Design of fractional-order hammerstein control auto-regressive model for heat exchanger system identification: Treatise on fuzzy-evolutionary computing - Mehmood, Ammara; Raja, Muhammad Asif Zahoor; Ninness, Brett (Chaos, solitons and fractals, 2024)
Parameter estimation of nonlinear dynamical Hammerstein processes is a renowned stiff optimization problem with extensive applications in the design, robustness and stability analysis. Introduction of the fractional calculus theories and concepts further escalates the competency of accurate modelling of Hammerstein system but at the cost of increase in the stiffness of parameter estimation and complexity. This study deals with a presentation of new design of fractional-order nonlinear Hammerstein control auto-regressive (FO-NHCAR) model for heat exchanger system by introducing fractional derivative of polynomial based transformation operator in linear dynamic block. The system identification problem of FO-NHCAR heat exchanger system is constructed by exploiting approximation theory in mean squared error sense taken between the actual and estimated responses. Exhaustive simulations are conducted via well-known global search efficacy of the fuzzy-evolutionary computing paradigm i.e., fuzzy-genetic algorithms (GAs), for FO-NHCAR heat exchanger model by variation in signal to noise ratios, model's degrees of freedom, fractional orders, and Hammerstein kernels. The parameter vectors of FO-NHCAR models are identified consistently with the fuzzy-GAs for various noisy environments with negligible proximity error. Results comparison on rigorous statistical analysis further endorse the efficient, accurate, robust and stable performance of fuzzy- GAs for estimation of FO-NHCAR heat exchanger system parameters. •Design of FO-NHCAR model for heat exchanger system is presented in this study with the introduction of fractional calculus concepts.•Fuzzy-GAs is exploited for FO-NHCAR heat exchanger model estimation by variation in SNR values and Hammerstein kernels.•The validation of the precision for the designed optimization strategy is substantiated through rigorous statistical analysis.