Analysis on Data-Based Integrated Learning Control for Batch Processes - Intelligent Computing for Sustainable Energy and Environment - page 130

Analysis on Data-Based Integrated Learning Control
for Batch Processes

Li Jia 1 , Luming Cao 1 , and Minsen Chiu 2

1 Shanghai Key Laboratory of Power Station Automation Technology,
Department of Automation, College of Mechatronics Engineering and Automation,
Shanghai University, Shanghai 200072, China
2 Department of Chemical and Biomolecular Engineering,
National University of Singapore, Singapore

Abstract. A novel integrated learning control system is presented in this paper.
It systematically integrates discrete-time (batch-axis) information and conti-
nuous-time (time-axis) information into one uniform frame. More specifically,
the iterative learning controller is designed in the domain of batch-axis, while
an adaptive single neuron predictive controller (SNPC) in the domain of time-
axis. In addition, the convergence and tracking performance of the proposed in-
tegrated learning control system are firstly given rigorous description and proof.
Lastly, to verify the effectiveness of the proposed integrated control system, it
is applied to a benchmark batch process, in comparison with ILC recently de-
veloped.

Keywords: batch processes, integrated learning control system, single neuron
predictive controller (SNPC).

1 Introduction

SINCE batch process satisfies the requirements of the modern market, it have been
widely used in the production of low volume and high value added products, such as
special polymers, special chemicals, pharmaceuticals, and heat treatment processes
for metallic or ceramic products[1]. For the purpose of deriving the maximum benefit
from batch process, it is important to optimize the operation policy of batch process.
Therefore, optimal control of batch process is very significant. However, with strong
nonlinearity and dynamic characteristics, the optimal control of batch process is more
complex than that of continuous process and thus it needs new non-traditional
techniques.
Iterative learning control (ILC) has been used in the optimization control of batch
process because of its repeatability [1], [2]. However, in ILC system, only the batch-
to-batch performance of the batch process is taken for consideration but not the real-
time feedback performance. Thus, ILC is actually an open-loop control from the view
of a separate batch because the feedback-like control just plays role between different
batches. Thus it is difficult to guarantee the performance of the batch process when