Deep learning adaptive Model Predictive Control of Fed-Batch Cultivations

TL;DR

This paper introduces a deep learning-based, convex-structured model predictive control approach for bioprocesses, enabling robust, efficient control of uncertain, nonlinear fed-batch cultivations with online parameter estimation.

Contribution

It presents a novel method combining deep learning, convex decomposition, and tube MPC for robust control of nonlinear bioprocesses with uncertain dynamics.

Findings

The method achieves robust constraint satisfaction in uncertain bioprocesses.

It enables online estimation of unknown model parameters.

The approach is computationally tractable for complex bioprocess control.

Abstract

Bioprocesses are often characterised by nonlinear and uncertain dynamics, posing particular challenges for model predictive control (MPC) algorithms due to their computational demands when applied to nonlinear systems. Recent advances in optimal control theory have demonstrated that concepts from convex optimisation, tube MPC, and differences of convex functions (DC) enable efficient, robust online process control. Our approach is based on DC decompositions of nonlinear dynamics and successive linearisations around predicted trajectories. By convexity, the linearisation errors have tight bounds and can be treated as bounded disturbances within a robust tube MPC framework. We describe a systematic, data-driven method for computing DC model representations using deep learning neural networks with a special convex structure, and explain how the resulting MPC optimisation can be solved using convex programming. For the problem of maximising product formation in a cultivation with uncertain model parameters, we design a controller that ensures robust constraint satisfaction and allows online estimation of unknown model parameters. Our results indicate that this method is a promising solution for computationally tractable, robust MPC of bioprocesses.