Learning control for position tracking of active suspension of high-speed AGV via neural network

Autor:	Huanran Xue, E.H.M. Cheung
Rok vydání:	2002
Předmět:	Vehicle dynamics Engineering Automatic control Artificial neural network Control theory business.industry Convergence (routing) Vibration control Control engineering business Active suspension Backpropagation
Zdroj:	Proceedings 1996 IEEE Conference on Emerging Technologies and Factory Automation. ETFA '96.
DOI:	10.1109/etfa.1996.573905
Popis:	This paper proposes a position tracking control approach of AGV's platform using neural network The control problem of AGV's active suspension is first analysed. Then a neural network control scheme for controlling active suspension is presented. The controller adopts a multilayer back-propagation neural network To obtain the fastest possible speed of convergence and to meet the need of real-time control, a prediction-correction method for adjusting learning parameters is developed. Finally, various manoeuvres of the AGV are simulated to evaluate the performance of the controller. The simulation results indicate that the neural network control scheme is feasible. The neuro-controller can accommodate the variation of operational conditions and environment.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::8b6e27d7c5b3c8350417bf48790f7935 https://doi.org/10.1109/etfa.1996.573905 Zobrazit plný text záznamu