Learning-Based Modular Indirect Adaptive Control for a Class of Nonlinear Systems

Mouhacine Benosman; Amir-massoud Farahmand; Meng Xia

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arXiv · 1509.07860

Learning-Based Modular Indirect Adaptive Control for a Class of Nonlinear Systems

Mouhacine Benosman·Amir-massoud Farahmand·Meng Xia

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Abstract

We study in this paper the problem of adaptive trajectory tracking control for a class of nonlinear systems with parametric uncertainties. We propose to use a modular approach, where we first design a robust nonlinear state feedback which renders the closed loop input-to-state stable (ISS), where 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 augment this robust ISS controller with a model-free learning algorithm to estimate the model uncertainties. We implement this method with two different learning approaches. The first one is a model-free multi-parametric extremum seeking (MES) method and the second is a Bayesian optimization-based method called Gaussian Process Upper Confidence Bound (GP-UCB). The combination of the ISS feedback and the learning algorithms gives a learning-based modular indirect adaptive controller. We show the efficiency of this approach on a two-link robot manipulator example.

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BibTeX RIS

Mouhacine Benosman, Amir-massoud Farahmand, Meng Xia. 2015-09-25. Learning-Based Modular Indirect Adaptive Control for a Class of Nonlinear Systems. https://arxiv.org/abs/1509.07860

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