Novel Fractional Swarming with Key Term Separation for Input Nonlinear Control Autoregressive Systems

In recent decades, fractional order calculus has become an important mathematical tool for effectively solving complex problems through better modeling with the introduction of differential/integral operators; swarming heuristics are also introduced and applied performance in different optimization tasks. This study investigates nonlinear system identification problem input control autoregressive (IN-CAR) model novel implementation particle swarm (FO-PSO) heuristics; further, key term separation technique (KTST) is FO-PSO to solve over-parameterization issue involved parameter estimation IN-CAR model. The proposed KTST-based FO-PSO, i.e., KTST-FOPSO accurately estimates parameters unknown robust cases noise scenarios. investigated exhaustively orders as well comparison standard counterpart. results statistical indices Monte Carlo simulations endorse reliability stability identification.