Abstract
Fault-Tolerant Controllers (FTCs) modify system behaviour to overcome faults without human interaction. These control algorithms, when based on active approach, detect, quantify and isolate the faults during Fault Detection and Isolation (FDI) phase. Afterwards, during Control Re-design (CR) phase, the controller is reconfigured and adapted to the faulty situation. This last phase has been approached by a wide variety of algorithms, being Adaptive Controllers (ACs) the ones studied in this paper. Despite their potentiality to overcome faults, industrial manufacturing systems demand robustness and flexibility levels hardly achievable by these algorithms. On this context, the paper proposes to upgrade them introducing novel Digital-Twin (DT) models to increase its flexibility and Anti-Windup (AW) techniques to improve their robustness. These novelties reach their maximum potential when FDI and CR phases merge to generate a novel FTC platform based on a Bank of Controllers (BC), improving the fault avoidance process as controller gains are switched to the ones that recover the machine more efficiently.
Highlights
Industry behaves as a living entity, continuously evolving their manufacturing processes to decrease production times without reducing product quality
We propose a novel methodology for this approach based on a Neural-Net trained to detect, grade and isolate the fault source and Adaptive Controllers to reduce the fault effect f (t) on the system implemented through Fig. 1 schematic
Traditional controllers require to stop the machine in this adverse conditions, but the novel methodology presented in the paper maintains industrial machines working, recovering them from faults
Summary
Industry behaves as a living entity, continuously evolving their manufacturing processes to decrease production times without reducing product quality. 3) ADAPTATION MECHANISM This type of ACs overcomes the fault varying controller gains to adapt the system new behaviour to the old performance [26], [27], that is to say, the adaptation mechanism is based on studying the tracking error e between the plant output yp and the reference model output ym: e(t) = yp(t) − ym(t)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
