Parameter identification for a quadrotor helicopter using PSO

For some real systems, physical parameters are generally unknown and cannot be measured precisely. Moreover, a multiple degrees of freedom unmanned aerial vehicle (UAV) usually has many physical parameters, and the method of obtaining them is challenging. Much research has been done in this area and a lot of methods have been applied to several UAVs. In this paper, we use particle swarm optimization (PSO) swarm intelligence algorithm to identify the inertia physical parameters of quadrotor helicopter. To primarily reduce complexity of the problem and simplify the design of parameter identification scheme, the dynamics of the whole system is decomposed into two subsystems by model transformation. Then using input and output data which come from model test, the model parameters of quadrotor helicopter are identified successfully. The simulating results validate that this scheme has not only good performance on convergence speed, but also low identification error.

[1]  Zhen Ji,et al.  A novel intelligent particle optimizer for global optimization of multimodal functions , 2007, 2007 IEEE Congress on Evolutionary Computation.

[2]  Visakan Kadirkamanathan,et al.  Stability analysis of the particle dynamics in particle swarm optimizer , 2006, IEEE Transactions on Evolutionary Computation.

[3]  R. V. Jategaonkar,et al.  Aerodynamic Parameter Estimation from Flight Data Applying Extended and Unscented Kalman Filter , 2010 .

[4]  Ioan Cristian Trelea,et al.  The particle swarm optimization algorithm: convergence analysis and parameter selection , 2003, Inf. Process. Lett..

[5]  R. V. Jategaonkar,et al.  Aerodynamic parameter estimation from flight data applying extended and unscented Kalman filter , 2006 .

[6]  Wang Xiao-peng.  Aerodynamic parameter identification of flight vehicles based on adaptive genetic algorithm , 2003 .

[7]  Meng Li,et al.  Improved particle swarm optimizer based on adaptive random learning approach , 2009, 2009 IEEE Congress on Evolutionary Computation.

[8]  Wei Zhang,et al.  Modeling and aerodynamic analysis of a ducted-fan micro aerial vehicle , 2012, 2012 Proceedings of International Conference on Modelling, Identification and Control.

[9]  Jun Zhang,et al.  Adaptive Particle Swarm Optimization , 2008, ANTS Conference.

[10]  Heinrich H. Bülthoff,et al.  Modeling and control of a quadrotor UAV with tilting propellers , 2012, 2012 IEEE International Conference on Robotics and Automation.

[11]  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.

[12]  Ümit Özgüner,et al.  Sliding mode control of a class of underactuated systems , 2008, Autom..

[13]  Vincent A Akpan,et al.  Nonlinear model identification and adaptive model predictive control using neural networks. , 2011, ISA transactions.

[14]  Umberto Soverini,et al.  Maximum likelihood identification of noisy input-output models , 2007, Autom..

[15]  Zhongke Shi,et al.  Frequency-Domain GTLS Identification Combined with Time-Frequency Filtering for Flight Flutter Modal Parameter Identification , 2006 .

[16]  Francesco Alonge,et al.  Least squares and genetic algorithms for parameter identification of induction motors , 2001 .

[17]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[18]  Chunshien Li,et al.  Complex Neuro-Fuzzy Self-learning Approach to Function Approximation , 2010, ACIIDS.

[19]  Xusheng Lei,et al.  A Linear Domain System Identification for Small Unmanned Aerial Rotorcraft Based on Adaptive Genetic Algorithm , 2010 .

[20]  Zafer Bingul,et al.  Dynamic identification of Staubli RX-60 robot using PSO and LS methods , 2011, Expert Syst. Appl..

[21]  Heidar Ali Talebi,et al.  A stable neural network-based observer with application to flexible-joint manipulators , 2006, IEEE Transactions on Neural Networks.

[22]  Leonid M. Fridman,et al.  Super twisting control algorithm for the attitude tracking of a four rotors UAV , 2012, J. Frankl. Inst..