title: State estimation of stochastic non-linear hybrid dynamic system using an interacting multiple model algorithm.
abstract: In this work, state estimation schemes for non-linear hybrid dynamic systems subjected to stochastic state disturbances and random errors in measurements using interacting multiple-model (IMM) algorithms are formulated. In order to compute both discrete modes and continuous state estimates of a hybrid dynamic system either an IMM extended Kalman filter (IMM-EKF) or an IMM based derivative-free Kalman filters is proposed in this study. The efficacy of the proposed IMM based state estimation schemes is demonstrated by conducting Monte-Carlo simulation studies on the two-tank hybrid system and switched non-isothermal continuous stirred tank reactor system. Extensive simulation studies reveal that the proposed IMM based state estimation schemes are able to generate fairly accurate continuous state estimates and discrete modes. In the presence and absence of sensor bias, the simulation studies reveal that the proposed IMM unscented Kalman filter (IMM-UKF) based simultaneous state and parameter estimation scheme outperforms multiple-model UKF (MM-UKF) based simultaneous state and parameter estimation scheme.
authors: Department of Instrumentation Engineering, Madras Institute of Technology Campus, Anna University, Chennai 600044, India.
authors: M
authors: M
authors: Elenchezhiyan
authors: Department of Instrumentation Engineering, Madras Institute of Technology Campus, Anna University, Chennai 600044, India. Electronic address: prakaiit@gmail.com.
authors: J
authors: J
authors: Prakash
keywords: Hybrid dynamic system; Interacting multiple-model based Kalman filter algorithm and extended Kalman filter; State estimation; Unscented Kalman filter and ensemble Kalman filter