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
7PM /P
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