Quantized Set-membership Filtering for Automatic Guided Vehicles with Unknown-but-bounded Noises

This paper investigates the quantized set-membership filtering (SMF) problem for network-based automatic guided vehicle (AGV) systems in the presence of unknown-but-bounded (UBB) process and measurement noises. Considering the bandwidth constraints of the communication networks, the measurement outputs of the AGV are quantized before transmitting to the remote filter. Two independent quantizers are designed to quantize each variable in the two-dimensional output vector separately. Then, an SMF scheme is presented to solve the problem of the remote state estimation for the AGV with UBB noises and the quantization error. Next, a recursive convex optimization algorithm is provided to obtain the state estimation ellipsoid sets for the considered AGV. Simulation results demonstrate the effectiveness of the proposed method.