Neurocontrol of Bicycle using GA with Rough Evaluations.
Tetsuhiko Yamamoto, Takahiro HAKI-AI, Kunihiko Nakazono, Hiroshi Kinjo, Shiro Tamaki · TRANSACTIONS OF THE JAPAN SOCIETY OF MECHANICAL ENGINEERS Series C · 1996
Genetic algorithms (GAs) with rough evaluations can prompt the evolution of neural networks that are able to control unstable dynamic systems. The proposed control method exploits the advantage of GAs that time-varying evaluations can be easily incorporated. First an easy evaluation in GAs induces the appearance of neural networks with controllability. Second, an evaluation of settling time prompts the evolution of neural networks that show high performance. The method is applied to the stable control of a bicycle. Neurocontrol of the steering at direction change causes reverse response like that of a human cyclist.