Robust Economic Model Predictive Control For Continuous Fermentation Processes

We propose an Economic Model Predictive Control (EMPC) framework that is robust to model structure error. The approach integrates parameter estimation with gradient correction to improve controller performance. At each sampling time, the algorithm performs parameter estimation over past samples, followed by a gradient correction step that updates model parameters to match the gradients of the model and plant using transient measurements. To match the gradients while maintaining model accuracy, a correction term is added, which ensures an upper bound on the model error. The approach is validated on a continuous penicillin production process subject to model-plant mismatch. Results demonstrate that the proposed EMPC with gradient correction drives the process closer to the true plant optimum values and achieves better convergence to optimal operating conditions than a similar EMPC without gradient correction.