TR2016-055

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


    •  Benosman, M.; Farahmand, A.-M., "Learning-Based Modular Indirect Adaptive Control for a Class of Nonlinear Systems", American Control Conference (ACC), DOI: 10.1109/ACC.2016.7525001, July 2016, pp. 733-738.
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      • @inproceedings{Benosman2016jul1,
      • author = {Benosman, M. and Farahmand, A.-M.},
      • title = {Learning-Based Modular Indirect Adaptive Control for a Class of Nonlinear Systems},
      • booktitle = {American Control Conference (ACC)},
      • year = 2016,
      • pages = {733--738},
      • month = jul,
      • doi = {10.1109/ACC.2016.7525001},
      • url = {http://www.merl.com/publications/TR2016-055}
      • }
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  • Research Areas:

    Control, Optimization, Robotics


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: we first design a robust nonlinear state feedback that renders the closed loop inputto-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 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 optimizationbased method called Gaussian Process Upper Confidence Bound (GPUCB). 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.