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The stabilization and 3D visual simulation of the triple inverted pendulum based on CGA-PIDNN

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  • Intelligent and Information Systems
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Abstract

Aiming at the triple inverted pendulum which is a strong coupling, multivariable, high-order and unsteady system, a design method of the controller based on PID neural network (PIDNN) optimized by cloud genetic algorithm (CGA) is proposed, this method is called CGA-PIDNN. CGA can be applied to learn and train the PIDNN connection weights. CGA can overcome the defect of the slow convergence rate and premature convergence for genetic algorithm (GA). PIDNN is a simple and normative network which is easy to be realized and has a good dynamic performance. The CGA-PIDNN control system of triple inverted pendulum is verified with MATLAB simulation test. The comparison results with the control effect of PIDNN control system optimized by standard GA (GA-PIDNN) are presented first. Then in LabVIEW environment, by using the combination of virtual reality technology and MATLAB, the three-dimensional (3D) animation simulation model of the triple inverted pendulum CGA-PIDNN control system is built. The simulation results indicate that CGA-PIDNN control method is effective, whose control effects are superior to those by GA-PIDNN control, it is believed that CGA-PIDNN is effective and will become a promising candidate of control methods.

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Author information

Authors and Affiliations

  1. National Engineering Research Center for Equipment and Technology of Cold Strip Rolling, Yanshan University, Qinhuangdao, 066004, China

    Xiu-Ling Zhang

  2. Key Laboratory of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao, 066004, China

    Xiu-Ling Zhang, Hong-Min Fan, Jia-Yin Zang, Liang Zhao & Shuang Hao

Authors
  1. Xiu-Ling Zhang
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  2. Hong-Min Fan
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  3. Jia-Yin Zang
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  4. Liang Zhao
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  5. Shuang Hao
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Corresponding author

Correspondence to Xiu-Ling Zhang.

Additional information

Recommended by Associate Editor DaeEun Kim under the direction of Editor Euntai Kim.

This work was supported by the University innovation team of Hebei Province Leading Talent Cultivation Project (No. LJRC 013) and Hebei Province Natural Science Foundation of Steel joint Research Funds of China (Grants No. E2015203354).

Xiu-Ling Zhang received her BE degree from the Northeast Heavy Machinery Institute in 1990, an ME degree from the Northeast Heavy Machinery Institute in 1995. She obtained her Ph.D. degree from Yanshan University, China, in 2002. At present, she is a professor of the College of Electrical Engineering, Yanshan University, China. Her research interests include the modeling, control and pattern recognition of complex system based on artificial intelligence.

Hong-Min Fan received her BE degree from the Liren Institute of Yanshan University in 2012. At present, she is a master student of the College of Electrical Engineering, Yanshan University, China. Her research interests include the modeling, control and pattern recognition of complex system based on artificial intelligence.

Jia-Yin Zang received her BE degree from the Liren Institute of Yanshan University in 2012. At present, she is a master student of the College of Electrical Engineering, Yanshan University, China. Her research interests include the modeling, control and pattern recognition of complex system based on artificial intelligence.

Liang Zhao received his BE degree from the Liren Institute of Yanshan University in 2012. At present, he is a master student of the College of Electrical Engineering, Yanshan University, China. His research interests include the modeling, control and pattern recognition of complex system based on artificial intelligence.

Shuang Hao received her BE degree from Yanshan University in 2013. At present, she is a master student of the College of Electrical Engineering, Yanshan University, China. Her research interests include the modeling, control and pattern recognition of complex system based on artificial intelligence.

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Zhang, XL., Fan, HM., Zang, JY. et al. The stabilization and 3D visual simulation of the triple inverted pendulum based on CGA-PIDNN. Int. J. Control Autom. Syst. 13, 1010–1019 (2015). https://doi.org/10.1007/s12555-014-0040-5

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  • DOI: https://doi.org/10.1007/s12555-014-0040-5

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