Fault-tolerant Model-based Predictive Control Applied to Industrial Processes

Abstract

Modern plants rely on sophisticated control systems to meet performance and stability requirements. In particular, a conventional feedback control design for a complex system may result in unsatisfactory performance, or even instability, in the event of malfunctions in actuators, sensors or other system components. In view of these aspects, this thesis addresses the design, development and evaluation of fault-tolerant controllers for typical industrial processes, which ensure the compliance of operational constraints despite the presence of faults. To begin with, the current state-of-art and the main specific concepts are introduced. Then, two model-based strategies are presented. On the one side, the design of a novel observer-based fault detection and diagnosis scheme and the development of an adaptive predictive controller are combined to deploy a non-linear active fault-tolerant control system, on the basis of the linear parameter varying system representation. This proposed scheme is evaluated on typical non-linear chemical industrial processes. On the other hand, an optimisation-based fault-tolerant predictive controller was proposed to develop a tertiary-level energy management system, based on a sugarcane distillery power plant. Lastly, it is important to remark that for each proposed scheme a realistic simulation scenario was presented. Enabling vast discussions about its performance and effectiveness, via graphical observations and metric indices.