Application of iterative learning control to an exothermic semibatch chemical reactor

Abstract

This work focuses on the temperature control of a semibatch chemical reactor used for fine chemicals production. Such a reactor is equipped with a heating/cooling system composed of different thermal fluids. Without extensive modeling investigations, a feedback-feedforward control strategy is proposed for ensuring the tracking performance of the desired temperature profile. Such a strategy is derived from a family of the iterative learning control (ILC) algorithms named batch model predictive control (BMPC). Learning is achieved without requiring a detailed knowledge of the system, which may be affected by unknown but repetitive disturbances. The learning control solution is based on the minimization of a linear quadratic cost function. The synthesis of the proposed strategy is studied, and improvements of the algorithm features are proposed. First, guaranteed convergence of the algorithm is illustrated in a few experimental runs. Second, some practical considerations for the removal of high-frequency disturbance effects are outlined to improve the achieved performance. Third, a robust supervisory control procedure is employed to choose the right fluid and to reduce the superfluous fluid changeovers, mainly when different fluids are available. Finally, experimental results are presented to illustrate the practical appeal and effectiveness of the proposed scheme.

Two-dimensional generalized predictive control (2D-GPC) scheme for the batch processes with two-dimensional (2D) dynamics

“…With the rapid development of the technology and fast changing requirements of the market, the batch processing has been a good candidate to produce high-value added products at lower cost Ahn et al (2007). Typical batch processes include robot arms in the manufacturing streamlines (Moore 1999;Gopinath and Kar 2004), batch reactions in the fine chemicals (Mezghani et al 2002;Youssef et al 2003;Xiong and Zhang 2003), biofermentation in the pharmaceutical industries (Ben and Youssef 2012), injection molding in the plastics processing (Gao et al 2001;, and the semiconductor production processes (Solomon et al 2001;Su et al 2007). …”

A survey on iterative learning control for nonlinear systems

“…In the past 25 years, ILC has been widely studied in theory (Moore 1993;Bien and Xu 1998;Moore and Xu 2000;Xu and Tan 2003;Bristow, Tharayil, and Allyne 2006;Ahn, Moore, and Chen 2007), and to a certain extent applied in practice (Arimoto, Kawamura, and Miyazaki 1984;Longman 2000;Mezghani et al 2002;Norrlo¨f 2002;Yang, Lee, Ahn, and Lee 2003;Xu, Panda, and Lee 2009). In terms of system nonlinearities, ILC problems can be classified into four categories: (1) linear ILC for linear systems, (2) nonlinear ILC for linear systems, (3) linear ILC for global Lipschitz continuous (GLC) systems and (4) nonlinear ILC for local Lipschitz continuous (LLC) systems.…”

PID and EPID types of Iterative Learning Control based on Evolutionary Algorithm

“…The input in an ILC system is updated trail by trail, and then the tracking accuracy of system output is improved progressively. ILC can be found in many applications such as robotics, semiconductors, chemical process, and other industrial manipulation [3][4][5][6].…”