Path Following with Threat Avoidance Through Accelerated Model Predictive Control

In this paper, we focus on the path following control of a fixed-wing UAV and investigate how to avoid pop-up threats by onboard optimization. It contains two subproblems: how to bypass threat areas in time and how to minimize deviations from the desired path. More importantly, the calculation must be completed in real-time. To address this, we design an accelerated model predictive controller with two stages. First, we incorporate multiple control objectives into the prediction model in a weighted manner. Then, by introducing the rectified linear unit penalties, it is transformed into an unconstrained problem, which is solved by gradient-based optimizers. Six state-of-the-art optimizers are compared in simulations. The results prove that the proposed control method can well deal with multiple static or moving threats, is robust to wind disturbance, and has high real-time performance.