Multi-parametric extremum seeking-based iterative feedback gains tuning for nonlinear control

We study in this paper the problem of iterative feedback gains auto-tuning for a class of nonlinear systems. For the class of input-output linearizable nonlinear systems with bounded additive uncertainties, we first design a nominal input-output linearization-based robust controller that ensures global uniform boundedness of the output tracking error dynamics. Then, we complement the robust controller with a model-free multi-parametric extremum seeking control to iteratively auto-tune the feedback gains. We analyze the stability of the whole controller, that is, the robust nonlinear controller combined with the multi-parametric extremum seeking model-free learning algorithm. We use numerical tests to demonstrate the performance of this method on a mechatronics example. Copyright (c) 2016 John Wiley & Sons, Ltd.