Implementation of a learning fuzzy controller

This article describes our efforts at designing and implementing a practical learning fuzzy controller using inexpensive hardware. The controller engages basic control concepts and system-independent learning rules to enable it to adapt in real time to unknown plants even when it starts with a vacuous initial control policy. The controller remains dormant when the plant is operating satisfactorily and autonomously initiates online adaptation in real time when adverse performance is observed. The Intel-8031-based hardware implementation is geared for extensibility, robustness, and fault tolerance. Limited plant-dependent information is incorporated to tailor the hardware to applications. The design produces learning rates exceeding 200 reinforcements per second. The controller thus is able to learn to control unknown plants in real time even while it is controlling them. Physical experiments indicate that the learning fuzzy controller can rapidly and effectively deal with variations in plant characteristics, compensate for wear and tear, and handle disturbances and noise.<
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