PAPERmaking! Vol9 Nr1 2023

1#. • Basis Weight Control System

algorithm is adopted for noisy step response, the identification accuracy rates of parameters K F , T F , and L F by SNPSO are increased from 40%, 20%, and 40% to 70%, 60%, and 50%, respectively. It can enhance the stability and robustness of SNPSO. 6.2 Ȟ Transfer function P 2 When the real process is assumed to be the transfer function (Eq. (12)), the estimation model (SOPTD model) is as follows: G S ( s ) = K S ( T S 1 s + 1) ( T S 2 s + 1) e ϖ L S s (16) Parameters K S , T S 1 , T S 2 , and L S are the estimation parameters and are characterized by the classical PSO, PSO-TVAC, MPSO, and proposed SNPSO. For zero noise step response ŷ ( t ) , Table 3 presents five performance indexes of the four methods. The accuracy and mean of identification (mean) using SNPSO are far better than the other three algorithms. The average convergence and stability of SNPSO are further verified

5BCMF 4ZTUFNJEFOUJGJDBUJPOPG4015%VTJOHGPVS140 - CBTFEBMHPSJUINT Methods Accuracy/% Min Max Mean Std

Best parameter K S =1, T S 1 =1, T S 2 =1, L S =1

10

0

6392

1786

2312

Classical PSO

K S =1, T S 1 =1, T S 2 =1, L S =1 K S =1, T S 1 =1, T S 2 =1, L S =1 K S =1, T S 1 =1, T S 2 =1, L S =1

10

0

6714

2112

2470

PSO-TVAC

50

0

6352

1242

2048

MPSO

90

0

6309

630

1995

SNPSO

by comparing the parameters K S , T S 1 , T S 2 , and L S of the estimation model obtained by running the four algorithms ten times (as illustrated in Fig. 10). Fig. 11 illustrates

'JH  Identification parameters of SOPTD with zero noise obtained by running the four algorithms ten times

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