鲸鱼优化相关向量机的网络控制系统变采样周期调度

doi:10.3969/j.issn.1001-506X.2020.10.25

摘要/Abstract

摘要：

针对资源受限的网络控制系统,提出一种基于鲸鱼优化相关向量机的变采样周期调度算法。通过网络监测模块获取网络带宽与数据传输时间数据,建立鲸鱼优化相关向量机的预测模型,实现对网络带宽及数据传输时间的预测。采用模糊推理计算系统各回路通信带宽的分配权重,进而结合通信带宽及数据传输时间的预测值对各闭环回路的采样周期进行计算,完成采样周期的实时调节。仿真结果表明,在资源受限条件下,所提算法保证了系统的稳定性与控制精度。

关键词: 网络控制系统, 鲸鱼优化算法, 相关向量机, 模糊反馈调度, 变采样周期

Abstract:

Aiming at the resource constrained networked control system (NCS), a variable sampling period scheduling algorithm based on the whale optimization algorithm (WOA) relevance vector machine (RVM) is proposed. The network bandwidth and data transmission time data are obtained by the data transmission time monitoring module. The prediction model of WOA-RVM is established to realize the prediction of network bandwidth and data transmission time. Fuzzy inference is used to calculate the distribution weight of communication bandwidth in each loop of the system, and then the sampling period of each closed-loop loop is calculated according to the prediction value of communication bandwidth and data transmission time to complete the real-time adjustment of sampling period. Simulation results show that the proposed algorithm guarantees the stability and control accuracy of the system under the condition of resource constraints.

Key words: networked control system (NCS), whale optimization algorithm (WOA), relevance vector machine (RVM), fuzzy feedback scheduling, variable sampling period

中图分类号:

TP273

时维国, 国明. 鲸鱼优化相关向量机的网络控制系统变采样周期调度[J]. 系统工程与电子技术, 2020, 42(10): 2348-2355.

Weiguo SHI, Ming GUO. Variable sampling period scheduling of networked control system based on whale optimization algorithm relevance vector machine[J]. Systems Engineering and Electronics, 2020, 42(10): 2348-2355.

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隶属度		E_i/EC_i量化等级
隶属度		-4	-3	-2	-1	0	1	2	3	4
E_i/EC_i模糊子集	NB	1	0.5	0	0	0	0	0	0	0
	NS	0	0.5	1	0.5	0	0	0	0	0
	ZO	0	0	0	0.5	1	0.5	0	0	0
	PS	0	0	0	0	0	0.5	1	0.5	0
	PB	0	0	0	0	0	0	0	0.5	1

P_i		E_i
P_i		NB	NS	ZO	PS	PB
EC_i	NB	PS	PS	B	S	PS
	NS	PS	S	M	B	PS
	ZO	PS	M	PB	M	PS
	PS	PS	B	M	S	PS
	PB	PS	S	S	PS	PS

回路	预测传输时间δ²值	预测网络带宽δ²值
1	17.215 6	65.584 6
2	12.726 7	59.874 3
3	11.341 6	67.764 2

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