A performance optimization algorithm for controller reconfiguration in fault tolerant distributed model predictive control. (October 2015)

• Record Type: Journal Article
• Authors: Zakharov, Alexey; Zattoni, Elena; Yu, Miao; Jämsä-Jounela, Sirkka-Liisa
• Is Part Of: Journal of process control. Volume 34(2015:Oct.)
• Page Start: 56
• Page End: 69
• Publication Date: 2015-10
• Subjects: Distributed model predictive control -- Fault tolerant control -- Controller reconfiguration -- Constrained optimization -- Alkylation of benzene; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2015.07.006
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract : Highlights: This paper presents a fault tolerant distributed model predictive control. Several candidate controller reconfigurations are proposed for the fault compensation. The MPC optimization problem with different controller reconfigurations is solved. The better performing controller reconfiguration is selected and implemented. The proposed approach is applied to a benzene alkylation process with distributed MPC. Abstract: This paper presents a performance optimization algorithm for controller reconfiguration in fault tolerant distributed model predictive control for large-scale systems. After the fault has been detected and diagnosed, several controller reconfigurations are proposed as candidate corrective actions for fault compensation. The solution of a set of constrained optimization problems with different actuator and setpoint reconfigurations is derived by means of an original approach, exploiting the information on the active constraints in the non-faulty subsystems. Thus, the global optimization problem is split into two optimization subproblems, which enable the online computational burden to be greatly reduced. Subsequently, the performances of different candidate controller reconfigurations are compared, and the better performing one is selected and then implemented to compensate the fault effects. Efficacy of the proposed approach has been shown by applying it to the benzene alkylation process, which is a benchmark process in distributed model predictive control.