Optimal design of cascaded Wiener-Hammerstein system using a heuristically supervised discrete Kalman filter with application on benchmark problems
作者：
Highlights：
• First attempt is made on Wiener-Hammerstein model estimation by the proposed method.
• Simulations carried out on numerical examples and also on benchmark plants.
• Various performance metrics are used to measure the estimated parameter accuracy.
• The result of proposed method is compared with other employed benchmark algorithms.
• Consistency of results obtained by the adopted methods is verified by Hypothesis test.
摘要
•First attempt is made on Wiener-Hammerstein model estimation by the proposed method.•Simulations carried out on numerical examples and also on benchmark plants.•Various performance metrics are used to measure the estimated parameter accuracy.•The result of proposed method is compared with other employed benchmark algorithms.•Consistency of results obtained by the adopted methods is verified by Hypothesis test.
论文关键词：Identification,Wiener-Hammerstein model,Kalman filter,Harris Hawks optimiser,Benchmark plants
论文评审过程：Received 11 September 2021, Revised 27 January 2022, Accepted 28 March 2022, Available online 2 April 2022, Version of Record 7 April 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.117065
