Aggressive Optimal Control for Agile Flight with a Slung Load
نویسندگان
چکیده
A possible design method for learning motion control consists of using a model-based optimal control algorithm to initialize the policy of a sampling based reinforcement learning algorithm. In this paper, the initial control trajectory design is performed for a quadrotor with a cable-suspended load using the iterative LQG (iLQG) algorithm. The hybrid model of the quadrotor with the load is introduced, and four sample tasks are presented. These tasks consist of a simple waypoint task as well as a task where the quadrotor and the load have to pass through a small window. The window task requires more aggressive control, exploiting the underactuated system dynamics including system mode switches, where the load is in free fall for some period of time.
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