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Self-Learning Fuzzy Controllers Based on Temporal Back Propagation

By Jyh-Shing Jang Department and Jyh-shing R. Jang


This paper presents a generalized control strategy that enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near-optimal manner. This methodology, termed temporal back propagation, is model-insensitive in the sense that it can deal with plants that can be represented in a piecewise differentiable format, such as difference equations, neural networks, GMDH, fuzzy models, etc. Regardless of the numbers of inputs and outputs of the plants under consideration, the proposed approach can either refine the fuzzy if-then rules obtained from human experts, or automatically derive the fuzzy if-then rules if human experts are not available. The inverted pendulum system is employed as a testbed to demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired fuzzy controller. 1 Introduction Fuzzy controllers (FC's) have recently found various applications in industry as well as in household appliances. For com..

Year: 1992
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