Advances in Gain-Scheduling and Fault Tolerant Control Techniques

• Κύριος συγγραφέας: Rotondo, Damiano (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2018.
• Έκδοση: 1st ed. 2018.
• Σειρά: Springer Theses, Recognizing Outstanding Ph.D. Research,
• Θέματα: Control engineering.; Computational intelligence.; Robotics.; Automation.; System theory.; Control and Systems Theory.; Computational Intelligence.; Robotics and Automation.; Systems Theory, Control.

 This thesis reports on novel methods for gain-scheduling and fault tolerant control (FTC). It begins by analyzing the connection between the linear parameter varying (LPV) and Takagi-Sugeno (TS) paradigms. This is then followed by a detailed description of the design of robust and shifting state-feedback controllers for these systems. Furthermore, it presents two approaches to fault-tolerant control: the first is based on a robust polytopic controller design, while the second involves a reconfiguration of the reference model and the addition of virtual actuators into the loop. In short, the thesis offers a thorough review of the state-of-the art in gain scheduling and fault-tolerant control, with a special emphasis on LPV and TS systems. 

 Introduction.-  Part -- Advances in gain-scheduling techniques -- Background on gain-scheduling.-  Automated generation and comparison of Takagi-Sugeno and polytopic quasi-LPV models -- Robust state-feedback control of uncertain LPV systems.-  Shifting state-feedback control of LPV systems -- part 2 -- Background on fault tolerant control.-  Fault tolerant control of LPV systems using robust state-feedback control.-  Fault tolerant control of LPV systems using reconfigured reference model and virtual actuators -- Fault tolerant control of unstable LPV systems subject to actuator saturations and fault isolation delay -- Conclusions and future work.