Title :
Self-Organizing Approximation-Based Control for Higher Order Systems
Author :
Zhao, Yuanyuan ; Farrell, Jay A.
Author_Institution :
Univ. of California, Riverside
fDate :
7/1/2007 12:00:00 AM
Abstract :
Adaptive approximation-based control typically uses approximators with a predefined set of basis functions. Recently, spatially dependent methods have defined self-organizing approximators where new locally supported basis elements were incorporated when existing basis elements were insufficiently excited. In this paper, performance-dependent self-organizing approximators will be defined. The designer specifies a positive tracking error criteria. The self-organizing approximation-based controller then monitors the tracking performance and adds basis elements only as needed to achieve the tracking specification. The method of this paper is applicable to general th-order input-state feedback linearizable systems. This paper includes a complete stability analysis and a detailed simulation example.
Keywords :
adaptive control; self-adjusting systems; stability; adaptive approximation; feedback linearizable systems; higher order systems; performance-dependent self-organizing approximators; self-organizing approximation-based control; stability analysis; tracking specification; Adaptive control; Adaptive systems; Analytical models; Approximation error; Automatic control; Control systems; Feedback; Function approximation; Stability analysis; Weight control; Adaptive nonlinear control; locally weighted learning; self-organizing approximation-based control; Algorithms; Computer Simulation; Decision Support Techniques; Feedback; Models, Theoretical; Neural Networks (Computer); Nonlinear Dynamics;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2007.899217