Development of Algorithms for Performance Index-Based Actuator Fault Detection and Fault-Tolerant Control of Autonomous Vehicle With Adaptive Feedback

Abstract

Abstract This study proposes algorithms for performance index-based actuator fault detection and fault-tolerant control with adaptive feedback. Longitudinal control algorithm of autonomous vehicles is generally divided into three main parts such as supervisory, upper level, and lower level controllers. In the supervisory controller part, desired behavior is determined based on control targets. Therefore, desired vehicle motion such as longitudinal acceleration can be designed in the upper level controller part using various control methods. In the lower level controller part, actuator control input is determined for tracking the desired vehicle motion designed in the upper level controller part. In order for fault detection and fault-tolerant control of actuators used for longitudinal autonomous driving, adaptive feedback controller has been designed with the MIT rule for determination of the desired longitudinal acceleration and the control input has been compared with the actual acceleration for performance index-based fault detection. It is designed that the adaptive feedback controller adjust the desired acceleration for making reasonable desired behavior despite of existence of actuator fault or performance degradation. The window-based weighted standard deviation of error between the adaptive desired longitudinal acceleration and current acceleration of vehicle has been used for computation of performance index. Performance evaluation has been conducted using Matlab/Simulink and commercial software (CarMaker).