Optimized Fuzzy Attitude Control of Quadrotor Unmanned Aerial Vehicle Using Adaptive Reinforcement Learning Strategy

This article is to develop an optimized attitude control using reinforcement learning (RL) for a quadrotor unmanned aerial vehicle (QUAV) system. Since this optimized scheme is required to steer both attitude angle position and velocity states to follow the predefined reference signals, it is a very challenging and interesting work. In theory, an optimal control can be found via solving the Hamilton-Jacobi-Bellman (HJB) equation. Nevertheless, the HJB equation associated with the QUAV attitude dynamic equation is hardly solved via analytical methods owing to the complex nonlinearity. To address the problem, the RL strategy of critic-actor architecture using fuzzy logic system approximation is proposed. For smooth manipulating both attitude angle position and velocity states, the optimized control has to contain both tracking error terms corresponding to the angle position and velocity. Thus, if the optimized attitude control learns from the traditional methods, the control algorithm will be very intricate. Since this RL tuning laws are derived from the negative gradient of a simple positive function that is equivalent to the HJB equation, the control algorithm is significantly simplified. Finally, both theory proof and actual experiment demonstrate that this optimized attitude control is competent for fulfilling the control objective.