Abstract
This paper considers the solution of a real-time optimization problem using adaptive extremum seeking control for a class of unknown discrete-time nonlinear systems. It is assumed that the equations describing the dynamics of the nonlinear system and the cost function to be minimized are unknown and that the objective function is measured. The main contribution of the paper is to formulate the extremum-seeking problem as a time-varying discrete-time estimation problem. The proposed approach is applied in the design of nonlinear model predictive control algorithms where the extremum-seeking controller is used to perform the real-time optimization of the MPC. A simulation study and an experimental study is presented that demonstrates the effectiveness of the proposed technique.
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