کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
708809 | 892034 | 2016 | 6 صفحه PDF | دانلود رایگان |
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 adaptive approach, where we first design a robust nonlinear state feedback which renders the closed loop input-to-state stable (ISS). 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. We augment this robust ISS controller with a model-free learning algorithm to estimate the model uncertainties. We implement this method with 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 test the efficiency of this approach on a two-link robot manipulator example, under noisy measurements conditions.
Journal: IFAC-PapersOnLine - Volume 49, Issue 13, 2016, Pages 253–258