In this paper, we present a controller design and its implementation on a mini rotorcraft having four rotors. The dynamic model of the four-rotor rotorcraft is obtained via a Lagrange approach. The proposed controller is based on Lyapunov analysis using a nested saturation algorithm. The global stability analysis of the closed-loop system is presented. Real-time experiments show that the controller is able to perform autonomously the tasks of taking off, hovering, and landing.

Pursuit-evasion is the problem of capturing mobile targets with one or more pursuers. We use deep reinforcement learning for pursuing an omni-directional target with multiple,homogeneous agents that are subject to unicycle kinematic constraints. We use shared experience to train a policy for a given number of pursuers, executed independently by each agent at run-time. The training uses curriculum learning, a sweeping-angle ordering to locally represent neighboring agents and a reward structure that encourages good formation and combines individual and group rewards. Simulated experiments with a reactive evader and up to eight pursuers show that our learning-based approach outperforms recent reinforcement learning techniques as well non-holonomic adaptations of classical algorithms.The learned policy is successfully transferred to the real world in a proof-of-concept demonstration with three motion-constrained pursuer drones.

We present a simple non-linear controller based on Lyapunov analysis for a planar vertical take-off and landing aircraft (PVTOL). It is proved that the proposed control scheme is globally asymptotically stable. The controller performance is shown in a real-time application.