Data-Driven Control and Process Monitoring for Industrial Applications-Part i

An iterative data-driven algorithm of controller tuning for nonlinear systems is presented by a team of researchers. The proposed algorithm has solved the optimization problems for nonlinear processes while using linear controllers accounting for operational constraints and employing a quadratic penalty function approach. The researchers have reduced the number of experiments needed to run on real-world processes by means of first-order gradient information obtained from neural network (NN)-based process models.