Approximate Optimal Recursive Sliding Mode Control for Quadrotors and Adaptive Parameter Optimization Via Deep Reinforcement Learning

In this paper, we propose an improved quadrotor controller by integrating the fixed-time disturbance observer (FTDO), the singularity-free recursive fast nonsingular terminal sliding mode control (RFNTSMC), and a deep reinforcement learning (DRL)-based hyperparameter optimizer. First, we decompose the overall quadrotor system into two loops: the attitude loop and the position loop. For each loop, we use an FTDO to provide a precise estimation of the disturbances within a fixed time. Then, we design for each loop an RFNTSMC to achieve finite-time tracking of a class of reference trajectories. Moreover, by adopting a DRL-based parameter optimization framework, the design parameter of our controller can be optimized while ensuring the trajectory of the closed-loop system is uniformly ultimately bounded during the learning process. We use both numerical simulations and physical experiments to illustrate the effectiveness and superiority of our results.