REAL-TIME ERROR DETECTION AND CORRECTION FOR ROBUST OPERATION OF AUTONOMOUS SYSTEMS USING ENCODED STATE CHECKS

The objective of the proposed research is to develop methodologies, support algorithms and software-hardware infrastructure for detection, diagnosis, and correction of failures for actuators, sensors and control software in linear and nonlinear state variable systems with the help of multiple checks employed in the system. This objective is motivated by the proliferation of autonomous sense-and-control real-time systems, such as intelligent robots and self-driven cars which must maintain a minimum level of performance in the presence of electro-mechanical degradation of system-level components in the field as well as external attacks in the form of transient errors. A key focus is on rapid recovery from the effects of such anomalies and impairments with minimal impact on system performance while maintaining low implementation overhead as opposed to traditional schemes for recovery that rely on duplication or triplication. On-line detection, diagnosis and correction techniques are investigated and rely on analysis of system under test response signatures to real-time stimulus. For on-line error detection and diagnosis, linear and nonlinear state space encodings of the system under test are used and specific properties of the codes, as well as machine learning model based approaches were used are analyzed in real-time. Recovery is initiated by copying check model values to correct error for sensor and control software malfunction, and by redesigning the controller parameter on-the-fly for actuators to restore system performance. Future challenges that need to be addressed include viability studies of the proposed techniques on mobile autonomous system in distributed setting as well as application to systems with soft as well as hard real-time performance constraints.Ph.D

Robust and Reliable Error Detection and Correction for Autonomous Systems

Presented at the Georgia Tech Career, Research, and Innovation Development Conference (CRIDC), January 27-28, 2020, Georgia Tech Global Learning Center, Atlanta, GA.The Career, Research, and Innovation Development Conference (CRIDC) is designed to equip on-campus and online graduate students with tools and knowledge to thrive in an ever-changing job market.Md Imran Momtaz and Abhijit Chatterjee - School of Electrical and Computer Engineering, Georgia Institute of Technology.The rapid rise of self-driving cars and drones has raised questions about the safety of autonomous robotics deployed in society. This is due to the large numbers of system state variables involved, the resulting degraded ability to perform accurate error detection and most importantly, loss of the ability to perform accurate error diagnosis. 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 facilitated by different machine learning algorithms can be used to detect, diagnose and compensate for errors in sensors, actuators and control program execution in linear and nonlinear systems for robotic applications. The primary focus is on low-cost, ultra-fast, efficient, and lightweight methods for mitigation of transient errors in sensor data and control program execution and parametric deviations in sensor circuitry and actuator subsystems. Additionally, the proposed approach should incur minimal hardware and software overhead. The proposed approach has been applied to multiple test-cases which includes DC motor control system, quadcopter as well as automotive subsystems such as steer by wire subsystem. Simulation results indicate that errors can be compensated with high efficiency and low computation overhead.National Science Foundation (U.S.)Semiconductor Research Corporatio