Abstract
ABSTRACTThis paper studies the iterative learning fault-tolerant control (ILFTC) problem for networked batch processes with event-triggered transmission strategy and data dropouts. During the transmission of input signal, the event-triggered mechanism is adopted to reduce the number of updated data items. The data dropouts are assumed to obey the Bernoulli random binary distribution. The objective of this paper is to design a state feedback controller such that the system is fault-tolerant and satisfies the robust performance requirement. By combining 2D stochastic system theory and linear matrix inequality (LMI) technique, some sufficient conditions are given to ensure the existence of the designed controller. Finally, an example of nozzle pressure control is utilized to verify the availability of the proposed method.
Highlights
Over the past several decades, as a class of production modes with low-volume and high-value, batch processes have been widely applied in manufacturing and chemical industries (Korovessi & Linninger, 2006; Reklaitis & Sunol, 1996)
This paper studies the iterative learning fault-tolerant control (ILFTC) problem for networked batch processes with event-triggered transmission strategy and data dropouts
By using the designed ILC scheme, the batch process can be modelled as a 2D system in Shi et al (2005a), the iterative learning controller design, which is the main work in this paper, has been transformed into a robust stabilization problem for 2D system
Summary
Over the past several decades, as a class of production modes with low-volume and high-value, batch processes have been widely applied in manufacturing and chemical industries (Korovessi & Linninger, 2006; Reklaitis & Sunol, 1996). By using the designed ILC scheme, the batch process can be modelled as a 2D system in Shi et al (2005a), the iterative learning controller design, which is the main work in this paper, has been transformed into a robust stabilization problem for 2D system. The multistage batch process with uncertainties has been converted to a 2D switched system in Wang et al (2017), and a hybrid iterative learning control scheme has been presented by using the average dwell time method to ensure the 2D robust stability. Motivated by the above discussion, we aim to design an event-triggered iterative learning fault-tolerant controller for a class of networked batch processes with data dropouts and sensor faults. (1) Based on the 2D Fornasini-Marchesini model, the event-triggered iterative learning fault-tolerant control strategy is proposed for networked batch processes with sensor faults.
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