Antagonistic Model Predictive Control for Quadrotor Cyber Attacks

This paper presents an attack strategy for autonomous unmanned aerial vehicles. Unmanned aerial vehicles are driven by two-layer control systems composed of an inner loop driving the vehicle’s altitude and attitude and an outer loop managing the position. In this study, it is assumed that the attacker can access the lower control loop and modify the control signal driving the actuators. While the vehicle is under attack, an optimal policy oriented to drive the vehicle in a failure condition is applied to compute the hacked control signals, thus overriding the inner-loop controller. This optimal policy is an Antagonistic Controller based on the Model Predictive Control paradigm. The antagonistic controller iteratively evaluates the effect of a possible attack, within the available attack time interval, to identify the suitable operating condition to initiate the attack. This evaluation is performed by the analysis of a performance index related to the Antagonistic Control state predictions to damage the vehicle. The proposed approach has been tested in simulation using a detailed nonlinear quadrotor model to show the effectiveness of the proposed approach.