Tension Active Disturbance Rejection Control of Automatic Yarn Splicing Robots for Ring Spinning

Lisu WANG , Yun CAI , Cheng JI , Junliang WANG

Journal of Donghua University(English Edition) ›› 2024, Vol. 41 ›› Issue (5) : 505 -512.

PDF (9175KB)
Journal of Donghua University(English Edition) ›› 2024, Vol. 41 ›› Issue (5) :505 -512. DOI: 10.19884/j.1672-5220.202405008
Intelligent Detection and Control
research-article

Tension Active Disturbance Rejection Control of Automatic Yarn Splicing Robots for Ring Spinning

Author information +
History +
PDF (9175KB)

Abstract

Automatic splicing of interrupted yarns in ring spinning has always been a problem in the industry. Factors such as low yarn strengths and environmental influence on yarn tensions make it difficult to control the yarn tension during the robotic splicing process. The purpose of this research is to design active disturbance rejection control(ADRC) for a third-order nonlinear tension system subject to external disturbances. Firstly, a third-order extended state observer(ESO) is designed to achieve the suppression and the compensation of the internal modeling error and the external disturbances of the system. Secondly, the adaptive gain error feedback control and the filtering process are designed to reduce the influence of sensor noise on the disturbance observation. Finally, the tension control during the splicing process is simulated and experimented, and the experiments show that the method has good robustness in the tension tracking task under a dynamic environment, which verifies the effectiveness of the method.

Keywords

yarn splicing robot / tension control / active disturbance rejection control (ADRC) / extended state observer(ESO)

Cite this article

Download citation ▾
Lisu WANG, Yun CAI, Cheng JI, Junliang WANG. Tension Active Disturbance Rejection Control of Automatic Yarn Splicing Robots for Ring Spinning. Journal of Donghua University(English Edition), 2024, 41(5): 505-512 DOI:10.19884/j.1672-5220.202405008

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

PENG Z Y, LI W, XU D, et al. Analysis of ring spinning technology and construction of ideal yarn structure[J]. Cotton Textile Technology, 2023, 51(11):1-6. (in Chinese)

[2]

TANG X J, SONG J Y, HE X D. Technology progress of ring spinning automatic piecing device at home and abroad[J]. Cotton Spinning Technology, 2019, 47(1):78-84. (in Chinese)

[3]

ZHANG Y S, WANG X H, HE Y. Research progress on key technology of ring spinning frame automatic joint[J]. Cotton Textile Technology, 2023, 51(10):90-96. (in Chinese)

[4]

KOLHE J P, SHAHEED M, CHANDAR T S, et al. Robust control of robot manipulators based on uncertainty and disturbance estimation[J]. International Journal of Robust and Nonlinear Control, 2013, 23(1):104-122.

[5]

ISLAM S, LIU P X, EL SADDIK A. Robust control of four-rotor unmanned aerial vehicle with disturbance uncertainty[J]. IEEE Transactions on Industrial Electronics, 2015, 62(3):1563-1571.

[6]

RIAZ M, YASIN A R, ARSHAD UPPAL A, et al. A novel dynamic integral sliding mode control for power electronic converters[J]. Science Progress, 2021, 104(4):003685042110448.

[7]

QU Y, ZHANG B, CHU H R, et al. Sliding-mode anti-disturbance speed control of permanent magnet synchronous motor based on an advanced reaching law[J]. ISA Transactions, 2023,139:436-447.

[8]

HOU Q K, DING S H, YU X H. Composite super-twisting sliding mode control design for PMSM speed regulation problem based on a novel disturbance observer[J]. IEEE Transactions on Energy Conversion, 2021, 36(4):2591-2599.

[9]

NAWRESS B, LAKHAL A N G, BRAÏEK N B. Neural state and disturbance observer-based sliding mode control of a unicycle robot[C]//2023 IEEE International Conference on Advanced Systems and Emergent Technologies. Hammamet,Tunisia: IEEE, 2023:1-6.

[10]

YANG G C, YAO J Y, DONG Z L. Neuroadaptive learning algorithm for constrained nonlinear systems with disturbance rejection[J]. International Journal of Robust and Nonlinear Control, 2022, 32(10):6127-6147.

[11]

HE T F, WU Z. Neural network disturbance observer with extended weight matrix for spacecraft disturbance attenuation[J]. Aerospace Science and Technology, 2022,126:107572.

[12]

CHENG X, LU W K, LIU H S. Adaptive neural network control for Euler-Lagrangian systems with uncertainties[J]. Journal of Donghua University (English Edition), 2022, 39(5):485-489.

[13]

HAN J Q. From PID to active disturbance rejection control[J]. IEEE Transactions on Industrial Electronics, 2009, 56(3):900-906.

[14]

WEI W, ZHANG Z Y, ZUO M. Phase leading active disturbance rejection control for a nanopositioning stage[J]. ISA Transactions, 2021,116:218-231.

[15]

GUO B Z, ZHAO Z L. Active disturbance rejection control for nonlinear systems:an introduction[M]. Hoboken,NJ,USA: John Wiley & Sons, 2017.

[16]

HUANG Y, XUE W C. Active disturbance rejection control:methodology and theoretical analysis[J]. ISA Transactions, 2014, 53(4):963-976.

[17]

FENG H, GUO B Z. Active disturbance rejection control:old and new results[J]. Annual Reviews in Control, 2017,44:238-248.

[18]

SARIYILDIZ E, OBOE R, OHNISHI K. Disturbance observer-based robust control and its applications:35th anniversary overview[J]. IEEE Transactions on Industrial Electronics, 2020, 67(3):2042-2053.

[19]

ŁAKOMY K, MADONSKI R. Cascade extended state observer for active disturbance rejection control applications under measurement noise[J]. ISA Transactions, 2021,109:1-10.

[20]

LAKOMY K, MADONSKI R, DAI B, et al. Active disturbance rejection control design with suppression of sensor noise effects in application to DC-DC buck power converter[J]. IEEE Transactions on Industrial Electronics, 2022, 69(1):816-824.

[21]

DU Y W, CAO W H, SHE J H. Analysis and design of active disturbance rejection control with an improved extended state observer for systems with measurement noise[J]. IEEE Transactions on Industrial Electronics, 2023, 70(1):855-865.

[22]

KHALIL H K, PRIESS S. Analysis of the use of low-pass filters with high-gain observers[J]. IFAC-PapersOnLine, 2016, 49(18):488-492.

[23]

BAI W Y, XUE W C, HUANG Y, et al. On extended state based Kalman filter design for a class of nonlinear time-varying uncertain systems[J]. Science China Information Sciences, 2018, 61(4):042201.

[24]

SUN H, MADONSKI R, LI S H, et al. Composite control design for systems with uncertainties and noise using combined extended state observer and Kalman filter[J]. IEEE Transactions on Industrial Electronics, 2022, 69(4):4119-4128.

[25]

XUE W C, BAI W Y, YANG S, et al. ADRC with adaptive extended state observer and its application to air-fuel ratio control in gasoline engines[J]. IEEE Transactions on Industrial Electronics, 2015, 62(9):5847-5857.

[26]

HOU Q, ZUO Y, WANG H, et al. High-order NESO based enhanced ADRC for PMSM drives considering uncertainty and measurement noise suppression[C]//IECON 2022-48th Annual Conference of the IEEE Industrial Electronics Society. New York: IEEE, 2022:1-6.

[27]

ZHANG J, CUI C, GU S, et al. Trajectory tracking control of pneumatic servo system:a variable gain ADRC approach[J]. IEEE Transactions on Cybernetics, 2022, 53(11):6977-6986.

Funding

National Natural Science Foundation of China(52275478)

Fundamental Research Funds for the Central Universities, China(2232024Y-01)

DHU Distinguished Young Professor Program, China(LZB2023001)

PDF (9175KB)

73

Accesses

0

Citation

Detail

Sections
Recommended

/