Hammerstein-Wiener nonlinear system identification by using honey badger algorithm hybridized Sage-Husa adaptive Kalman filter with real-time applications

Abstract

This paper introduces a hybrid Sage-Husa adaptive Kalman filter (SHAKF) with a metaheuristic algorithm to estimate the parameters of the Hammerstein-Wiener (H-W) model. The initial state parameters of the SHAKF must be appropriately tuned based on the estimation problem; otherwise, it would lead to divergence. To get the global optimal tuned SHAKF parameters for modelling the H-W system, this proposed work has formulated an objective function based on the identification problem optimised with a recently introduced metaheuristic technique called the Honey Badger algorithm (HBA). Finally, the unknown parameters of the H-W system are identified by using the basic SHAKF technique by considering the achieved optimised SHAKF parameters as the initial state inputs. The efficiency of the proposed technique is verified on numerical problems, and its practical feasibility is tested on mechanical systems, namely arm robots and ball-and-beam system identification based on real data sets. The experimental results warrant that the proposed technique possesses high robustness to additive noise, quick convergence speed and low steady-state error compared to the other recently reported methods. For comparison study, different metaheuristic algorithms, namely, comprehensive learning particle swarm optimiser (CLPSO), an ensemble of mutation strategies and control parameters with the differential evolution (EPSDE) and colliding bodies optimisation (CBO), are incorporated into SHAKF to estimate the H-W systems and the obtained results for various standard metrics show the superiority of the proposed HBA-aided SHAKF method over others.