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| Record Number | 1989 |
| Reference Type | Journal Article |
| Author(s) | Nakanishi, J.;Farrell, J. A.;Schaal, S. |
| Year | 2005 |
| Title | Composite adaptive control with locally weighted statistical learning |
| Journal/Conference/Book Title | Neural Networks |
| Keywords | adaptive control, statistical learning, composite control law, provably stable, locally weighted regression |
Abstract |
This paper introduces a provably stable learning adaptive control framework with statistical learning. The proposed algorithm employs nonlinear function approximation with automatic growth of the learning network according to the nonlinearities and the working domain of the control system. The unknown function in the dynamical system is approximated by piecewise linear models using a nonparametric regression technique. Local models are allocated as necessary and their parameters are optimized on-line. Inspired by composite adaptive control methods, the proposed learning adaptive control algorithm uses both the tracking error and the estimation error to update the parameters. We first discuss statistical learning of nonlinear functions, and motivate our choice of the locally weighted learning framework. Second, we begin with a class of first order SISO systems for theoretical development of our learning adaptive control framework, and present a stability proof including a parameter projection method that is needed to avoid potential singularities during adaptation. Then, we generalize our adaptive controller to higher order SISO systems, and discuss further extension to MIMO problems. Finally, we evaluate our theoretical control framework in numerical simulations to illustrate the effectiveness of the proposed learning adaptive controller for rapid convergence and high accuracy of control.
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| Notes | clmc |
| URL(s) | http://www-clmc.usc.edu/publications/N/nakanishi-NN2005.pdf
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| Volume | 18 |
| Number | 1 |
| Pages | 71-90 |
| Date | Jan |
| Short Title | Composite adaptive control with locally weighted statistical learning |
| Accession Number | 15649663 |
| Address | ATR Computational Neuroscience Laboratories, Department of Humanoid Robotics and Computational Neuroscience, 2-2 Hikaridai, Seiko-cho, Soraku-gun, Kyoto 619-0288, Japan; Computational Brain Project, ICORP, Japan Science and Technology Agency, Kyoto 619-02 |
| Papers are available as Adobe PDF ".pdf" files. Adobe Reader is available for free for all computer platforms.
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