LPV Model-Based Tracking Control and Robust Sensor Fault Diagnosis for a Quadrotor UAV

This work is dedicated to the design of a robust fault detection and tracking controller system for a UAV subject to external disturbances. First, a quadrotor modelled as a Linear Parameter Varying (LPV) system is considered as a target to design and to illustrate the proposed methodologies. In order to perform fault detection and isolation, a robust LPV observer is designed. Sufficient conditions to guarantee asymptotic stability and robustness against disturbance are given by a set of feasible Linear Matrix Inequalities (LMIs). Furthermore, the observer gains are designed with a desired dynamic by considering pole placement based on LMI regions. Then, a fault detection and isolation scheme is considered by mean of an observer bank in order to detect and isolate sensor faults. Second, a feedback controller is designed by considering a comparator integrator control scheme. The goal is to design a robust controller, such that the UAV tracks some reference positions. Finally, some simulations in fault-free and faulty operations are considered on the quadrotor system.

[1]  Daewon Lee,et al.  Build Your Own Quadrotor: Open-Source Projects on Unmanned Aerial Vehicles , 2012, IEEE Robotics & Automation Magazine.

[2]  Youmin Zhang,et al.  Development of advanced FDD and FTC techniques with application to an unmanned quadrotor helicopter testbed , 2013, J. Frankl. Inst..

[3]  M. Witczak,et al.  Fault-Tolerant Control of a two-degree of freedom helicopter using LPV techniques , 2008, 2008 16th Mediterranean Conference on Control and Automation.

[4]  M. Wongsaisuwan,et al.  Optimal control of quad-rotor helicopter using state feedback LPV method , 2012, 2012 9th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology.

[5]  Rolf Isermann,et al.  Fault-diagnosis systems : an introduction from fault detection to fault tolerance , 2006 .

[6]  Paul M. Frank,et al.  Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: A survey and some new results , 1990, Autom..

[7]  Didier Theilliol,et al.  Design of LPV observers with immeasurable gain scheduling variable under sensor faults , 2011 .

[8]  Francisco Jurado,et al.  Stabilization of a quadrotor via Takagi-Sugeno fuzzy control , 2008 .

[9]  Guangxun Du,et al.  Controllability Analysis and Degraded Control for a Class of Hexacopters Subject to Rotor Failures , 2015, J. Intell. Robotic Syst..

[10]  Corentin Briat Linear Parameter-Varying and Time-Delay Systems: Analysis, Observation, Filtering & Control , 2014 .

[11]  Jeff S. Shamma,et al.  An Overview of LPV Systems , 2012 .

[12]  Steven X. Ding,et al.  H_/H∞ fault detection filter design for discrete-time Takagi-Sugeno fuzzy system , 2013, Autom..

[13]  Youmin Zhang,et al.  A Review on Fault Diagnosis and Fault Tolerant Control Methods for Single-rotor Aerial Vehicles , 2014, J. Intell. Robotic Syst..

[14]  Guilherme V. Raffo,et al.  An integral predictive/nonlinear Hinfinity control structure for a quadrotor helicopter , 2010, Autom..

[15]  Didier Theilliol,et al.  Robust H−/H∞ fault detection observer design for descriptor-LPV systems with unmeasurable gain scheduling functions , 2015, Int. J. Control.

[16]  D. Theilliol,et al.  Robust sensor fault diagnosis and tracking controller for a UAV modelled as LPV system , 2014, 2014 International Conference on Unmanned Aircraft Systems (ICUAS).

[17]  Ian Postlethwaite,et al.  Survey and application of sensor fault detection and isolation schemes , 2011 .

[18]  Didier Theilliol,et al.  Observer-based fault tolerant control design for a class of LPV descriptor systems , 2014, J. Frankl. Inst..

[19]  A. Benallegue,et al.  Exact linearization and noninteracting control of a 4 rotors helicopter via dynamic feedback , 2001, Proceedings 10th IEEE International Workshop on Robot and Human Interactive Communication. ROMAN 2001 (Cat. No.01TH8591).

[20]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[21]  G. Duan,et al.  LMIs in Control Systems: Analysis, Design and Applications , 2013 .

[22]  T. Guerra,et al.  Motion control of planar parallel robot using the fuzzy descriptor system approach. , 2012, ISA transactions.

[23]  Antonio Sala,et al.  Application of Takagi-Sugeno observers for state estimation in a quadrotor , 2011, IEEE Conference on Decision and Control and European Control Conference.

[24]  Damiano Rotondo,et al.  Robust Quasi–LPV Model Reference FTC of a Quadrotor Uav Subject to Actuator Faults , 2015, Int. J. Appl. Math. Comput. Sci..

[25]  Hassan Noura,et al.  Robust Fault Diagnosis for Quadrotor UAVs Using Adaptive Thau Observer , 2014, J. Intell. Robotic Syst..

[26]  Jie Chen,et al.  Robust Model-Based Fault Diagnosis for Dynamic Systems , 1998, The International Series on Asian Studies in Computer and Information Science.

[27]  Youmin Zhang,et al.  Bibliographical review on reconfigurable fault-tolerant control systems , 2003, Annu. Rev. Control..

[28]  Pierre Apkarian,et al.  Robust pole placement in LMI regions , 1999, IEEE Trans. Autom. Control..

[29]  Didier Theilliol,et al.  Robust sensor fault estimation for descriptor-LPV systems with unmeasurable gain scheduling functions: Application to an anaerobic bioreactor , 2015, Int. J. Appl. Math. Comput. Sci..

[30]  Mohammad Hassan Asemani,et al.  A robust H∞ observer-based controller design for uncertain T-S fuzzy systems with unknown premise variables via LMI , 2013, Fuzzy Sets Syst..

[31]  Hamid Reza Karimi,et al.  Robust Observer Design for Unknown Inputs Takagi–Sugeno Models , 2013, IEEE Transactions on Fuzzy Systems.