Automatic Tuning Architecture for the Navigation Control Loops of Unmanned Aerial Vehicles

One of the most time consuming phases in the development of an Unmanned Aerial Vehicle is the tuning of the control algorithms. In this paper the hardware and software suite developed for the self-tuning of the control loops of unmanned flying platforms is presented. The VOLCAN UAV has been used as platform to test and validate the developed architecture. The simplified control system of the VOLCAN UAV is described, together with the Graphical User Interface that allows the rapid automatic tuning of the system by means of Åström and Hägglund’s method. The Hardware in the Loop architecture used to test both the control algorithms and the tuning procedure is presented in the final part of the paper, together with the obtained experimental results.

[1]  Giovanni Muscato,et al.  “Hardware in the Loop” Tuning for a Volcanic Gas Sampling UAV , 2007 .

[2]  Giovanni Muscato,et al.  An Overview of the “Volcan Project”: An UAS for Exploration of Volcanic Environments , 2009, J. Intell. Robotic Syst..

[3]  N. Regina,et al.  Fixed-wing UAV guidance law for surface-target tracking and overflight , 2012, 2012 IEEE Aerospace Conference.

[4]  Kimon P. Valavanis,et al.  Advances in Unmanned Aerial Vehicles: State of the Art and the Road to Autonomy , 2007 .

[5]  S. Levy,et al.  PID autotuning using relay feedback , 2012, 2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel.

[6]  I. Nilkhamhang,et al.  Autonomous path tracking and disturbance force rejection of UAV using fuzzy based auto-tuning PID controller , 2010, ECTI-CON2010: The 2010 ECTI International Confernce on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology.

[7]  Yoshiaki Kuwata,et al.  Flight Demonstrations of Cooperative Control for UAV Teams , 2004 .

[8]  YangQuan Chen,et al.  Autopilots for small unmanned aerial vehicles: A survey , 2010 .

[9]  Vasfi Emre Omurlu,et al.  Self tuning fuzzy PD application on TI TMS320F28335 for an experimental stationary quadrotor , 2010, 4th European Education and Research Conference (EDERC 2010).

[10]  Karl Johan Åström,et al.  PID Controllers: Theory, Design, and Tuning , 1995 .

[11]  Giovanni Muscato,et al.  HIL Tuning of UAV for Exploration of Risky Environments , 2008 .

[12]  Kimon P. Valavanis,et al.  Advances in Unmanned Aerial Vehicles , 2007 .

[13]  Young-shin Kang,et al.  Hardware-In-the-Loop simulation test for actuator control system of Smart UAV , 2010, ICCAS 2010.

[14]  Jiao Shi,et al.  Design and Simulation of the Longitudinal Autopilot of UAV Based on Self-Adaptive Fuzzy PID Control , 2009, 2009 International Conference on Computational Intelligence and Security.

[15]  Vladimír Oplustil,et al.  Experience with integration and certification of COTS based embedded system into advanced avionics system , 2007, 2007 International Symposium on Industrial Embedded Systems.

[16]  Zhen Jiang,et al.  Online Fuzzy Self-Adaptive PID Attitude Control of a Sub Mini Fixed-Wing Air Vehicle , 2007, 2007 International Conference on Mechatronics and Automation.

[17]  Giovanni Muscato,et al.  Volcanic Environments: Robots for Exploration and Measurement , 2012, IEEE Robotics & Automation Magazine.

[18]  A. Leva,et al.  Comparative study of model-based PI(D) autotuning methods , 2007, 2007 American Control Conference.