Local Models for data-driven learning of control policies for complex systems
Expert Systems with Applications: An International Journal
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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.