Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10326110 | Neural Networks | 2005 | 20 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Jun Nakanishi, Jay A. Farrell, Stefan Schaal,