Optimal Tracking Control Based on Integral Reinforcement Learning for An Underactuated Drone

Abstract

Abstract A drone is desirable to perform various flying missions with different loads while always guaranteeing optimal flying performance. In this paper, an integral reinforcement learning algorithm is developed for a drone such that it can learn optimal control policy online. The drone is described by an underactuated nonlinear model and the inner-outer loop control strategy is applied for the navigation control. In the outer loop an optimal controller is designed to minimize a cost function with input saturation, and a policy iteration based integral reinforcement learning (IRL) algorithm is proposed. Critic-actor neural networks (NNs) are further applied for online implementation of the IRL algorithm. In the inner loop a quaternion based feedback attitude controller is designed to guarantee system stability. A simulation study is finally provided to demonstrate the effectiveness of the proposed IRL algorithm.