Input Design for Fault Detection Using Extended Kalman Filter and Reinforcement Learning

Abstract

Abstract The paper deals with an active fault detection problem for jump Markov nonlinear systems with Gaussian noises. The problem is formulated as a functional optimization problem over an infinite-time horizon with a general discounted detection criterion. The design of an active fault detector is performed in two steps. First, the original problem is recast as a perfect state information problem by complementing the nonlinear system with a suboptimal state estimator based on a bank of extended Kalman filters. Then, a temporal-difference learning algorithm is used to train the active fault detector such that the criterion is minimized. A simulation example of a differential wheeled robot is used to illustrate the performance of the proposed design.