Real-Time Error Detection in Nonlinear Control Systems Using Machine Learning Assisted State-Space Encoding

Suvadeep Banerjee

doi:10.1109/TDSC.2019.2903049

Return

Real-Time Error Detection in Nonlinear Control Systems Using Machine Learning Assisted State-Space Encoding

2021-03-01

Suvadeep Banerjee *

Balavinayagam Samynathan

Jacob A. Abraham

Abhijit Chatterjee

DOI：10.1109/TDSC.2019.2903049

Original

Save

View PDF

Abstract

En 中文

Successful deployment of autonomous systems in a wide range of societal applications depends on error-free operation of the underlying signal processing and control functions. Real-time error detection in nonlinear systems has mostly relied on redundancy at the component or algorithmic level causing expensive area and power overheads. This paper describes a real-time error detection methodology for nonlinear control systems for detecting sensor and actuator degradations as well as malfunctions due to soft errors in the execution of the control algorithm on a digital processor. Our approach is based on creation of a redundant check state in such a way that its value can be computed from the current states of the system as well as from a history of prior observable state values and inputs (via machine learning algorithms). By checking for consistency between the two, errors are detected with low latency. The method is demonstrated on two test case simulations - an inverted pendulum balancing problem and a sliding mode controller driven brake-by-wire (BBW) system. In addition, hardware results from error injection experiments in an ARM core representation on an FPGA and artificial sensor degradations on a self-balancing robot prove the practical feasibility of implementation.

Keywords:

Nonlinear systems

Hardware

Real-time systems

Nonlinear control systems

Actuators

Degradation

Nonlinear Systems

Nonlinear Control

Real-time Systems

State-space Checksum

Fault Detection

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.