Abstract
S-surface control has proven to be an effective means for motion control of underwater autonomous vehicles (AUV). However there are still problems maintaining steady precision of course due to the constant need to adjust parameters, especially where there are disturbing currents. Thus an intelligent integral was introduced to improve precision. An expert S-surface control was developed to tune the parameters on-line, based on the expert system, it provides S-surface control according to practical experience and control knowledge. To prevent control output over-compensation, a fuzzy neural network was included to adjust the production rules to the knowledge base. Experiments were conducted on an AUV simulation platform, and the results show that the expert S-surface controller performs better than an S-surface controller in environments with currents, producing good steady precision of course in a robust way.
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