Neural network adaptive inverse model control method for quadrotor UAV

For the highly nonlinear, strong coupling and underactuated, an effective controller for quadrotor unmanned aerial vehicle (UAV) is not easy to obtain, especially in conditions of the parameters variations and external disturbance. In this paper, an adaptive inverse model control method (AIMCM) is proposed for this plant. Two BP networks are used in control system to achieve the model identification and control. Both offline trained and online learned are used to ensure the fast learning and the robustness. The convergence of the learning algorithm is proved based on Lyapunov function. At last, a quadrotor UAV control simulation based on the full model shows the superiority and robustness of the control system.

[1]  Wei Wu,et al.  Deterministic convergence of an online gradient method for BP neural networks , 2005, IEEE Transactions on Neural Networks.

[2]  Yongxian Song,et al.  Digital implementation of neural network inverse control for induction motor based on DSP , 2010, 2010 2nd International Conference on Future Computer and Communication.

[3]  Zhihao Cai,et al.  Self-tuning PID control design for quadrotor UAV based on adaptive pole placement control , 2013, 2013 Chinese Automation Congress.

[4]  Wang Yaonan,et al.  RBF Networks-Based Adaptive Inverse Model Control System for Electronic Throttle , 2010, IEEE Transactions on Control Systems Technology.

[5]  Holger Voos,et al.  Nonlinear control of a quadrotor micro-UAV using feedback-linearization , 2009, 2009 IEEE International Conference on Mechatronics.

[6]  Jiangjiang Wang,et al.  Adaptive PID control with BP neural network self-tuning in exhaust temperature of micro gas turbine , 2008, 2008 3rd IEEE Conference on Industrial Electronics and Applications.

[7]  Robert M. Sanner,et al.  Gaussian Networks for Direct Adaptive Control , 1991, 1991 American Control Conference.

[8]  Frank L. Lewis,et al.  Backstepping Approach for Controlling a Quadrotor Using Lagrange Form Dynamics , 2009, J. Intell. Robotic Syst..

[9]  Sangchul Won,et al.  PID based sliding mode controller design for the micro quadrotor , 2013, 2013 13th International Conference on Control, Automation and Systems (ICCAS 2013).

[10]  Zhang Lei,et al.  BP neural network control of single inverted pendulum , 2013, Proceedings of 2013 3rd International Conference on Computer Science and Network Technology.

[11]  Snehasis Mukhopadhyay,et al.  Adaptive control using neural networks and approximate models , 1997, IEEE Trans. Neural Networks.

[12]  B. Widrow,et al.  Adaptive inverse control , 1987, Proceedings of 8th IEEE International Symposium on Intelligent Control.

[13]  Alejandro Ramirez-Serrano,et al.  Adaptive fuzzy control for a quadrotor helicopter robust to wind buffeting , 2011, J. Intell. Fuzzy Syst..

[14]  Shuzhi Sam Ge,et al.  Adaptive MNN control for a class of non-affine NARMAX systems with disturbances , 2004, Syst. Control. Lett..

[15]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[16]  Jianfeng Zhang,et al.  The identification research of airplane target based on BP neural network , 2010, 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems.