# Dual-control based approach to batch process operation under uncertainty based on optimality-conditions parameterization

## TL;DR

This paper introduces a computationally efficient dual-control scheme for batch process operation under uncertainty, balancing robust performance and parameter learning through a scenario-based adaptive predictive control approach.

## Contribution

It proposes a novel parameterized optimality-condition approach within a dual-control framework, reducing conservativeness and computational burden compared to existing methods.

## Key findings

- Effective control of batch membrane filtration demonstrated
- Enhanced robustness and learning capability shown in case study
- Reduced computational complexity compared to traditional approaches

## Abstract

This paper presents a scheme for dual robust control of batch processes under parametric uncertainty. The dual-control paradigm arises in the context of adaptive control. A trade-off should be decided between the control actions that (robustly) optimize the plant performance and between those that excite the plant such that unknown plant model parameters can be learned precisely enough to increase the robust performance of the plant. Some recently proposed approaches can be used to tackle this problem, however, this will be done at the price of conservativeness or significant computational burden. In order to increase computational efficiency, we propose a scheme that uses parameterized conditions of optimality in the adaptive predictive-control fashion. The dual features of the controller are incorporated through scenario-based (multi-stage) approach, which allows for modeling of the adaptive robust decision problem and for projecting this decision into predictions of the controller. The proposed approach is illustrated on a case study from batch membrane filtration.