A New Nonlinear Control Strategy Embedded With Reinforcement Learning for a Multirotor Transporting a Suspended Payload
Hean Hua, Yongchun Fang, Xuetao Zhang, Qian Chen
Abstract
In this article, a reinforcement learning (RL)-based controller is proposed for a multirotor-based transportation system, guaranteeing that the trained RL controller is effective in both simulation and practical experiments. The main novelty lies in that, as far as we know, this is the first attempt of combining the advantages of nonlinear and intelligent control techniques to derive a practice-oriented RL controller for the multirotor-based transportation system, where the high-dimensional complicated dynamics are fully considered in the framework. Specifically, inspired by the physical insight of the system, a new nonlinear control approach is proposed, in which the underactuated properties and the nontrivial couplings are well handled. On this basis, an RL network is proposed to parameterize the nonlinear controller, where the obtained algorithm presents the features of strong reliability and fast convergence even in complicated working conditions (e.g., model uncertainties, parameters drift, external disturbances, and so on). Subsequently, the states are proven to asymptotically converge to the equilibrium point by Lyapunov analysis and RL techniques. A series of simulation and real world experiments are implemented to verify satisfactory positioning accuracy and robustness of the proposed algorithm.