Optimal design of cascaded Wiener-Hammerstein system using a heuristically supervised discrete Kalman filter with application on benchmark problems

Abstract

Block oriented models are recurrently employed to describe the dynamic characteristics of nonlinear systems. This paper adopts an efficacious block-oriented model called cascaded Wiener-Hammerstein (W-H) structure to model a nonlinear system. The parameters of the W-H model are identified using a nature-inspired evolution algorithm-supported Kalman filter (KF). In the proposed technique, the utility of the employed optimisation algorithm named Harris Hawks optimiser (HHO) is to acquire the optimal global solution of initial state KF parameters for the W-H system identification problem. Then, the conventional KF algorithm utilises these optimised KF parameters to beget the near-global estimated W-H system parameters. The effectiveness of the proposed HHO-based KF algorithm is verified through a comparison study with other variants of particle swarm optimisation (PSO) and harmony search (HS) algorithms-supported KF methods on two numerical W-H problems with different nonlinearity levels and two practical benchmark plants, namely, real electronic diode switching network and flutter clearance of F-18 systems research aircraft. The simulation results ensure that the proposed HHO-KF approach offers significantly improved results over other competing methods in terms of various well known standard metrics. The hypothesis test verifies the stability and consistency of the proposed identification method. Moreover, the proposed approach provides a faster convergence rate and the lowest estimation precision compared with other reported techniques.