Constrained data-driven controller tuning for nonlinear systems

This paper proposes a data-driven algorithm that solves an optimal control problem by iteratively tuning the controller. The data-driven algorithm solves the optimization problem for a nonlinear process with a linear controller, accounting for operational constraints and employing an interior-point barrier (IPB) algorithm. The search process in the IPB algorithm requires first-order information which is generated using identified models via neural networks in order to reduce the number of experiments. A case study which deals with the angular position control of a nonlinear aerodynamic system is included to validate the new algorithm by simulation results.