Runtime Monitoring and Fault Detection for Neural Network-Controlled
  Systems
Jianglin Lan Siyuan Zhan Ron Patton Xianxian Zhao
There is an emerging trend in applying deep learning methods to control
complex nonlinear systems. This paper considers enhancing the runtime safety of
nonlinear systems controlled by neural networks in the presence of disturbance
and measurement noise. A robustly stable interval observer is designed to
generate sound and precise lower and upper bounds for the neural network,
nonlinear function, and system state. The obtained interval is utilised to
monitor the real-time system safety and detect faults in the system outputs or
actuators. An adaptive cruise control vehicular system is simulated to
demonstrate effectiveness of the proposed design.