Abstract
A novel approach to adaptive control architecture, which formulates the adaptive behavior of human mathematically, is presented. It is made up of the two main modules: the Controller constructed by fuzzy neural network (FNN), and the Adapter composed of three components: the Performance Evaluator (PE), the Action Searcher (AS), and the Rule Constructer (RC). The Controller and Adaptor perform the off-line and on-line learning to learn control strategy and to adapt variant environments. The PE evaluates the system's performance. If the control effect is satisfactory, the Controller keeps on its assignment. Otherwise, the genetic algorithm (GA)-based AS will explore the new control actions. Then, the RC transforms these actions to the fuzzy rules and updates the corresponding fuzzy rules in Controller. An example of the path planning of a mobile robot is to demonstrate this presented method.
| Original language | English |
|---|---|
| Pages (from-to) | 347-352 |
| Number of pages | 6 |
| Journal | Proceedings of the IEEE International Conference on Systems, Man and Cybernetics |
| Volume | 1 |
| Publication status | Published - 2001 |
| Event | 2001 IEEE International Conference on Systems, Man and Cybernetics - Tucson, AZ, United States Duration: 2001 Oct 7 → 2001 Oct 10 |
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Hardware and Architecture