Model identification of an unmanned helicopter using ELSSVM

Authors:
Xinjiu Mei;Yi Feng
Affiliations:
School of Control Science and Engineering, Dalian University of Technology, Dalian, Liaoning, China;School of Control Science and Engineering, Dalian University of Technology, Dalian, Liaoning, China
Venue:
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Year:
2013

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Abstract

The dynamic model of unmanned helicopter is a coupled nonlinear system. With respect to the identification problem for this model, extended least squares support vector machine (ELSSVM) is proposed. ELSSVM extends the solution space of structure parameters to improve the convergence performance. Base width of kernel function and regularization parameter of ELSSVM are minimized by differential evolution (DE). As compared to the traditional identification method for helicopter dynamic model, the proposed method omits the linear process and the trained model is closer to the helicopter dynamic model. The data-driven based experiments show that the proposed method takes a short training time and has a high identification accuracy.