Fault-Tolerant Autolanding Controller Design using Neural Network
In the paper, a neural control scheme is presented for an UAV automatic landing problem under the failure of stuck control surfaces and severe winds. The scheme incorporates a neural controller which augments an existing conventional controller called Baseline Trajectory Following Controller (BTFC). The neural controller is designed using Single Hidden Layer Feedforward Networks (SLFNs) with additive or Radial Basis Function (RBF) hidden nodes in a unified framework. The SLFNs are trained based on the recently proposed neural algorithm named Online Sequential Extreme Learning Machine (OS-ELM). In OS-ELM, the parameters of hidden nodes (the input weights and biases of additive nodes or the centers and impact factors of RBF nodes) are randomly selected and the output weights are analytically determined based on the sequentially arriving data. Performance of the proposed neural control scheme is evaluated on a typical aircraft autolanding with a single stuck failure of left elevator. The simulation results demonstrate good fault tolerant performance of the proposed neural fault tolerant controller.
Single Hidden Layer Feedforward Networks Extreme Learning Machine Fault Tolerant Controller
Jian-Ming Bai Hai-Jun Rong
Optical Direction and Pointing Technique Research Department, Xi’an Institute of Optics and Precisio State Key Laboratory of Strength and Vibration, School of Aerospace, Xi’an Jiaotong University, Xi’a
国际会议
The 31st Chinese Control Conference(第三十一届中国控制会议)
合肥
英文
3017-3022
2012-07-01（万方平台首次上网日期，不代表论文的发表时间）