Multi-Parametric Extremum Seeking-based Auto-Tuning for Robust Input-Output Linearization Control
For control engineers, this work provides an auto-tuning approach for robust nonlinear controllers, though it is incremental as it combines existing methods.
This paper addresses iterative feedback gain tuning for nonlinear systems with uncertainties, combining robust input-output linearization with model-free multi-parametric extremum seeking for auto-tuning. Numerical tests on a mechatronics example demonstrate the method's performance.
We study in this paper the problem of iterative feedback gains tuning for a class of nonlinear systems. We consider Input-Output linearizable nonlinear systems with additive uncertainties. We first design a nominal Input-Output linearization-based 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 (MES) control to iteratively auto-tune the feedback gains. We analyze the stability of the whole controller, i.e. robust nonlinear controller plus model-free learning algorithm. We use numerical tests to demonstrate the performance of this method on a mechatronics example.