Attitude Control of a Quadrotor Using Model Free Based Sliding Model Controller

In this paper, a model free based sliding mode control (MFSMC) is developed to realize the attitude control of a quadrotor whose model contains the system uncertainties and external disturbances. Firstly, a model free based intelligent PD controller (iPD) which contains time-delay estimation is designed. The problem is that although the proposed design of the controller does not need to know the system model, but it exists the unknown dynamics estimation error. Then for the considered problem, the MFSMC is proposed. This referred controller which consists of three parts: the equivalent control law, exponent reaching law and correction control law, ensures the trajectories tracking. Finally, the proposed MFSMC method is validated on the quadrotor with system uncertainties and external disturbances and compared with classic PD and iPD control. The corresponding simulation results demonstrate the effectiveness and performance of the MFSMC controller against the controllers of iPD and PD.

[1]  Felix Mora-Camino,et al.  Attitude control of a quadrotor aircraft using LQR state feedback controller with full order state observer , 2013, The SICE Annual Conference 2013.

[2]  Brahim Cherki,et al.  Robust control for attitude tracking problem for a quadrotor unmanned aerial vehicle , 2013, 3rd International Conference on Systems and Control.

[3]  Christian Vasseur,et al.  Adaptive optimal trajectory tracking control of nonlinear affine in control system with unknown internal dynamics , 2014, Proceedings of the 33rd Chinese Control Conference.

[4]  Mehmet Önder Efe,et al.  Neural Network Assisted Computationally Simple PI$^\lambda$D$^\mu$ Control of a Quadrotor UAV , 2011, IEEE Transactions on Industrial Informatics.

[5]  LI Rong-ming Research on Control Algorithm of Microquadrotor Aircraft , 2011 .

[6]  Qiang Zhan,et al.  Control system design and experiments of a quadrotor , 2012, 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[7]  Cédric Join,et al.  Stability margins and model-free control: A first look , 2014, 2014 European Control Conference (ECC).

[8]  Abdelhamid Rabhi,et al.  Model-free control of a quadrotor vehicle , 2014, 2014 International Conference on Unmanned Aircraft Systems (ICUAS).

[9]  Roland Siegwart,et al.  Backstepping and Sliding-mode Techniques Applied to an Indoor Micro Quadrotor , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[10]  Kamal Youcef-Toumi,et al.  Control of robot manipulators using time delay , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[11]  Michel Fliess,et al.  Model-free control of dc/dc converters , 2010, 2010 IEEE 12th Workshop on Control and Modeling for Power Electronics (COMPEL).

[12]  Cédric Join,et al.  Model-free control and intelligent PID controllers: towards a possible trivialization of nonlinear control? , 2009, ArXiv.

[13]  Claudia-Adina Dragos,et al.  Model-free tuning solution for sliding mode control of servo systems , 2014, 2014 IEEE International Systems Conference Proceedings.

[14]  Steven Lake Waslander,et al.  Multi-agent quadrotor testbed control design: integral sliding mode vs. reinforcement learning , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[15]  Wei Wang,et al.  Attitude and Altitude Controller Design for Quad-Rotor Type MAVs , 2013 .

[16]  Kamal Youcef-Toumi,et al.  Application of decentralized time-delay controller to robot manipulators , 1989, Proceedings, 1989 International Conference on Robotics and Automation.