Extremum Seeking for Maximizing Higher Derivatives of Unknown Maps in Cascade with Reaction-Advection-Diffusion PDEs

摘要

We present a generalization of the scalar Newton-based extremum seeking algorithm, which maximizes the map’s higher derivatives in the presence of dynamics described by Reaction-Advection-Diffusion (RAD) equations. Basically, the effects of the Partial Differential Equations (PDEs) in the additive dither signals are canceled out using the trajectory generation paradigm. Moreover, the inclusion of a boundary control for the RAD process stabilizes the closed-loop feedback system. By properly demodulating the map output corresponding to the manner in which it is perturbed, the extremum seeking algorithm maximizes the n-th derivative only through measurements of the own map. The Newton-based extremum seeking approach removes the dependence of the convergence rate on the unknown Hessian of the higher derivative, an effort to improve performance and remove limitations of standard gradient-based extremum seeking. In particular, our RAD compensator employs the same perturbation-based (averaging-based) estimate for the Hessian’s inverse of the function to be maximized provided by a differential Riccati equation applied in the previous publications free of PDEs. We prove local stability of the algorithm, maximization of the map sensitivity and convergence to a small neighborhood of the desired (unknown) extremum by means of backstepping transformation, Lyapunov functional and the theory of averaging in infinite dimensions.