Autonomous systems with nonlinear dynamics need to be extremely resilient to errors in sensors, actuators and on-board electronics for the purpose of overall vehicle safety. Prior research has focused on control-theoretic methods with significant computational burden with a focus on failures in actuation. In contrast, we propose the use of hierarchical machine learning driven state space checks that detect and diagnose errors in control program execution, sensors and actuators with high sensitivity and low latency. Each check produces a time-varying error signal that facilitates effect-cause diagnosis of the system, while allowing rapid parameter estimation from each check. Since the checks are over small subsets of system parameters, estimation is fast and accurate. The estimated parameters are then used to reconfigure the system controller parameters for rapid system recovery. We use a quadcopter system to demonstrate and validate our approach. Controller, sensor and actuator errors can be detected, diagnosed and compensated using a common checking platform with low computational overhead. The technique is validated on a quadcopter hardware test vehicle.