CIESC Journal ›› 2019, Vol. 70 ›› Issue (2): 541-547.doi: 10.11949/j.issn.0438-1157.20181366

• Process system engineering • Previous Articles     Next Articles

Infinite horizon linear quadratic hybrid fault-tolerant control for multi-phase batch process

Limin WANG1(),Libin LU1,Furong GAO2,Donghua ZHOU3   

  1. 1. School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, Hainan, China
    2. Department of Chemical and Biomolecular Engineering, Hong Kong University of Science and Technology, Hong Kong, China
    3. College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, Shandong, China
  • Received:2018-11-07 Revised:2018-11-23 Online:2019-02-05 Published:2018-12-04
  • Contact: Limin WANG E-mail:wanglimin0817@163.com

Abstract:

A hybrid fault-tolerant controller with infinitely adjustable time-domain parameters is designed to ensure fault-tolerant control performance. Firstly a multi-phase state space model by acquiring input and output data is established, and further the state space model is into transformed an extended state space model containing state variables and output tracking errors, and the switched system model is used to represent it, so as to design the controller in an infinite horizon. Then, to obtain the minimum running time, the dwell time method depending on the Lyapunov function is proposed for different stages. Finally, taking the injection molding process as an example, the system simulation is carried out. The simulation shows that the proposed method is feasible and effective.

Key words: batch processes, switched systems, process control, control, systems engineering, actuator failure

CLC Number: 

  • TB 114.43

Fig.1

Tracking error of the 20th batch"

Fig.2

The 20th batch of one-dimensional input"

Fig.3

The 20th of batch one-dimensional output(1bar=0.1 MPa)"

1 WangY Q, ShiJ, ZhouD H, et al. Iterative learning fault-tolerant control for batch processes[J]. Industrial & Engineering Chemistry Research, 2006, 45(26): 9050-9060.
2 WangY Q, ZhouD H, GaoF R. Iterative learning reliable control of batch processes with sensor faults[J]. Chemical Engineering Science, 2008, 63(4): 1039-1051.
3 WangY Q, ZhouD H, GaoF R. Generalized predictive control of linear systems with actuator arrearage faults[J]. Journal of Process Control, 2009, 19(5): 803-815.
4 AumiS, MhaskarP. Robust model predictive control and fault handling of batch processes[J]. AIChE Journal, 2011, 57(7): 1796-1808.
5 WangL M, DongW W. Optimal iterative learning fault-tolerant guaranteed cost control for batch processes in the 2D-FM model[J]. Abstract and Applied Analysis, 2014, 2012(5): 919-929.
6 ZhangR D, GanL Z, LuJ Y, et al. New design of state space linear quadratic fault-tolerant tracking control for batch processes with partial actuator failure[J]. Industrial & Engineering Chemistry Research, 2013, 52(46): 16294-16300.
7 WangL M, ChenX, GaoF R. An LMI method to robust iterative learning fault-tolerant guaranteed cost control for batch processes[J]. Chinese Journal of Chemical Engineering, 2013, 21(4): 401-411.
8 ZhangR D, LuJ Y, QuH Y, et al. State space model predictive fault-tolerant control for batch processes with partial actuator failure[J]. Journal of Process Control, 2014, 24(5): 613-620.
9 ZhangR D, ZouH B, XueA K, et al. GA based predictive functional control for batch processes under actuator faults[J]. Chemometrics and Intelligent Laboratory Systems, 2014, 137(1): 67-73.
10 ZhangR D, LuR Q, XueA K, et al. Predictive functional control for linear systems under partial actuator faults and application on an injection molding batch process[J]. Ind.Eng.Chem. Res., 2014, 53(2): 723-731.
11 ZhangR D, JinQ B, GaoF R. Design of state space linear quadratic tracking control using GA optimization for batch processes with partial actuator failure[J]. Journal of Process Control, 2015, 26: 102-114.
12 ZhangR D, GaoF R. Improved infinite horizon LQ tracking control for injection molding process against partial actuator failures[J]. Computers & Chemical Engineering, 2015, 80: 130-139.
13 ZhangR D, LuR Q, XuaA K, et al. New minmax linear quadratic fault-tolerant tracking control for batch processes[J]. IEEE Transactions on Automatic Control, 2016, 61(10): 3045-3051.
14 WangL M, LiuF F, YuJ X, et al. Iterative learning fault-tolerant control for injection molding processes against actuator faults[J]. Journal of Process Control, 2017, 59: 59-72.
15 ZouT, WuS, ZhangR D. Improved state space model predictive fault-tolerant control for injection molding batch processes with partial actuator faults using GA optimization[J]. ISA Transactions, 2018, 73: 147-153.
16 WangY Q, ZhouD H, GaoF R. Active fault-tolerant control of nonlinear batch processes with sensor faults[J]. Industrial & Engineering Chemistry Research, 2007, 46(26): 9158-9169.
17 PengK, ZhangK, YouB, et al. A quality-based nonlinear fault diagnosis framework focusing on industrial multimode batch processes[J]. IEEE Transactions on Industrial Electronics, 2016, 63(4): 2615-2624.
18 WangL M, ZhuC J, YuJ X, et al. Fuzzy iterative learning control for batch processes with interval time-varying delays[J]. Industrial & Engineering Chemistry Research, 2017, 56(14): 3993-4001.
19 AltinB, BartonK. Exponential stability of nonlinear differential repetitive processes with applications to iterative learning control[J]. Automatica, 2017, 81: 369-376.
20 WangL M, LiB Y, YuJ X, et al. Design of fuzzy iterative learning fault-tolerant control for batch processes with time-varying delays[J]. Optimal Control Applications & Methods, 2018, 39: 1887-1903.
21 LuJ Y, CaoZ X, ZhangR D, et al. Nonlinear monotonically convergent iterative learning control for batch processes[J]. IEEE Transactions on Industrial Electronics, 2018, 65(7): 5826-5836.
22 LiuT, GaoF. A generalized relay identification method for time delay and non-minimum phase processes[J]. Automatica, 2010, 45(4): 1072-1079.
23 WangL M, MoS Y, ZhouD H, et al. Robust delay dependent iterative learning fault-tolerant control for batch processes with state delay and actuator failures[J]. Journal of Process Control, 2012, 7(22): 1273-1286.
24 WangL M, MoS Y, ZhouD H, et al. Delay-range-dependent method for iterative learning fault-tolerant guaranteed cost control for batch processes[J]. Industrial & Engineering Chemistry Research, 2013, 52(7): 2661-2671.
25 LiuC. Optimal control of a switched autonomous system with time delay arising in fed-batch processes[J]. IMA Journal of Applied Mathematics, 2015, 80(2): 569-584.
26 WangY M, ZhaoD H, LiY Y, et al. Unbiased minimum variance fault and state estimation for linear discrete time-varying two-dimensional systems[J]. IEEE Transactions on Automatic Control, 2017, 62(10): 5463-5469.
27 TaoH, PaszkeW, RogersE, et al. Iterative learning fault-tolerant control for differential time-delay batch processes in finite frequency domains[J]. Journal of Process Control, 2017, 56: 112-128.
28 WangL M, HeX, ZhouD H. Average dwell time-based optimal iterative learning control for multi-phase batch processes[J]. Journal of Process Control, 2016, 40: 1-12.
29 GaoM, ShengL, ZhouD H, et al. Iterative learning fault-tolerant control for networked batch processes with multi-rate sampling and quantization effects[J]. Industrial & Engineering Chemistry Research, 2017, 56(9): 2515-2525.
30 WangL M, ShengY T, YuJ X, et al. Robust iterative learning control for multi-phase batch processes: an average dwell-time method with 2D convergence indexes[J]. International Journal of Systems Science, 2018, 49(2): 324-343.
31 LuJ Y, CaoZ X, GaoF R. A multi-point iterative learning model predictive control for batch processes[J]. IEEE Transactions on Industrial Electronics, 2018, DOI: 10.1109/TIE.2018.2873133.
doi: 10.1109/TIE.2018.2873133
[1] Enwei ZHI, Fei YAN, Mifeng REN, Gaowei YAN. Soft sensor of wet ball mill load parameters based on transfer variational autoencoder - label mapping [J]. CIESC Journal, 2019, 70(S1): 150-157.
[2] Weifeng XU, Aipeng JIANG, Haokun WANG, Enhui JIANG, Qiang DING, Hanhan GAO. A grid reconstruction strategy based on pseudo Wigner-Ville analysis for dynamic optimization problem [J]. CIESC Journal, 2019, 70(S1): 158-167.
[3] Dong HUANG, Xionglin LUO. Judgement of process transition control strategies for large-range conditions change of chemical processes [J]. CIESC Journal, 2019, 70(5): 1848-1857.
[4] Fei LI, Cuili YANG, Wenjing LI, Junfei QIAO. Optimal control of wastewater treatment process using NSGAII algorithm based on multi-objective uniform distribution [J]. CIESC Journal, 2019, 70(5): 1868-1878.
[5] Aipeng JIANG, Quannan ZHANG, Haokun WANG, Qiang DING, Weifeng XU, Jian WANG. An improved dynamic real time optimization strategy for heat pump heating system [J]. CIESC Journal, 2019, 70(4): 1494-1504.
[6] Shipin YANG, Zhen HUANG, Lijuan LI, Jianquan SONG, Jing YE, Hui WANG. A deep dive diagnostic and correction algorithm for mismatched sub-models in complicated chemical processes [J]. CIESC Journal, 2019, 70(4): 1485-1493.
[7] Bowen SHI, Yanyan YIN, Fei LIU. Optimal control strategies combined with PSO and control vector parameterization for batchwise chemical process [J]. CIESC Journal, 2019, 70(3): 979-986.
[8] Zhizhen WANG, Zhiyun ZOU. Nonlinear predictive control strategies of pH neutralization process based on neural networks [J]. CIESC Journal, 2019, 70(2): 678-686.
[9] Weiqing HUANG, Pingru XU, Yu QIAN. Atmospheric environment risk analysis of oil consuming by vehicles based on FTA method: taking Hangzhou as a case study [J]. CIESC Journal, 2019, 70(2): 661-669.
[10] Qilei LIU, Kun FENG, Linlin LIU, Jian DU, Qingwei MENG, Lei ZHANG. Reaction solvent design method based on Dragon descriptors and modified decision tree-genetic algorithm [J]. CIESC Journal, 2019, 70(2): 533-540.
[11] Xiuhui HUANG, Jun WANG, Guomin CUI. Dynamic simulation and analysis of control strategies of acetic acid dehydration tower in PTA plant [J]. CIESC Journal, 2019, 70(2): 625-633.
[12] Zhencheng YE, Huanlan ZHOU, Debao RAO. Hybrid modeling and optimization of acetylene hydrogenation process [J]. CIESC Journal, 2019, 70(2): 496-507.
[13] Junren BAI, Jun YI, Qian LI, Ling WU, Xuemei CHEN. Multi-objective optimization of QPSO for thereaction-regeneration process [J]. CIESC Journal, 2019, 70(2): 750-756.
[14] Zhiqiang GENG, Shaoxing JING, Ju BAI, Zhongkai WANG, Qunxiong ZHU, Yongming HAN. Improved intelligent warning method based on MWSPCA-CBR and its application in petrochemical industries [J]. CIESC Journal, 2019, 70(2): 572-580.
[15] Xiaohan ZHANG, Pingjiang WANG, Xiangbai GU, Yuan XU, Yanlin HE, Qunxiong ZHU. Research on principal components extraction based robust extreme learning machine(PCE-RELM) and its application to modeling chemical processes [J]. CIESC Journal, 2019, 70(2): 475-480.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!