A Novel Guided Deep Reinforcement Learning Tracking Control Strategy for Multirotors

This paper presents an intelligent control scheme for multirotors, where accurate trajectory tracking, strong robustness and reliable generalization are guaranteed by the dual-feedback sliding-mode (DFSM) guided deep reinforcement learning (RL). Different from current solutions, the proposed method explores optimal learning strategy on the sliding surface according to the DFSM demonstrations, where the elegantly designed parallel evaluation takes full advantage of model knowledge and learning exploration. Specifically, the intelligent tracking control is achieved in a two-step design. First, the DFSM algorithm is designed for multirotors, where the linear and nonlinear feedback terms work cooperatively. Second, the DFSM-guided deep RL is put forward to achieve intelligent switching on the sliding surface, where position and velocity errors are both considered to generate accurate switching decisions. In the framework, explorations and the DFSM demonstrations are evaluated in parallel, where only the explorations that are better than the DFSM baseline, are kept for policy improvement. In this way, the DFSM algorithm keeps pushing the RL policy to explore better strategy, where the unavoidable bad experiences arisen from exploration are identified accurately. Practical comparative experimental results are included to verify the effectiveness of the proposed strategy. Note to Practitioners —This paper is motivated by the practical problem of controlling multirotor system in uncertain environments. Up until now, most existing approaches are proposed without taking full advantage of model knowledge and deep learning techniques simultaneously, which lacks of reliability in practical application. To deal with the problem, a new dual-feedback sliding-mode (DFSM) guided deep reinforcement learning (RL) strategy is proposed, where the dual feedback and guided RL are designed to achieve satisfactory tracking control and simultaneously handle uncertainties. Specifically, by introducing double-check framework, the RL strategy explores optimal switching policy on the sliding surface according to the DFSM demonstrations, guaranteeing strong robustness and reliable generalization of the obtained RL policy in uncertain environments. The key feature of the framework is that the DFSM-driven training guarantees practice-oriented tracking control in a DFSM-RL cooperative manner. Comparative experiments are implemented to verify the tracking performance of the proposed intelligent control strategy.