Adaptive fuzzy systems for backing up a truck-and-trailer

• Published in: IEEE transactions on neural networks Vol. 3; no. 2; pp. 211 - 223
• Main Authors: Kong, S.-G., Kosko, B.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.03.1992; Institute of Electrical and Electronics Engineers
• Subjects: Adaptative systems; Adaptive systems; Applied sciences; Backpropagation algorithms; Computational modeling; Computer science; control theory; systems; Control system synthesis; Control theory. Systems; Exact sciences and technology; Fuzzy control; Fuzzy systems; Neural networks; Robustness; System testing; Training data; Learning; Backpropagation; Fuzzy system; Adaptive system; Control system; Robustness; Neural network
• ISSN: 1045-9227; 1941-0093
• DOI: 10.1109/72.125862

Fuzzy control systems and neural-network control systems for backing up a simulated truck, and truck-and-trailer, to a loading dock in a parking lot are presented. The supervised backpropagation learning algorithm trained the neural network systems. The robustness of the neural systems was tested by removing random subsets of training data in learning sequences. The neural systems performed well but required extensive computation for training. The fuzzy systems performed well until over 50% of their fuzzy-associative-memory (FAM) rules were removed. They also performed well when the key FAM equilibration rule was replaced with destructive, or 'sabotage', rules. Unsupervised differential competitive learning (DCL) and product-space clustering adaptively generated FAM rules from training data. The original fuzzy control systems and neural control systems generated trajectory data. The DCL system rapidly recovered the underlying FAM rules. Product-space clustering converted the neural truck systems into structured sets of FAM rules that approximated the neural system's behavior.< >