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.