Fractional-order PID servo control based on decoupled visual model
Corresponding Author
Weipeng Liu
State Key Laboratory for Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China
School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
Engineering Research Center of Intelligent Rehabilitation and Detecting Technology, Ministry of Education, Tianjin, China
Hebei Control Engineering Technology Research Center, Hebei University of Technology, Tianjin, China
Weipeng Liu, State Key Laboratory for Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; or School of Artificial Intelligence, Hebei University of Technology, Tianjin 300130, China; or Engineering Research Center of Intelligent Rehabilitation and Detecting Technology, Ministry of Education, Tianjin 300130, China; or Hebei Control Engineering Technology Research Center, Hebei University of Technology, Tianjin 300130, China.
E-mail: [email protected]
Search for more papers by this authorGui-Bin Bian
State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
Search for more papers by this authorMuhammad Rameez Ur Rahman
School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
Search for more papers by this authorHaojie Zhang
School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
Search for more papers by this authorHaiyong Chen
State Key Laboratory for Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China
School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
Engineering Research Center of Intelligent Rehabilitation and Detecting Technology, Ministry of Education, Tianjin, China
Hebei Control Engineering Technology Research Center, Hebei University of Technology, Tianjin, China
Search for more papers by this authorWanqing Wu
CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Shenzhen, China
Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
Search for more papers by this authorCorresponding Author
Weipeng Liu
State Key Laboratory for Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China
School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
Engineering Research Center of Intelligent Rehabilitation and Detecting Technology, Ministry of Education, Tianjin, China
Hebei Control Engineering Technology Research Center, Hebei University of Technology, Tianjin, China
Weipeng Liu, State Key Laboratory for Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; or School of Artificial Intelligence, Hebei University of Technology, Tianjin 300130, China; or Engineering Research Center of Intelligent Rehabilitation and Detecting Technology, Ministry of Education, Tianjin 300130, China; or Hebei Control Engineering Technology Research Center, Hebei University of Technology, Tianjin 300130, China.
E-mail: [email protected]
Search for more papers by this authorGui-Bin Bian
State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
Search for more papers by this authorMuhammad Rameez Ur Rahman
School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
Search for more papers by this authorHaojie Zhang
School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
Search for more papers by this authorHaiyong Chen
State Key Laboratory for Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China
School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
Engineering Research Center of Intelligent Rehabilitation and Detecting Technology, Ministry of Education, Tianjin, China
Hebei Control Engineering Technology Research Center, Hebei University of Technology, Tianjin, China
Search for more papers by this authorWanqing Wu
CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Shenzhen, China
Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
Search for more papers by this authorSummary
A decoupled visual model and a fractional PID controller are designed aiming at the problem of the view distortion in the fillet weld welding process. Firstly, the intersection point coordinates of two laser stripes are selected as the image characteristics, and the decoupled visual model is designed to the tracking control of the fillet weld seam so that the control value in two directions is decoupled, reducing the difficulty of control. In addition to this, the image may produce distortion in the dynamic tracking process, causing nonlinearity and coupling in two directions. To solve this problem, the fractional-order PID controller is designed so that the adjustment range and the control ability are improved better than the traditional PID controller. Some experiments verify that the desired performance can be achieved by using the proposed methods.
REFERENCES
- 1Zhu JL, Jiao XD, Jiang LP, et al. A new mechanical contact seam tracking sensor system. Welded Pipe Tube. 2007; 30(4): 54-55.
- 2Li Y, Li YF, Wang QL, Xu D, Tan M. Measurement and defect detection of the weld bead based on online vision inspection. IEEE Trans Instrum Meas. 2010; 59(7): 1841-1849.
- 3Zhang L, Ye Q, Yang W, Jiao J. Weld line detection and tracking via spatial-temporal cascaded hidden Markov models and cross structured light. IEEE Trans Instrum Meas. 2014; 63(4): 742-753.
- 4Chen H, Liu W, Huang L, Xing G, Wang M, Sun H. The decoupling visual feature extraction of dynamic three-dimensional V-type seam for gantry welding robot. Intern J Adv Manuf Technol. 2015; 80(9-12): 1741-1749.
- 5Fang Z, Xu D, Tan M. A vision-based self-tuning fuzzy controller for fillet weld seam tracking. IEEE/ASME Trans Mechatron. 2011; 16(3): 540-550.
- 6Gnucci M, Gospodarczyk M, Carnevale D, Tiberti M, Tomei P, Verrelli CM. A learning control algorithm for periodic robot synchronization: experimental results. Int J Adapt Control Signal Process. 2018; 32(5): 729-741.
- 7Fossen TI, Lekkas AM. Direct and indirect adaptive integral line-of-sight path-following controllers for marine craft exposed to ocean currents. Int J Adapt Control Signal Process. 2017; 31(4): 445-463.
- 8Li X, Li X, Khyam MO, Ge SS. Robust welding seam tracking and recognition. IEEE Sens J. 2017; 17(17): 5609-5617.
- 9Wang H, Yang B, Liu Y, Chen W, Liang X, Pfeifer R. Visual servoing of soft robot manipulator in constrained environments with an adaptive controller. IEEE/ASME Trans Mechatron. 2017; 22(1): 41-50.
- 10Ma WB, Zhang K, Liu JH, Rui YN. Based on Nash optimization decoupling control system for special shape pipe automatic welding machine. Adv Mater Res. 2012; 383: 2832-2837.
- 11Pdlubny I. Fractional-order systems and PIλDμ-controllers. IEEE Trans Autom Control. 1999; 44(1): 208-214.
- 12Chopade AS, Khubalkar SW, Junghare AS, Aware MV, Das S. Design and implementation of digital fractional order PID controller using optimal pole-zero approximation method for magnetic levitation system. IEEE/CAA J Autom Sin. 2016; 5(5): 977-989.
10.1109/JAS.2016.7510181 Google Scholar
- 13Zhong J, Li L. Tuning fractional-order PIλ Dμ controllers for a solid-Core magnetic bearing system. IEEE Trans Control Syst Technol. 2015; 23(4): 1648-1656.
- 14Taher SA, Fini MH, Aliabadi SF. Fractional order PID controller design for LFC in electric power systems using imperialist competitive algorithm. Ain Shams Eng J. 2014; 5(1): 121-135.
10.1016/j.asej.2013.07.006 Google Scholar
- 15Rabah K, Ladaci S, Lashab M. Bifurcation-based fractional-order PIλ Dμ controller design approach for nonlinear chaotic systems. Front Inf Technol Electron Eng. 2018; 19(2): 180-191.
- 16Belter D, Nowicki M, Skrzypczyński P. Modeling spatial uncertainty of point features in feature-based RGB-D SLAM. Mach Vis Appl. 2018; 29(5): 827-844.
- 17Zhang YH, Li JN. Intelligent vehicle control based on fractional order PID controller. Control Eng China. 2011; 18(3): 356-359.
- 18Sondhi S, Hote YV. Fractional order PID controller for load frequency control. Energy Convers Manag. 2014; 85(9): 343-353.
- 19Huang LL, Zhou XL, Xiang JH. Self-adjusting design on parameters of the fractional order PID controller. Syst Eng Electron. 2013; 35(5): 1064-1069.
- 20Wang ZB, Cao GY, Zeng QS, Zhu XJ. Fractional order PID controller and its digital implementation. J Shanghai Jiaotong Univ. 2004; 38(4): 517-520.
