A Dynamic Model of Interval Uncertainty of Rotational Chain Shell Magazine

doi:10.12382/bgxb.2023.0379

Abstract

Abstract:

For the identification problem with interval uncertain parameters, a double-layer nested identification (DNI) method based on an interval possibility degree transformation model is proposed. By dividing the parameters to be identified into two categories, the first type of deterministic parameters are identified by DNI method, and the interval range of the second type of interval uncertainty parameters is optimized by the DNI-based interval optimization method. The BO-PSO algorithm is chosen as the inner-layer algorithm to improve the efficiency of the nested strategy type method. For the inner layer of DNI method, BO-PSO method is used to calculate the upper and lower bounds of interval, and for the outer layer, ICS method is used to identify the specific parameters. In order to shorten the solving time, an ICS-MK-ELM agent model is proposed. The ICS-MK-ELM agent model overcomes the difficulty of manually adjusting the hyper-parameters of each kernel function, and the prediction precision of the model is obviously higher than those of KELM and MK-ELM. Finally, the DNI method is applied to the parameter identification of the rotational chain shell magazine, which solves the problem of the parameter identification of the chain-type magazine with interval uncertainty. The results of parameter identification show that the DNI method and the interval optimization method based on DNI have higher accuracy and stability.

Key words: uncertainty, interval possibility degree, shell magazine, parameter identification, multiple kernel extreme learning machine, Bayesian optimization, cuckoo search algorithm

CLC Number:

TJ301

ZHAO Wei, HOU Baolin, YAN Shaojun, BAO Dan, LIN Yubin. A Dynamic Model of Interval Uncertainty of Rotational Chain Shell Magazine[J]. Acta Armamentarii, 2024, 45(6): 1991-2002.

Figures/Tables 16

Fig.1 Structure sketch of rotational chain shell magazine

Fig.2 Improvement effect diagram of friction model

Fig.3 Flowchart of DNI method

Fig.4 Six position relations of Intervals A and B

Fig.5 Agent model structure based on MK-ELM

Fig.6 Flowchart of ICS-MK-ELM

Fig.7 Comparison of predicted results of agent model

Fig.8 Motor speed

Fig.9 Motor current

Fig.10 Comparison of upper and lower bounds of the interval

Table 1 Identifiied results of the first type of parameters (no load)

方法	M_fc/(N·m)	M_fs/(N·m)	ω_s/(rad·s^-1)	A_s	B_e/(N·m·s·rad^-1)	标准差	极差	离散系数	相似度区间
区间	[0,10]	[0,10]	[0.1,3]	[0,100]	[0.1,10]
DNI	0.0457	7.7283	1.7906	88.14504	7.962252	0	0	0	[0.0354,0.1398]
文献[14]	2.1565	8.5718	1.6062	56.8612	7.2311	0.0152	0.0792	0.0285	[0.0353,0.1750]
文献[15]	0.0190	7.9256	2.6388	19.5739	8.0893	0.0161	0.0025	0.0756	[0.0354,0.1475]

Table 2 Identified results of the first type of parameters (half load)

方法	M_fc/(N·m)	M_fs/(N·m)	ω_s/(rad·s^-1)	A_s	B_e/(N·m·s·rad^-1)	标准差	极差	离散系数	相似度区间
区间	[0,20]	[0,20]	[0.1,3]	[0,100]	[0.1,10]
DNI	5.8666	12.6646	0.5839	0.0009	8.6948	0	0	0	[0.0241,0.1264]
文献[14]	3.7937	15.0262	1.4869	53.77662	8.3186	0.1468	0.3602	0.1590	[0.0231,0.3121]
文献[15]	3.0889	13.2096	1.1470	0.0228	9.5951	0.0178	0.0515	0.0876	[0.0216,0.1733]

Table 3 Identified results of the second type of parameters (no load)

方法	ε	J_e/(kg·m²)	$J · e$ /(kg·m²·s^-1)	标准差	极差	离散系数	相似度区间
	0.15	[2.0140,2.9750]	[-2.9764,1.4157]	0.0011	0.0020	0.0056	[0.0358,0.1368]
DNI	0.1	[2.4942,2.8982]	[-2.4516,0.8382]	0.0012	0.0022	0.0081	[0.0366,0.0998]
	0.05	[2.6867,2.9641]	[-1.6102, -0.5150]	0.0007	0.0014	0.0083	[0.0359,0.0486]
	0.15	[2.0146,2.9847]	[-2.9798,1.4808]	0.0260	0.0907	0.0005	[0.0355,0.1465]
文献[15]	0.1	[2.4384,2.9884]	[-2.7622,0.5517]	0.0345	0.1439	0.0108	[0.0354,0.1018]
	0.05	[2.6380,2.9943]	[-1.9418,-0.7364]	0.0109	0.1001	0.0071	[0.0354,0.0508]

Table 3 Identified results of the second type of parameters (no load)

方法	ε	J_e/(kg·m²)	$J · e$ /(kg·m²·s^-1)	标准差	极差	离散系数	相似度区间
	0.15	[2.0140,2.9750]	[-2.9764,1.4157]	0.0011	0.0020	0.0056	[0.0358,0.1368]
DNI	0.1	[2.4942,2.8982]	[-2.4516,0.8382]	0.0012	0.0022	0.0081	[0.0366,0.0998]
	0.05	[2.6867,2.9641]	[-1.6102, -0.5150]	0.0007	0.0014	0.0083	[0.0359,0.0486]
	0.15	[2.0146,2.9847]	[-2.9798,1.4808]	0.0260	0.0907	0.0005	[0.0355,0.1465]
文献[15]	0.1	[2.4384,2.9884]	[-2.7622,0.5517]	0.0345	0.1439	0.0108	[0.0354,0.1018]
	0.05	[2.6380,2.9943]	[-1.9418,-0.7364]	0.0109	0.1001	0.0071	[0.0354,0.0508]

Table 4 Identified results of the second type of parameters (half load)

方法	ε	J_e/(kg·m²)	$J · e$ /(kg·m²·s^-1)	标准差	极差	离散系数	相似度区间
	0.15	[4.0301,4.9414]	[-5.9923,2.9921]	0.0027	0.0014	0.0008	[0.0246,0.1258]
DNI	0.1	[4.0025,4.9070]	[-0.3079,2.9863]	0.0019	0.0002	0.0009	[0.0241,0.0981]
	0.05	[4.0689,4.2069]	[0.8067,2.9926]	0.0033	0.0015	0.0013	[0.0252,0.0488]
	0.15	[4.0210,4.9750]	[-5.9676,2.9744]	0.0251	0.0661	0.0025	[0.0217,0.143]
文献[15]	0.1	[4.0204,4.9040]	[-0.4373,2.9504]	0.0526	0.1423	0.0123	[0.0244,0.1017]
	0.05	[4.0686,4.2515]	[0.8364,2.9583]	0.0805	0.2448	0.0341	[0.0253,0.0495]

Table 4 Identified results of the second type of parameters (half load)

方法	ε	J_e/(kg·m²)	$J · e$ /(kg·m²·s^-1)	标准差	极差	离散系数	相似度区间
	0.15	[4.0301,4.9414]	[-5.9923,2.9921]	0.0027	0.0014	0.0008	[0.0246,0.1258]
DNI	0.1	[4.0025,4.9070]	[-0.3079,2.9863]	0.0019	0.0002	0.0009	[0.0241,0.0981]
	0.05	[4.0689,4.2069]	[0.8067,2.9926]	0.0033	0.0015	0.0013	[0.0252,0.0488]
	0.15	[4.0210,4.9750]	[-5.9676,2.9744]	0.0251	0.0661	0.0025	[0.0217,0.143]
文献[15]	0.1	[4.0204,4.9040]	[-0.4373,2.9504]	0.0526	0.1423	0.0123	[0.0244,0.1017]
	0.05	[4.0686,4.2515]	[0.8364,2.9583]	0.0805	0.2448	0.0341	[0.0253,0.0495]

Fig.11 Identification curve family under no-load condition

Fig.12 Identification curve family under half load condition

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