1#. • Basis Weight Control System
5BCMF Ȟ 401%5SFTVMUTGPS 1 VTJOHGPVS140 - CBTFEBMHPź SJUINT Methods Min Max Mean Std
Best parameter K S =1.000, T S 1 =0.996, T S 2 =0.221,
23.6
2908.1
354.2
902.6
Classical PSO
L S =0.000 K S =1.000, T S 1 =0.000, T S 2 =1.036, L S =0.000 K S =1.000, T S 1 =0.935, T S 2 =0.022, L S =0.294 K S =1.000, T S 1 =0.000, T S 2 =0.910, L S =0.354
26.5
2908.1
622.0
1091.5
PSO-TVAC
'JH Step response of the best SOPDT approaching P 4 using classical PSO, PSO-TVAC, MPSO, and SNPSO
22.3
2128.4
558.4
751.6
MPSO
22.3
1648.2
324.8
512.1
SNPSO
performance: the integral of absolute error (IAE), where the error in each case is the difference between the "real" process and model output. The statistical results demonstrate that the average convergence performance of SNPSO is better than other methods. Fig. 17 and Fig. 18 illustrate the best FOPDT and SOPDT step responses approximating P 4 using classical PSO, PSO-TVAC, MPSO, and SNPSO. Black line represents the primitive high-order function P 4 . The identification method based on optimization algorithm can make the identification model approach the primary model well. The approximation performance of the FOPDT model is better than that of the SOPDT model. Nyquist curve further proves that the FOPDT model is more
approximate to the primary model than the SOPDT model as illustrated in Fig. 19. ` #ONCLUSIONS Here, we introduced the application of the strange 'JH Nyquist curve: primitive high-order function P 4 (black line), and approximation model using PSO, PSO-TVAC, MPSO, and SNPSO
'JH Ȟ Step response of the best FOPDT approaching P 4 using classical PSO, PSO-TVAC, MPSO, and SNPSO
7PM /P
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