem on nonlinear stabilization. Syst Control Lett. 1989;13:117-123. – reference: Müller MA, Angeli D, Allgöwer F. On the performance of economic model predictive control with self-tuning terminal cost. J Process Control. 2014;24:1179-1186. – reference: Ellis M, Christofides PD. Real-time economic model predictive control of nonlinear process systems. AIChE J. 2015;61:555-571. – reference: Zhang J, Liu S, Liu J. Economic model predictive control with triggered evaluations: state and ouptut feedback. J Process Control. 2014;24:1197-1206. – reference: Wächter A, Biegler LT. On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Math Program. 2006;106:25-57. – reference: Rawlings JB, Ji L. Optimization-based state estimation: current status and some new results. J Process Control. 2012;22:1439-1444. – reference: Ellis M, Zhang J, Liu J, Christofides PD. Robust moving horizon estimation-based output feedback economic model predictive control. Syst Control Lett. 2014;68:101-109. – reference: Khalil HK. Nonlinear Systems, 3rd ed. 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| SubjectTerms | Analytical chemistry Closed loop systems Control systems Economic analysis economic model predictive control Economic models Economics Horizon Maintenance moving horizon estimation Predictive control Preventive maintenance process control process economics sensor preventive maintenance Sensors smart manufacturing Stability state estimation |
| Title | Real-time preventive sensor maintenance using robust moving horizon estimation and economic model predictive control |
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