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The last decade has seen tremendous advances in the transformation of ubiquitous control, computing and communication platforms that are anytime, anywhere. These platforms allow humans to interact with machines through sensing, control and actuation functions in ways not imaginable a few decades ago. While robust control techniques aim to maintain autonomous system performance in the presence of bounded modeling errors, they are not designed to manage large multiparameter variations and internal component failures that are inevitable during lengthy periods of field deployment. To address the trustworthiness of autonomous systems in the field, we propose a cross-layer error resilience approach in which errors are detected and corrected at appropriate levels of the design (hardware-through software) with the objective of minimizing the latency of error recovery while maintaining high failure coverage. At the control processor level, soft errors in the digital control processor are considered. At the system level, sensor and actuator failures are analyzed. These impairments define the health of the system. A methodology for adapting the control procedure of the autonomous system to compensate for degraded system health is proposed. It is shown how this methodology can be applied to simple linear and nonlinear control systems to maintain system performance in the presence of internal component failures. Experimental results demonstrate the feasibility of the proposed methodology.