TY - GEN
T1 - Applying Joint and Dual State and Parameter Estimation Using Derivative-Free Kalman Filter for a Switched Nonlinear System
AU - Elenchezhiyan, M.
AU - Thirunavukkarasu, I.
AU - Kumar, E. Govinda
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - In a hybrid system, the continuous and discrete modes are interaction with each other which results that the system states are operated in nonlinear dynamics and complexity of the system is increased. When the system dynamics are subjected to state and measurement uncertainty with parametric uncertainty, online parameter estimation of such a hybrid system is a difficult problem. Estimation of hybrid system states and parameters is a critical aspect in fault classification and identification, as well as fault tolerant control and adaptive control applications. Dual unscented Kalman filter (DUKF) and joint unscented Kalman filter (JUKF) are used to estimate state and parameters of the system based on dual state and parameter estimation approaches. The simulations were carried out for the performance evaluation of JUKF and DUKF on benchmark system, namely switched mode continuous stirred tank reactor (CSTR). According to simulation data, DUKF outperforms JUKF for switched nonlinear systems, and choice of recommendation is DUKF for simultaneous state and parameter estimation of switched nonlinear hybrid systems.
AB - In a hybrid system, the continuous and discrete modes are interaction with each other which results that the system states are operated in nonlinear dynamics and complexity of the system is increased. When the system dynamics are subjected to state and measurement uncertainty with parametric uncertainty, online parameter estimation of such a hybrid system is a difficult problem. Estimation of hybrid system states and parameters is a critical aspect in fault classification and identification, as well as fault tolerant control and adaptive control applications. Dual unscented Kalman filter (DUKF) and joint unscented Kalman filter (JUKF) are used to estimate state and parameters of the system based on dual state and parameter estimation approaches. The simulations were carried out for the performance evaluation of JUKF and DUKF on benchmark system, namely switched mode continuous stirred tank reactor (CSTR). According to simulation data, DUKF outperforms JUKF for switched nonlinear systems, and choice of recommendation is DUKF for simultaneous state and parameter estimation of switched nonlinear hybrid systems.
UR - https://www.scopus.com/pages/publications/85208028064
UR - https://www.scopus.com/pages/publications/85208028064#tab=citedBy
U2 - 10.1007/978-981-97-4650-7_33
DO - 10.1007/978-981-97-4650-7_33
M3 - Conference contribution
AN - SCOPUS:85208028064
SN - 9789819746491
T3 - Lecture Notes in Electrical Engineering
SP - 441
EP - 458
BT - Intelligent Control, Robotics, and Industrial Automation - Proceedings of International Conference, RCAAI 2023
A2 - Suresh, Shilpa
A2 - Lal, Shyam
A2 - Kiran, Mustafa Servet
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Robotics, Control, Automation and Artificial Intelligence, RCAAI 2023
Y2 - 12 October 2023 through 14 October 2023
ER -