Hierarchical State Space Checks for Errors in Sensors, Actuators and Control of Nonlinear Systems: Diagnosis and Compensation

The rapid rise of self-driving cars and drones has raised questions about the safety of autonomous robotics deployed in society. Prior work on robust and adaptive control make assumptions about the boundedness of errors or require the use of full scale system models running in the background for control reference. In this research, we show how state space checks can be used to diagnose and compensate for errors in sensors, actuators and control program execution in nonlinear systems for robotic applications. The primary focus is on lightweight methods for mitigation of transient errors in sensor data and control program execution and parametric deviations in sensor circuitry and actuator subsystems. A quadcopter is used as a test vehicle for the research and simulation results indicate that errors can be compensated with high efficiency and low computation overhead.