Abstract In this paper, in order to linearize the nonlinear model of batch processes, a batch process is modeled by just in time learning (JITL) method and dynamic updating locally linear model parameters along batch cycle is also proposed. Considering that the error between the actual model and the prediction model, iterative learning control strategy based on a quadratic performance criterion is proposed and the system controller is solved by linear matrix inequality (LMI) method. Moreover the convergence of tracking error based on ILC is also analyzed and the conditions of convergence is proposed. In order to satisfy the condition, a novel ILC method based on JITL is proposed. To improve the convergence speed, this paper further uses of ILC based on nominal trajectory. As a result, the simulation results show that the system has better accuracy of output. It provides a new way for the control of batch processes.
An LMI Approach to Iterative Learning Control Based on JITL for Batch Processes
01.01.2017
11 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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