Payload Drop Application Using an Unmanned Quadrotor Helicopter Based on Gain-Scheduled PID and Model Predictive Control

Two useful control techniques are investigated and applied experimentally to an unmanned quadrotor helicopter for a practical and important scenario of using an Unmanned Aerial Vehicle (UAV) for dropping a payload in circumstances where search and rescue and delivery of supplies and goods is dangerous and difficult to reach environments such as forest or high building fires fighting, rescue in earthquake, flood and nuclear disaster situations. The two considered control techniques for such applications are the Gain-Scheduled Proportional-Integral-Derivative (GS-PID) control and the Model Predictive Control (MPC). Both the model-free (GS-PID) and model-based (MPC) algorithms show a very promising performance with application to taking-off, height holding, payload dropping, and landing periods in a payload dropping mission. Finally, both algorithms are successfully implemented on an unmanned quadrotor helicopter testbed (known as Qball-X4) available at the Networked Autonomous Vehicles Lab (NAVL) of Concordia University for payload dropping tests to illustrate the effectiveness and performance comparison of the two control techniques.

[1]  Jay H. Lee,et al.  Model predictive control: Review of the three decades of development , 2011 .

[2]  Timothy W. McLain,et al.  Decentralized Cooperative Aerial Surveillance Using Fixed-Wing Miniature UAVs , 2006, Proceedings of the IEEE.

[3]  Sun Yu,et al.  Kinematic Characteristics of 3-UPU Parallel Manipulator in Singularity and Its Application , 2011 .

[4]  Addy Wahyudie,et al.  Robust PID Controller for Quad-rotors , 2013 .

[5]  E. Altug,et al.  Modeling and PD Control of a Quadrotor VTOL Vehicle , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[6]  Youmin Zhang,et al.  An Efficient Model Predictive Control Scheme for an Unmanned Quadrotor Helicopter , 2013, J. Intell. Robotic Syst..

[7]  M. Tadjine,et al.  Sliding Mode Control Based on Backstepping Approach for an UAV Type-Quadrotor , 2007 .

[8]  Peter Strobl,et al.  Monitoring of gas pipelines - a civil UAV application , 2005 .

[9]  Randal W. Beard,et al.  Enhanced UAS Surveillance Using a Video Utility Metric , 2013 .

[10]  Olivier Stasse,et al.  An optimized linear model predictive control solver , 2010 .

[11]  Jeremiah Gertler,et al.  Homeland Security: Unmanned Aerial Vehicles and Border Surveillance , 2010 .

[12]  Kun Li,et al.  Development of an Unmanned Coaxial Rotorcraft for the DARPA UAVForge Challenge , 2013 .

[13]  Maki K. Habib,et al.  Robot-Assisted Risky Intervention, Search, Rescue and Environmental Surveillance , 2010 .

[14]  Alberto Bemporad,et al.  Discrete-Time Non-smooth Nonlinear MPC: Stability and Robustness , 2007 .

[15]  Isaac Kaminer,et al.  A Computationally Efficient Approach to Trajectory Management for Coordinated Aerial Surveillance , 2013 .

[16]  Timothy W. McLain,et al.  Cooperative forest fire surveillance using a team of small unmanned air vehicles , 2006, Int. J. Syst. Sci..

[17]  Paolo Rocco,et al.  Stability of PID control for industrial robot arms , 1996, IEEE Trans. Robotics Autom..

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

[19]  Therese Skrzypietz,et al.  Unmanned Aircraft Systems for Civilian Missions , 2012 .

[20]  M.O. Efe,et al.  Robust low altitude behavior control of a quadrotor rotorcraft through sliding modes , 2007, 2007 Mediterranean Conference on Control & Automation.

[21]  Sukho Park,et al.  Development of Biomedical Microrobot for Intravascular Therapy , 2010 .

[22]  Jun Xu,et al.  Cooperative Search and Exploration in Robotic Networks , 2013 .