Resilient-based Control Reconfiguration of Autonomous Systems

Abstract

This paper introduces a design methodology for resilientbasedcontrol reconfiguration of Unmanned AutonomousSystems (UAS) when extreme disturbances, such as a largelygrowing fault or component failure mode occur. It isdocumented that more than 40% of Class air mishaps areattributed to Unmanned Aerial Vehicles (UAVs). There is anurgent need to improve the operational integrity, resilienceand reliability of such critical assets. An optimal controlapproach with Differential Dynamic Programming (DDP)and Model Predictive Control (MPC) is introduced in thispaper as a means for control authority redistribution andreconfiguration; therefore, the system continues performingits mission while compensating for the impact of the extremedisturbances. Prognostic knowledge is considered in aquadratic cost function of the optimal control problem as asoft constraint. A trade-off parameter is introduced betweenthe prognostic constraint and the terminal cost. Anautonomous ground operable under-actuated hovercraft isused to demonstrate the efficacy of the proposedreconfiguration strategy, and it is extendable to other cyberphysical systems.