Data-driven compensation method for sensor drift faults in digital PID systems with unknown dynamics. (May 2018)

• Record Type: Journal Article
• Authors: Wang, Jun-Sheng; Yang, Guang-Hong
• Is Part Of: Journal of process control. Volume 65(2018)
• Page Start: 15
• Page End: 33
• Publication Date: 2018-05
• Subjects: Data-driven methods -- Digital PID controller -- Fault-tolerant control (FTC) -- Sensor faults; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2017.10.011
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: The paper presents a data-driven approach to the full decoupling estimation of simultaneous multiple sensor faults in unknown dynamic systems with PID controllers. A data-driven residual generator is built to make each of the above faults mapped (entirely, continuously, and without affecting other residual variables) onto its corresponding residual variable. A data-driven fault-tolerant compensation scheme is devised to eliminate the impact of the aforementioned sensor faults on the tracking performance of the PID systems. Abstract: This paper investigates the problem of compensating for the slow-changing sensor drift failures occurring in digital PID systems with unknown dynamics. It is extremely difficult to ensure the tracking performance of the faulty systems, due to the system models unavailable, the measurements corrupted by the faults, and the impact of the sensor failures propagated under feedback control. Therefore, the existing data-driven fault-tolerant control (FTC) methods are not capable of dealing with the above problem. Contrary to the current state-of-the-art, a novel residual generator structure is devised. Furthermore, its data-driven realization is accomplished, along with the convergence analysis of the proposed closed-loop recursive identification algorithm. On this basis, such failures can be estimated continuously from the residual signals, using an iterative estimation procedure, the convergence of whose mean-square estimation error is theoretically proven. As a result, the effect of the aforementioned failures on the tracking properties is eliminated with the aid of a fault detection mechanism and based on the tracking error signal corrected with the estimated faults. Finally, the effectiveness and merits of the resultant data-driven FTC algorithm are validated by the continuous stirred tank heater benchmark process.