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Robotic airship mission path-following control based on ANN and human operator’s skill

Department of Precision Mechanical Engineering, Shanghai University, P.O. Box 108, No. 149, Yanchang Rd., 200072 Shanghai, P.R. China, mrjjrao{at}yahoo.com.cn

Zhenbang Gong

Department of Precision Mechanical Engineering, Shanghai University, P.O. Box 108, No. 149, Yanchang Rd., 200072 Shanghai, P.R. China

Jun Luo

Department of Precision Mechanical Engineering, Shanghai University, P.O. Box 108, No. 149, Yanchang Rd., 200072 Shanghai, P.R. China

Zhen Jiang

Shanghai Arrow-MEMS Technology Co. Ltd., P.O. Box 108, No. 149, Yanchang Rd., 200072 Shanghai, P.R. China

Shaorong Xie

Shanghai Arrow-MEMS Technology Co. Ltd., P.O. Box 108, No. 149, Yanchang Rd., 200072 Shanghai, P.R. China

Wufa Liu

College of Mechanical Engineering, Zhengzhou University, 450002 Zhengzhuo, P.R. China

Robotic airships have numerous low-speed and low-altitude application potentials. Mission path following is one such application, which, however, presents an autonomy challenge. In this paper, a yawing controller, which is based on artificial neural network (ANN) and human operator skills, is proposed for mission path following of robotic airships. First, the path-following errors based on the operator’s point of view are discussed. Then, a data acquisition system is designed to collect the flight data under manual control, and the data are then processed and used for offline training and validation of a multilayer feed-forward neural network. Finally, the trained neural network is reconstructed in the flight control system for yawing control, and the experimental results confirm the effectiveness of this method. It is also shown that the ANN controller is robust even with wind disturbance.

Key Words: airship • artificial neural network (ANN) • flight control • human strategy • mission path following

Transactions of the Institute of Measurement and Control, Vol. 29, No. 1, 5-15 (2007)
DOI: 10.1177/0142331207075608


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