Actuator fault tolerance in control systems with predictive constrained set-point optimizers

Piotr M. Marusak; Piotr Tatjewski

International Journal of Applied Mathematics and Computer Science (2008)

  • Volume: 18, Issue: 4, page 539-551
  • ISSN: 1641-876X

Abstract

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Mechanisms of fault tolerance to actuator faults in a control structure with a predictive constrained set-point optimizer are proposed. The structure considered consists of a basic feedback control layer and a local supervisory set-point optimizer which executes as frequently as the feedback controllers do with the aim to recalculate the set-points both for constraint feasibility and economic performance. The main goal of the presented reconfiguration mechanisms activated in response to an actuator blockade is to continue the operation of the control system with the fault, until it is fixed. This may be even long-term, if additional manipulated variables are available. The mechanisms are relatively simple and consist in the reconfiguration of the model structure and the introduction of appropriate constraints into the optimization problem of the optimizer, thus not affecting the numerical effectiveness. Simulation results of the presented control system for a multivariable plant are provided, illustrating the efficiency of the proposed approach.

How to cite

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Piotr M. Marusak, and Piotr Tatjewski. "Actuator fault tolerance in control systems with predictive constrained set-point optimizers." International Journal of Applied Mathematics and Computer Science 18.4 (2008): 539-551. <http://eudml.org/doc/207906>.

@article{PiotrM2008,
abstract = {Mechanisms of fault tolerance to actuator faults in a control structure with a predictive constrained set-point optimizer are proposed. The structure considered consists of a basic feedback control layer and a local supervisory set-point optimizer which executes as frequently as the feedback controllers do with the aim to recalculate the set-points both for constraint feasibility and economic performance. The main goal of the presented reconfiguration mechanisms activated in response to an actuator blockade is to continue the operation of the control system with the fault, until it is fixed. This may be even long-term, if additional manipulated variables are available. The mechanisms are relatively simple and consist in the reconfiguration of the model structure and the introduction of appropriate constraints into the optimization problem of the optimizer, thus not affecting the numerical effectiveness. Simulation results of the presented control system for a multivariable plant are provided, illustrating the efficiency of the proposed approach.},
author = {Piotr M. Marusak, Piotr Tatjewski},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {fault-tolerant control; model predictive control; set-point optimization; nonlinear systems},
language = {eng},
number = {4},
pages = {539-551},
title = {Actuator fault tolerance in control systems with predictive constrained set-point optimizers},
url = {http://eudml.org/doc/207906},
volume = {18},
year = {2008},
}

TY - JOUR
AU - Piotr M. Marusak
AU - Piotr Tatjewski
TI - Actuator fault tolerance in control systems with predictive constrained set-point optimizers
JO - International Journal of Applied Mathematics and Computer Science
PY - 2008
VL - 18
IS - 4
SP - 539
EP - 551
AB - Mechanisms of fault tolerance to actuator faults in a control structure with a predictive constrained set-point optimizer are proposed. The structure considered consists of a basic feedback control layer and a local supervisory set-point optimizer which executes as frequently as the feedback controllers do with the aim to recalculate the set-points both for constraint feasibility and economic performance. The main goal of the presented reconfiguration mechanisms activated in response to an actuator blockade is to continue the operation of the control system with the fault, until it is fixed. This may be even long-term, if additional manipulated variables are available. The mechanisms are relatively simple and consist in the reconfiguration of the model structure and the introduction of appropriate constraints into the optimization problem of the optimizer, thus not affecting the numerical effectiveness. Simulation results of the presented control system for a multivariable plant are provided, illustrating the efficiency of the proposed approach.
LA - eng
KW - fault-tolerant control; model predictive control; set-point optimization; nonlinear systems
UR - http://eudml.org/doc/207906
ER -

References

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  1. Bemporad A., Borrelli F. and Morari M. (2000a). Optimal controllers for hybrid systems: Stability and piecewise linear explicit form, Proceedings of the 39-th IEEE Conference on Decision and Control, Sydney, Australia, pp. 1810-1815. 
  2. Bemporad A., Ferrari-Trecate G. and Morari M. (2000b). Observability and controllability of piecewise affine and hybrid systems, IEEE Transactions on Automatic Control 45(10): 1864-1876. Zbl0990.93010
  3. Bemporad A., Morari M., Dua V. and Pistikopoulos E. (2002). The explicit linear-quadratic regulator for constrained systems, Automatica 38(1): 3-20. Zbl0999.93018
  4. Biswas P., Grieder P., Löfberg J. and Morari M. (2005). A survey on stability analysis of discrete-time piecewise affine systems, Proceedings of the IFAC World Congress, Prague, Czech Republic, CD-ROM, paper no. Th-E12-TO/1. 
  5. Blanke M., Kinnaert M., Lunze J. and Staroswiecki M. (2006). Diagnosis and Fault-Tolerant Control, Springer-Verlag, Berlin. Zbl1126.93004
  6. Blevins T., McMillan G., Wojsznis W. and Brown M. (2003). Advanced Control Unleashed, ISA - The Instrumentation, Systems, and Automation Society, Research Triangle Park. 
  7. Camacho E. and Bordons C. (1999). Model Predictive Control, Springer-Verlag, London. Zbl1223.93037
  8. Jones C., Kerrigan E. and Maciejowski J. (2007). Lexicographic perturbation for multiparametric linear programming with applications to control, Automatica 43(10): 1808-1816. Zbl1127.90068
  9. Kassmann D., Badgwell T. and Hawkins R. (2000). Robust steady-state target calculation for model predictive control, AIChE Journal 46(5): 1007-1024. 
  10. Kerrigan, E. and Maciejowski J. (2004). Feedback min-max model predictive control using a single linear program: Robust stability and the explicit solution, International Journal of Robust and Nonlinear Control 14(4): 395-413. Zbl1051.93034
  11. Korbicz J., Kościelny J., Kowalczuk Z. and Cholewa W. (2004). Fault Diagnosis: Models, Artificial Intelligence, Applications, Springer-Verlag, Berlin. Zbl1074.93004
  12. Kościelny J. (2001). Diagnosis of Automated Industrial Processes, Academic Publishing House EXIT, Warsaw (in Polish). 
  13. Ławryńczuk M., Marusak P. and Tatjewski P. (2007a). Multilayer and integrated structures for predictive control and economic optimisation, Proceedings of the 11-th IFAC/IFORS/IMACS/IFIP Symposium on Large Scale Systems: Theory and Applications, Gdańsk, Poland, CD-ROM, paper no. 60. Zbl1153.93411
  14. Ławryńczuk M., Marusak P. and Tatjewski P. (2007b). Set-point optimisation and predictive constrained control for fast feedback controlled processes, Proceedings of the 13-th IEEE/IFAC International Conference on Methods and Models in Automation and Robotics MMAR 2007, Szczecin, Poland, pp. 357-362. 
  15. Lunze J. and Schröder J. (2004). Sensor and actuator fault diagnosis of systems with discrete inputs and outputs, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 34(2): 1096-1107. 
  16. Lunze J. and Steffen T. (2006). Control reconfiguration after actuator failures using disturbance decoupling methods, IEEE Transactions on Automatic Control 51(10): 1590-1601. 
  17. Lunze J. and Supavatanakul P. (2002). Timed discrete-event method for diagnosis of industrial actuators, Proceedings of the IEEE International Conference on Industrial Technology, Bangkok, Thailand, pp. 1354-1359. 
  18. Maciejowski J. (2002). Predictive Control with Constraints, Prentice Hall, Harlow. Zbl0978.93002
  19. Marusak P. (2006). Predictive control algorithms in constrained control systems tolerating sensor faults, Proceedings of the 12-th IEEE International Conference on Methods and Models in Automation and Robotics MMAR 2006, Mi˛edzyzdroje, Poland, pp. 797-804. 
  20. Marusak P. (2007a). Actuator fault toleration in control systems with analytical predictive controllers and output constraints, Proceedings of the 13-th IEEE/IFAC International Conference on Methods and Models in Automation and Robotics MMAR 2007, Szczecin, Poland, pp. 825-832. 
  21. Marusak P. (2007b). Predictive controllers integrated with economic optimization tolerating actuator faults: Application to a nonlinear plant, in J. Korbicz, K. Patan and M. Kowal (Eds), Fault Diagnosis and Fault Tolerant Control, Academic Publishing House EXIT, Warsaw, pp. 173-185. 
  22. Marusak P. and Tatjewski P. (2004). Predictive control algorithms in systems tolerating actuator faults, Proceedings of the 10-th IEEE International Conference on Methods and Models in Automation and Robotics MMAR 2004, Mi˛edzyzdroje, Poland, pp. 1355-1360. 
  23. Mayne D., Rawlings J., Rao C. and Scokaert P. (2000). Constrained model predictive control: Stability and optimality, Automatica 36(6): 789-814. Zbl0949.93003
  24. Mignone D., Ferrari-Trecate G. and Morari M. (2000). Stability and stabilization of piecewise affine and hybrid systems: An LMI approach, Proceedings of the 39-th IEEE Conference on Decision and Control, Sydney, Australia, pp. 504-509. 
  25. Newell R. and Lee P. (1989). Applied Process Control - A Case Study, Prentice Hall, London. 
  26. Qin S. and Badgwell T. (2003). A survey of industrial model predictive control technology, Control Engineering Practice 11(7): 733-764. 
  27. Richter J., Schlage T. and Lunze J. (2007). Control reconfiguration of a thermofluid process by means of a virtual actuator, IET Control Theory and Applications 1(6): 1606-1620. 
  28. Rossiter J. (2003). Model-Based Predictive Control, CRC Press, Boca Raton, FL. 
  29. Staroswiecki M., Yang H. and Jiang B. (2007). Progressive accommodation of parametric faults in linear quadratic control, Automatica 43(12): 2070-2076. Zbl1138.49029
  30. Tatjewski P. (2007). Advanced Control of Industrial Processes; Structures and Algorithms, Springer-Verlag, London. Zbl1134.93037
  31. Tondell P., Johansen T. and Bemporad A. (2003). An algorithm for multiparametric quadratic programming and explicit MPC solutions, Automatica 39(3): 489-497. Zbl1019.93019
  32. Tvrzska de Gouvea M. and Odloak D. (1998). One-layer real time optimization of LPG production in the FCC unit: Procedure, advantages and disadvantages, Computers and Chemical Engineering 22(Supplement 1): S191-S198. 
  33. Venkatasubramanian V., Rengaswamy R., Yin K. and Kavuri, S. (2003). A review of process fault detection and diagnosis, Computers and Chemical Engineering 27(3): 293-346. 
  34. Yen G. and Ho L. (2003). Online multiple-model-based fault diagnosis and accommodation, IEEE Transactions on Industrial Electronics 50(2): 296-312. 
  35. Zanin A., de Gouvea M. T. and Odloak D. (2000). Industrial implementation of a real-time optimization strategy for maximizing production of LPG in a FCC unit, Computers and Chemical Engineering 24(2-7): 525-531. 
  36. Zanin A., de Gouvea M. T. and Odloak D. (2002). Integrating real-time optimization into the model predictive controller of the FCC system, Control Engineering Practice 10(8): 819-831. 
  37. Zhang Y. (2007). Active fault-tolerant control systems: Integration of fault diagnosis and reconfigurable control, in J. Korbicz, K. Patan and M. Kowal (Eds), Fault Diagnosis and Fault Tolerant Control, Academic Publishing House EXIT, Warsaw, pp. 21-41. 

Citations in EuDML Documents

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  1. Ahmed Khelassi, Didier Theilliol, Philippe Weber, Reconfigurability analysis for reliable fault-tolerant control design
  2. Silvio Simani, Residual generator fuzzy identification for automotive diesel engine fault diagnosis
  3. Boumedyen Boussaid, Christophe Aubrun, Mohamed Naceur Abdelkrim, Mohamed Koni Ben Gayed, Performance evaluation based fault tolerant control with actuator saturation avoidance
  4. Piotr Tatjewski, Supervisory predictive control and on-line set-point optimization

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