Online deep neural network-based feedback control of a Lutein bioprocess

An online adaptive deep neural network (DNN) scheme has been introduced for the tracking control of a nonlinear bioprocess with uncertain internal dynamics. First, a detailed controllability analysis is conducted for the Lutein bioprocess to represent the bioprocess as a nonlinear system in affine form. Next, a controller consisting of a DNN-based function approximator is designed for the nonlinear Lutein production bioprocess. It is demonstrated that closed-loop tracking control of a bioprocess for a desired yield profile is possible only with two inputs. The set point trajectory to yield maximum Lutein production is shown by the proposed online adaptive deep NN controller. The proposed controller exhibits self-learning capability under closed loop condition, due to the online learning phase. In other words, no explicit offline learning phase is required and online learning is preferred due to lack of a priori training data for approximating complex nonlinear functions. Simulation results are provided to confirm the performance of the proposed approach. (c) 2020 Elsevier Ltd. All rights reserved.

Automated summary

An online adaptive deep neural network (DNN) scheme is introduced for the tracking control of a nonlinear bioprocess, demonstrating closed-loop tracking control for a desired yield profile can be achieved with only two inputs. The proposed controller exhibits self-learning capability under closed loop conditions and does not require an offline learning phase.