Design of Attitude Control System for UAV Based on Feedback Linearization and Adaptive Control

Attitude dynamic model of unmanned aerial vehicles (UAVs) is multi-input multioutput (MIMO), strong coupling, and nonlinear. Model uncertainties and external gust disturbances should be considered during designing the attitude control system for UAVs. In this paper, feedback linearization and model reference adaptive control (MRAC) are integrated to design the attitude control system for a fixed wing UAV. First of all, the complicated attitude dynamic model is decoupled into three single-input single-output (SISO) channels by input-output feedback linearization. Secondly, the reference models are determined, respectively, according to the performance indexes of each channel. Subsequently, the adaptive control law is obtained using MRAC theory. In order to demonstrate the performance of attitude control system, the adaptive control law and the proportional-integral-derivative (PID) control law are, respectively, used in the coupling nonlinear simulation model. Simulation results indicate that the system performance indexes including maximum overshoot, settling time (2% error range), and rise time obtained by MRAC are better than those by PID. Moreover, MRAC system has stronger robustness with respect to the model uncertainties and gust disturbance.

[1]  Mihai Lungu,et al.  Adaptive backstepping flight control for a mini‐UAV , 2013 .

[2]  Haiyang Chao,et al.  Roll-channel fractional order controller design for a small fixed-wing unmanned aerial vehicle , 2010 .

[3]  Chao Liu,et al.  Design and Verification the Simulation System of UAS Flight Control Considering the Effects of Time Delay , 2013 .

[4]  Elisa Capello,et al.  Design and Validation of a L1 Adaptive Controller for a mini-UAV Autopilot , 2012 .

[5]  Chunxia Lu,et al.  Design of the Platform for A UAV Flight Control System Based on STM32 , 2013 .

[6]  Youmin Zhang,et al.  Quad-Rotor UAV: High-Fidelity Modeling and Nonlinear PID Control , 2010 .

[7]  Takeshi Yamasaki,et al.  Integrated guidance and autopilot design for a chasing UAV via high-order sliding modes , 2012, J. Frankl. Inst..

[8]  Alan F. Lynch,et al.  Experimental Validation of a Helicopter Autopilot Design using Model-Based PID Control , 2013, J. Intell. Robotic Syst..

[9]  H. Jin Kim,et al.  Neural Networks Adaptive Support Vector Regression for Uav Flight Control , 2022 .

[10]  Ilkay Yavrucuk,et al.  Autolanding Controller Strategies For a Fixed Wing UAV in Adverse Atmospheric Conditions , 2008 .

[11]  Huijun Gao,et al.  Network-Induced Constraints in Networked Control Systems—A Survey , 2013, IEEE Transactions on Industrial Informatics.

[12]  Hao Zhang,et al.  Quantized Control Design for Impulsive Fuzzy Networked Systems , 2011, IEEE Transactions on Fuzzy Systems.

[13]  Lorenzo Marconi,et al.  High‐gain output feedback for a miniature UAV , 2014 .

[14]  Fuwen Yang,et al.  Distributed average filtering for sensor networks with sensor saturation , 2013 .

[15]  Daobo Wang,et al.  UAV Flight Control System Based on an Intelligent BEL Algorithm , 2013 .

[16]  Ali Reza Babaei,et al.  Fuzzy sliding mode autopilot design for nonminimum phase and nonlinear UAV , 2013, J. Intell. Fuzzy Syst..

[17]  Ali Reza Babaei,et al.  Classical and fuzzy-genetic autopilot design for unmanned aerial vehicles , 2011, Appl. Soft Comput..

[18]  Gary J. Balas,et al.  Development and application of an integrated framework for small UAV flight control development , 2011 .

[19]  Okyay Kaynak,et al.  Adaptive neuro-fuzzy inference system based autonomous flight control of unmanned air vehicles , 2007, Expert Syst. Appl..

[20]  Stefan R. Bieniawski,et al.  Model Reference Adaptive Control of a Quadrotor UAV , 2010 .

[21]  James Lam,et al.  A new delay system approach to network-based control , 2008, Autom..

[22]  Daobo Wang,et al.  Modified shuffled frog leaping algorithm for optimization of UAV flight controller , 2011, Int. J. Intell. Comput. Cybern..

[23]  Fuwen Yang,et al.  Observer-based H ∞ control for discrete-time stochastic systems with quantisation and random communication delays , 2013 .

[24]  高建平,et al.  STUDY ON GAIN-SCHEDULING PROBLEM IN FLIGHT CONTROL , 1999 .

[25]  Elisa Capello,et al.  Design and Validation of an ${\mathcal{L}}_{1}$ Adaptive Controller for Mini-UAV Autopilot , 2013, J. Intell. Robotic Syst..