Iterative Data-Driven Tuning of Controllers for Nonlinear Systems With Constraints

This paper presents a new iterative data-driven algorithm (IDDA) for the experiment-based tuning of controllers for nonlinear systems. The proposed IDDA solves the optimization problems for nonlinear processes while using linear controllers accounting for operational constraints and employing a quadratic penalty function approach. The search algorithm employs first-order gradient information obtained from neural-network-based process models to reduce the number of experiments needed to run on real-world processes. A data-driven controller tuning for the angular position control of a nonlinear aerodynamic system is used as an experimental case study to validate the proposed IDDA.