Conditional Trigger Model Predictive Control for Aerial Manipulation

Carrying a robotic arm on a UA Venables the creation of an aerial manipulator system with the capability to actively execute tasks. However, challenges such as multiple variables, strong coupling, and high computational demands exist. Controlling a drone's complex model to concurrently achieve tracking and grasping has become a focal and challenging aspect of research. To mitigate computational costs, a model linearization method is employed through nonlinear prediction and linearization along the trajectory. Subsequently, the Model Predictive Control (MPC) method is applied to the integrated model for simultaneous trajectory tracking and grasping control. Finally, a conditional trigger mechanism is proposed in conjunction with the MPC method to further reduce computational expenses. Results indicate that this method successfully achieves traj ectory tracking and target grasping for the aerial manipulator system, demonstrating high accuracy while effectively lowering computational costs. This approach holds promise for practical applications in aerial manipulator systems.