A control scheme for an active suspension system is developed using fuzzy logic. In contrast to the trial-and-error approach used in most fuzzy logic control (FLC) development, the focus of this research work is on developing systematic learning algorithms that can be used to fine-tune an FLC. A per
Fuzzy drive expert system for an automobile
β Scribed by Mikio Maeda
- Publisher
- Elsevier Science
- Year
- 1995
- Tongue
- English
- Weight
- 1024 KB
- Volume
- 4
- Category
- Article
- ISSN
- 1069-0115
No coin nor oath required. For personal study only.
β¦ Synopsis
This paper deals with a fuzzy drive expert system for an auto-cruise car. This system consists of four rule sets such as environment recognition rules, driving control rules, learning-evaluation rules, and management. meta-rules. The structure c'f those rules is hierarchical. The fuzzy drive expert system is structured with five units; the distance extraction and image processing unit, the environment recognition unit, the control unit, the learning-evaluation unit, the I/O unit, the knowledge rule base, and the man-machine interface.
Each unit drives the fuzzy production rules which are described by sentence and symbols based on the if-then type format. Antecedent parts and consequent parts of those rules include the fuzzy words such as big, positive, wide, short, and so on. On the basis of the recognized result of environment., the control unit manipulates the steering and the throttle valve (or fuel injectors, brake pressure) for direction control and speed control of vehicle.
Th.e vehicle drive controls on the straight road and the corner are simulated on the digital computer. The overtaking control, the tracking control, and the avoid,snce control of obstacles are successful and smoothable.
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