Research on Neural Network PID Control Algorithm for a Quadrotor

Abstract:

Quadrotor is a kind of popular unmanned aerial vehicle which obtains prime advantages in simple structure, vertically taking off and landing and hovering ability; hence it possesses wide application prospects in reconnaissance and rescue, geological exploration and video surveillance. However, attitude and position control of the quadrotor are challenging tasks because it is an under-actuated system with strong nonlinear, coupling and model uncertainty characteristics. In this paper, the dynamics model and the state space function of the micro-quadrotor are firstly established. Then, a cascade control scheme is proposed to decouple the control system and a multivariate RBF(Radial Basis Function) neural network control PID algorithm is proposed to realize robust control of the quadrotor. This algorithm is not only characterized by simple structure and easy implementation, but also capable of self-adaption and online learning. Simulation results show that the proposed control algorithm performs well in tracking and under disturbances and model uncertainties.

• Periodical: Applied Mechanics and Materials (Volumes 719-720)
• Pages: 346-351
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.719-720.346
• Online since: January 2015
• Authors: Wei Nan Gao, Jia Lu Fan*, Yan Nong Li
• Keywords: Neural Network, Parameter Uncertainties, PID Controller, Quadrotor

References

[1] T. Bresciani, Modelling, Identification and Control of a Quadrotor Helicopter, [D]. Lund Sweden: Department of Automatic Control, (2008).

[2] A. Saif, A. Hiddabi, Quadrotor control using feedback linearization with dynamic extension, [C]. Sharjah, UAE: Proceeding of the 6th International Symposium on Mechatronics and its Applications, (2009).

DOI: 10.1109/isma.2009.5164788

[3] E. Altug, B. Erginer, Modeling and pd control of a quadrotor vtol vehicle, [C]. Istanbul, Turkey: in IEEE Int Conf on Vehicles Symposium, (2007).

DOI: 10.1109/ivs.2007.4290230

[4] S. Bouabdallah, R. Siegwart, Backstepping and sliding-mode techniques applied to an indoor micro-quadrotor, [C]. Barcelona, Spain: Proceeding of the IEEE International Conference on Robotics and Automation, (2005).

DOI: 10.1109/robot.2005.1570447

[5] M. Tarek, B. Abdelaziz, Control of a Quadrotor Mini-Helicopter via Full State Backstepping Technique, [C]. Washington, USA: in IEEE Int Conf on Decision and Control, (2006).

DOI: 10.1109/cdc.2006.377548

[6] S. Bouabdallall, Design and control of quadrotors with application to autonomous flying, [D]. Swiss: University Aboubekr Belkaid, 2007, pp: 171-210.

[7] H. Piao, Z. Wang, H. Zhang, Nonlinear control system of PID neural network based on cooperated particle swarm optimization, [J]. Control Theory & Applications, 2009, 26(12): 1318-1324.

[8] Z. Fang, W. Gao, Adaptive Integral Backstepping Control for a Micro-Quadrotor"[C]. Harbin, China: in IEEE Int, 2 Conf on Intelligent Control and Information Processing. (2011).

DOI: 10.1109/icicip.2011.6008382