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

Abstract

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

CLC Number:

TP273

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.

Figures/Tables 15

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Table 1

Table 2

Table 3

Table 4

Fig.6

Fig.7

Fig.8

Fig.9

Fig.10

Fig.11

References 31

1	WANG T L , ZHOU C , LU H , et al. Hybrid scheduling and quantized output feedback control for networked control systems[J]. International Journal of Control, Automation and Systems, 2018, 16 (1): 197- 206.
2	TRUONG D Q , AHN K K . Robust variable sampling period control for networked control systems[J]. IEEE Trans.on Industrial Electronics, 2015, 62 (9): 5630- 5643. doi: 10.1109/TIE.2015.2410765
3	YANG T, HUANG W, ZHAO Y X. A dynamic allocation strategy of bandwidth of networked control systems with bandwidth constraints[C]//Proc.of the IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, 2016: 1153-1158.
4	ZHANG Z W, JIA L, LIU X B. Multi-parameter NCS scheduling based on fuzzy neural network[C]//Proc.of the IEEE International Conference on Smart Internet of Things, 2018: 192-197.
5	BENITEZ-PEREZ H , ORTEGA-ARJONA J , ROJAS-VARGAS J A , et al. Design of a fuzzy networked control systems priority exchange scheduling algorithm[J]. International Journal of Computers Communications & Control, 2016, 11 (2): 179- 193.
6	WANG Y Z , WANG L D , YAN X , et al. Fuzzy immune particle swarm optimization algorithm and its application in scheduling of MVB periodic information[J]. Journal of Intelligent & Fuzzy Systems, 2017, 32 (6): 3797- 3807.
7	TIAN Z D , LI S J , WANG Y H , et al. Priority scheduling of networked control system based on fuzzy controller with self-tuning scale factor[J]. IAENG International Journal of Computer Science, 2017, 44 (3): 308- 315.
8	田中大, 高宪文, 史美华, 等. 资源受限网络控制系统的模糊反馈调度[J]. 电机与控制学报, 2013, 17 (1): 94- 101.
	TIAN Z D , GAO X W , SHI M H , et al. Fuzzy feedback sche-duling of resource constrained networked control system[J]. Electric Machines and Control, 2013, 17 (1): 94- 101.
9	田中大, 李树江, 王艳红, 等. 网络控制系统的动态权重变采样周期调度算法[J]. 哈尔滨工业大学学报, 2016, 48 (4): 114- 120.
	TIAN Z D , LI S J , W Y H , et al. A dynamic weight variable sampling periodical scheduling algorithm for networked control system[J]. Journal of Harbin Institute of Technology, 2016, 48 (4): 114- 120.
10	邱占芝, 李世峰. 基于神经网络的PID网络化控制系统建模与仿真[J]. 系统仿真学报, 2018, 30 (4): 1423- 1432.
	QIU Z Z , LI S F . Modeling and simulation of PID networked control systems based on neural network[J]. Journal of System Simulation, 2018, 30 (4): 1423- 1432.
11	TIAN Z D , LI S J , WANG Y H , et al. Scheduling method for networked control system with resource constraints based on fuzzy feedback priority and variable sampling period[J]. Transactions of the Institute of Measurement and Control, 2018, 40 (4): 1136- 1149. doi: 10.1177/0142331216677574
12	TIAN Z D , REN Y , WANG G . A variable sampling period scheduling method for networked control system under resource constraints[J]. Australian Journal of Electrical and Electronics Engineering, 2019, 16 (4): 289- 304. doi: 10.1080/1448837X.2019.1661335
13	时维国, 王力, 邵城. 资源受限系统的灰色神经网络变采样调度算法[J]. 系统仿真学报, 2018, 30 (4): 1272- 1278.
	SHI W G , WANG L , SHAO C . Variable sampling period scheduling algorithm based on grey neural networks with resource-constrained system[J]. Journal of System Simulation, 2018, 30 (4): 1272- 1278.
14	YOO W, SIM A. Network bandwidth utilization forecast model on high bandwidth networks[C]//Proc.of the International Conference on Computing, Networking and Communications, 2015: 494-498.
15	TIAN Z D , GAO X W , GONG B , et al. Time-delay compensation method for networked control system based on time-delay prediction and implicit PIGPC[J]. International Journal of Automation and Computing, 2015, 12 (6): 648- 656. doi: 10.1007/s11633-015-0897-7
16	TIPPING M E . Sparse Bayesian learning and the relevance vector machine[J]. Journal of Machine Learning Research, 2001, 1 (3): 211- 244.
17	SCHMOLCK A , EVERSON R . Smooth relevance vector machine: a smoothness prior extension of the RVM[J]. Machine Learning, 2007, 68 (2): 107- 135.
18	PATEL D , CHOTHANI N G , MISTRY K D , et al. Design and development of fault classification algorithm based on relevance vector machine for power transformer[J]. IET Electric Power Applications, 2018, 12 (4): 557- 565. doi: 10.1049/iet-epa.2017.0562
19	XU Y , SHEN S Q , HE Y L , et al. A novel hybrid method integrating ICA-PCA with relevant vector machine for multivariate process monitoring[J]. IEEE Trans.on Control Systems Technology, 2019, 27 (4): 1780- 1787. doi: 10.1109/TCST.2018.2816903
20	ZHANG C J , LUO Q , DAI L J , et al. Intensity estimation of tropical cyclones using the relevance vector machine from infrared satellite image data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12 (3): 763- 773. doi: 10.1109/JSTARS.2019.2894654
21	LIU J , ZHOU X R , HUANG J Y , et al. Semantic classification for hyperspectral image by integrating distance measurement and relevance vector machine[J]. Multimedia Systems, 2017, 23 (1): 95- 104.
22	MIANJI F A , ZHANG Y . Robust hyperspectral classification using relevance vector machine[J]. IEEE Trans.on Geoscience and Remote Sensing, 2011, 49 (6): 2100- 2112. doi: 10.1109/TGRS.2010.2103381
23	MIRJALILI S , LEWIS A . The whale optimization algorithm[J]. Advances in Engineering Software, 2016, 95, 51- 67. doi: 10.1016/j.advengsoft.2016.01.008
24	ALJARAH I , FARIS H , MIRJALILI S . Optimizing connection weights in neural networks using the whale optimization algorithm[J]. Soft Computing, 2018, 22 (1): 1- 15.
25	QIAO W B , HUANG K , AZIMI M , et al. A novel hybrid prediction model for hourly gas consumption in supply side based on improved whale optimization algorithm and relevance vector machine[J]. IEEE Access, 2019, 7, 88218- 88230. doi: 10.1109/ACCESS.2019.2918156
26	QIAO W B , YANG Z , KANG Z Y , et al. Short-term natural gas consumption prediction based on Volterra adaptive filter and improved whale optimization algorithm[J]. Engineering Applications of Artificial Intelligence, 2020, 87, 103323. doi: 10.1016/j.engappai.2019.103323
27	HEMASIAN-ETEFAGH F , SAFI-ESFAHANI F . Dynamic scheduling applying new population grouping of whales meta-heuristic in cloud computing[J]. Journal of Supercomputing, 2019, 75 (10): 6386- 6450. doi: 10.1007/s11227-019-02832-7
28	ABDEL-BASSET M , ABDLE-FATAH L , SANGAIAH A K . An improved Lévy based whale optimization algorithm for bandwidth-efficient virtual machine placement in cloud comput-ing environment[J]. Cluster Computing, 2019, 22 (4): 8319- 8334.
29	HASANIEN H M . Whale optimization algorithm for automatic generation control of interconnected modern power systems including renewable energy sources[J]. IET Generation Transmission & Distribution, 2018, 12 (3): 607- 614.
30	HASANIEN H M . Performance improvement of photovoltaic power systems using an optimal control strategy based on whale optimization algorithm[J]. Electric Power Systems Research, 2018, 157, 168- 176. doi: 10.1016/j.epsr.2017.12.019
31	RAZEGHI-JAHROMI M, SEYEDI A. Stabilization of distri-buted networked control systems with minimal communications network[C]//Proc.of the American Control Conference, 2012: 515-520.

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

[1]	SHI Weiguo, XU Chao. Time delay prediction based on phase space reconstruction and robust extreme learning machine [J]. Systems Engineering and Electronics, 2019, 41(2): 416-421.
[2]	LU Cheng, XU Tingxue, WANG Hong. State prediction method of radar seeker based on the unification of local relevance vector machine model [J]. Systems Engineering and Electronics, 2018, 40(1): 9-16.
[3]	GAO Mingzhe, XU Aiqiang, ZHANG Wei. Adaptive modeling relevance vector machine and its application in electronic system state prediction [J]. Systems Engineering and Electronics, 2017, 39(8): 1898-1904.
[4]	WANG Xiaodan, LI Rui, XUE Aijun, SUN Xiangfang. HRRP fusion recognition by a self-adaptive weighted majority vote strategy based on entropy [J]. Systems Engineering and Electronics, 2017, 39(4): 707-713.
[5]	CHEN Xiongzi1, YU Jinsong2, LU Wengao1, LI Xingshan2. Systemlevel failure prognostics using synthesized health index and relevance vector machine [J]. Systems Engineering and Electronics, 2015, 37(10): 2298-2305.
[6]	FAN Geng， MA Deng-wu， WU Ming-hui， MENG Shang. Condition time series prediction of electronic system based on optimized relevance vector machin [J]. Systems Engineering and Electronics, 2013, 35(9): 2011-2015.
[7]	TIAN Zhongda， GAO Xianwen， LI Kun. Networked control system timedelay prediction method based on KPCA and LSSVM [J]. Journal of Systems Engineering and Electronics, 2013, 35(6): 1281-1285.
[8]	TIAN Zhong-da, GAO Xian-wen, LI Kun. Networked control system time-delay compensation algorithm based on predictive control [J]. Systems Engineering and Electronics, 2013, 35(10): 2165-2169.
[9]	FAN Geng， MA Dengwu， DENG Li， LV Xiaofeng. Fault prognostic model based on grey relevance vector machine [J]. Journal of Systems Engineering and Electronics, 2012, 34(2): 424-428.
[10]	CHEN Gang, YANG Chunhua, ZHU Hongqiu. Analysis and synthesis for networked control systems with network-induced delay and data dropout problems [J]. Journal of Systems Engineering and Electronics, 2012, 34(2): 342-347.
[11]	MA Li-wei, TIAN Zuo-hua, SHI Song-jiao. Approach to design integrated observer/controller for networked control systems [J]. Journal of Systems Engineering and Electronics, 2011, 33(2): 395-398.
[12]	WANG Jun, LI Wei, LI Zhan-ming. Robust guaranteed fault-tolerant design for uncertain  NNCS based on T-S model [J]. Journal of Systems Engineering and Electronics, 2011, 33(10): 2288-2294.

Variable sampling period scheduling of networked control system based on whale optimization algorithm relevance vector machine

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 15

References 31

Related Articles 12

Recommended Articles

Metrics

Comments

隶属度		P_i量化等级
隶属度		1	2	3	4	5
P_i模糊子集	PS	1	0.2	0	0	0
	S	0.2	1	0.2	0	0
	M	0	0.2	1	0.2	0
	B	0	0	0.2	1	0.2
	PB	0	0	0	0.2	1