Robust particle filter for state estimation using measurements with different types of gross errors. (July 2017) - Zhu, Zhiliang; Meng, Zhiqiang; Zhang, Zhengjiang; Chen, Junghui; Dai, Yuxing
Abstract: For industrial processes, the state estimation plays a key role in various applications, such as process monitoring and model based control. Although the particle filter (PF) is able to deal with nonlinear and non-Gaussian processes, it rarely considers the influence of measurements with gross errors, such as outliers, biases and drifts. Nevertheless, measurements of dynamical systems are often influenced by different types of gross errors. This paper proposes a robust PF approach, in which gross error identification is used to estimate magnitudes of gross error. The gross errors can be removed or compensated so that a feasible set of particle sampling can contain the true states of the system. The proposed robust PF approach is implemented on a complex nonlinear dynamic system, the free radical polymerization of styrene. The application results show that the proposed approach is an appealing alternative to solving PF estimation problems with measurements containing gross errors. Highlights: A robust PF is proposed to solve state estimation problems with gross errors (GE). The influence of GE is effectively decreased by GE identification and compensation. The robust PF is applied onto a complex dynamic chemical process system. The robust PF can achieve more accurate and robust results than the generic PF.