This paper is concerned with the state estimation problem for an automatic guided vehicle (AGV). A novel set-membership filtering (SMF) scheme is presented to solve the state estimation problem in the trajectory tracking process of the AGV under the unknown-but-bounded (UBB) process and measurement noises. Different from some existing traditional filtering methods, such as Kalman filtering method and [Formula: see text] filtering method, the proposed SMF scheme is developed to provide state estimation sets rather than state estimation points for the system states to effectively deal with UBB noises and reduce the requirement of the sensor precision. Then, in order to obtain the state estimation ellipsoids containing the true states, a set-membership estimation algorithm is designed based on the AGV physical model and S-procedure technique. Finally, comparison examples are presented to illustrate the effectiveness of the proposed SMF scheme for an AGV state estimation problem in the present of the UBB noises.
On State Estimation for Discrete Time-Delayed Memristive Neural Networks Under the WTOD Protocol: A Resilient Set-Membership Approach
