A multilevel sampled-data approach for resilient navigation and control of autonomous systems

摘要

Autonomous systems are rapidly becoming an integrated part of the modern life. Safe and secure navigation and control of these systems present significant challenges in the presence of uncertainties, physical failures, and cyber attacks. In this paper, we formulate a navigation and control problem for autonomous systems using a multilevel control structure, in which the high-level reference commands are limited by a saturation function, whereas the low-level controller tracks the reference by compensating for disturbances and uncertainties. For this purpose, we consider a class of nested, uncertain, multiple-input-multiple-output systems subject to reference command saturation, possibly with nonminimum phase zeros. A multirate output-feedback L1 adaptive controller is developed as the low-level controller. The sampled-data (SD) design of this controller facilitates the direct implementation on digital computers, where the input/output signals are available at discrete time instances with different sampling rates. In addition, stealthy zero-dynamics attacks become detectable by considering a multirate SD formulation. Robust stability and performance of the overall closed-loop system with command saturation and multirate L1 adaptive control are analyzed. Simulation scenarios for navigation and control of a fixed-wing drone under failures/attacks are provided to validate the theoretical findings.