Learning-Based Iterative Modular Adaptive Control for Nonlinear Systems


In this paper we study the problem of adaptive trajectory tracking control for a class of nonlinear systems with structured parametric uncertainties. We propose to use an iterative modular approach: we first design a robust nonlinear state feedback that renders the closed-loop input-to-state stable ISS). Here, the input is considered to be the estimation error of the uncertain parameters, and the state is considered to be the closed loop output tracking error. Next, we propose an iterative adaptive algorithm, where we augment this robust ISS controller with an iterative data-driven learning algorithm to estimate online the parametric uncertainties of the model. We implement this method with two different learning approaches. The first one is a datadriven multi-parametric extremum seeking (MES) method, which guarantees local convergence results, and the second is a Bayesian optimization-based method called Gaussian Process Upper Confidence Bound (GPUCB), which guarantees global results in a compact search set. The combination of the ISS feedback and the data-driven learning algorithms gives a learning-based modular indirect adaptive controller. We show the efficiency of this approach on a two-link robot manipulator numerical example.