Real-time Model Predictive Control for Aerial Manipulation

摘要

The rapid development in the field of Unmanned Aerial Vehicles (UAVs) is driven by new applications in agriculture, logistics, inspection and smart manufacturing. The future keys in these domains are the abilities to autonomously interact with the environment and with other robotic systems. This thesis is providing control engineering solutions to contribute to these key capabilities. The first step of this thesis is to develop an understanding of the dynamic behavior of UAVs. For this purpose, dynamic and kinematic models are presented to describe a UAV's motion. This includes a kinematic model which is suitable for off-the-shelf UAVs and combines full 360° heading operation with a low computational complexity. The presented models are subsequently used to develop a nonlinear model predictive control NMPC strategy. In this context, the performance of several NMPC solvers and inequality constraint handling techniques is evaluated. The real-time capability and NMPC performance are validated with real AR.Drone 2.0 and DJI M100 quadrotors. This includes collision avoidance and advanced tracking scenarios. The design work-flow for the related control objectives and constraints is presented accordingly. As a next step, this UAV NMPC strategy is extended for a UAV with attached robotic arm. For this purpose, the forward kinematics of the robotic arm are developed and combined with the kinematic model of the UAV. The resulting NMPC strategy is validated in a grasping scenario with a real aerial manipulator. The final step of this thesis is the NMPC of cooperating UAVs. The computational complexity of such scenarios conflicts directly with the fast UAV dynamics. In addition, control objectives and system topologies can dynamically change. To address these challenges, this thesis presents the DENMPC software framework. DENMPC provides a computationally efficient central NMPC strategy that allows changing the control scenario at runtime. This is finally stated in the control of a real cooperative aerial manipulation scenario.