Interacting multiple model estimation-based adaptive robust unscented Kalman filter

• Published in: International journal of control, automation, and systems 2017, Automation, and Systems, 15(5), , pp.2013-2025
• Main Authors: Gao, Bingbing, Gao, Shesheng, Zhong, Yongmin, Hu, Gaoge, Gu, Chengfan
• Format: Article
• Language: English
• Subjects: Adaptive filters; Adaptive systems; Computer simulation; Control; Dynamical systems; Engineering; Fading; Innovations; Kalman filters; Mechatronics; Nonlinear systems; Orthogonality; Regular Papers; Robotics; Robustness; State estimation; Statistical analysis; Uncertainty; 제어계측공학
• ISSN: 1598-6446; 2005-4092
• DOI: 10.1007/s12555-016-0589-2

The unscented Kalman filter (UKF) is a promising approach for the state estimation of nonlinear dynamic systems due to its simple calculation process and superior performance in highly nonlinear systems. However, its solution will be degraded or even divergent when the system model involves uncertainty. This paper presents an interacting multiple model (IMM) estimation-based adaptive robust UKF to address this problem. This method combines the merits of the adaptive fading UKF and robust UKF and discards their demerits to inhibit the disturbance of system model uncertainty on the filtering solution. An adaptive fading UKF for the case of process model uncertainty and a robust UKF for the case of measurement model uncertainty are established based on the principle of innovation orthogonality. Subsequently, an IMM estimation is developed to fuse the adaptive fading UKF and robust UKF as sub-filters according to the mode probability. The system state estimation is achieved as a probabilistic weighted sum of the estimation results from the two sub-filters. Simulations, experiments and comparison analysis validate the efficacy of the proposed method.