Facultad Regional San Francisco
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Item Fault-tolerant predictive control based on linear parameter varying scheme for industrial processes(2021-12-01) Bernardi, Emanuel; Adam, Eduardo J.Background: Process safety is a major concern in the researchers community, both in the past and today. However, the hardware complexity and the involved non-linear dynamics of industrial processes could lead to unsatisfactory behavior of traditional control methods. Methods: To cope with these issues, this paper presents a model-based strategy for fault tolerance in non-lin- ear chemical processes. Specifically, an observer-based fault detection and diagnosis scheme was imple- mented, which generates early and detailed fault information. Therefore, this valuable data was used to compensate the effects induced by actuator and sensor faults throughout the use of an integrated optimiza- tion-based identification and model predictive control technique, which allowed to track a reference even in the presence of faults. Significant Findings: This method reinforces the inherent robustness against faults of linear parameter varying predictive controllers. Moreover, the observers convergence and the controller stability were guaranteed in terms of linear matrix inequalities problems. A simulation based on a typical chemical industrial process, the highly non-linear continuous stirred tank reactor shows that the proposed method can achieve satisfactory performance in fault tolerance.Item Full-order Output Observer Applied to a Linear Parameter Varying System with Unknown Input(IEEE, 2020-12-01) Bernardi, Emanuel; Pipino, Hugo; Adam, Eduardo J.This short work presents the outline of a set of full-order observers, applied to linear parameter varying systems with unknown input. In particular, this approach is used to constructs a strategy to detect and diagnose sensor faults on industrial processes. The observers’ design and its stability conditions are guaranteed in terms of a linear matrix inequalities framework. As a consequence, the main purpose of this paper is to provide a model-based observers’ technique, to detect, isolate and diagnose sensor faults upon non-linear systems. At last, two numerical simulations of typical chemical industrial processes are given to illustrate its implementation and performance.Item Nonlinear Fault-tolerant Model Predictive Control Strategy for Industrial Processes(IEEE, 2020-10-28) Bernardi, Emanuel; Adam, Eduardo J.This short paper presents a strategy to tolerate the income of additive faults in nonlinear chemical processes. For that, an observed-based fault detection and diagnosis scheme is implemented to generate an early and detailed fault information. Then, this valuable knowledge is used to compensate the effects induced by actuators and sensors faults throughout the use of an integrated optimization-based estimation and model predictive control scheme, which allows to track a reference even in presence of faults. A simulation based on a typical chemical industrial process, the highly non-linear continuous stirred tank reactor, is addressed to illustrate the design process and the performance of such approach.Item Fault-tolerant Model-based Predictive Control Applied to Industrial Processes(UNL, 2021-10-21) Bernardi, Emanuel; Adam, Eduardo J.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.