Abstract:
Quadrotor unmanned aerial vehicles (UAVs) are playing important roles in cargo transportation, especially in complex and unknown environments. However, the underactuated ...Show MoreMetadata
Abstract:
Quadrotor unmanned aerial vehicles (UAVs) are playing important roles in cargo transportation, especially in complex and unknown environments. However, the underactuated nature of the quadrotor UAV transportation systems makes it difficult for control schemes design. Considering system parameter uncertainties, a novel nonlinear adaptive control law is designed via Immersion and Invariance (I&I) method. Besides, in order to pursue faster convergence and stronger robustness, a novel control structure based on deep reinforcement learning is presented. The RL agent, which is designed through twin delayed deep deterministic policy gradient (TD3) algorithm, generates optimal control gains for the I&I adaptive controller in real-time. The stability of the closed-loop system are guaranteed through Lyapunov technique and LaSalle’s invariance theorem. Finally, by comparing with the classical control scheme, the superior performance and stronger robustness of the proposed controller are validated through numerous simulations.
Date of Conference: 09-11 July 2022
Date Added to IEEE Xplore: 29 November 2022
ISBN Information: