Robust Adaptive Control with Active Learning for Fed-Batch Process Based on Approximate Dynamic Programming

Abstract

Batch process is often subject to a high degree of uncertainty in raw material quality and other initial feedstock conditions. One of the key objectives in operating a batch process is achieving consistent performance and constraint satisfaction in the presence of these uncertainties This study presents a method for optimal control of a fed-batch process, which can actively and robustly cope with system uncertainty. As in dual control, the method aims to achieve an optimal balance between control actions (exploitation) and probing actions (exploration), leading to improved process performance by actively reducing system uncertainty. An optimal solution of the dual control problem can be found by stochastic dynamic programming but it is computationally intractable in most practical cases. In this study, an approximate dynamic programming (ADP) method for solving the dual control problem is tailored to a batch process which involves non-stationary and nonlinear dynamics Rewards are formulated to maximize a given end objective while satisfying path constraints. Performance of the ADP-based dual controller is tested on a fed batch bioreactor with two uncertain parameters. Copyright (C) 2020 The Authors.