LAUSR

In this technical note, a data-driven adaptive performance seeking control (DAPSC) technique is first developed for the optimal output tracking of an aircraft engine with nonlinear and non-analytical dynamics. Firstly, an optimal output tracking description for the aircraft engine is given mathematically, and then the optimal output tracking of aircraft engines is converted by a dynamic programming method, and a Hamilton-Jacoby-Bellman equation is further derived. Secondly, by introducing a function approximation mechanism, a data-driven adaptive optimization algorithm for the Hamilton-Jacoby-Bellman equation is designed. The developed algorithm can update the optimal control law online, and convergence results of the optimal control law are presented. Finally, by comparing DAPSC scheme and proportional-integral-derivative (PID) method, simulation results of an aircraft engine component level model are shown to reveal the potential of the designed technique.