Adaptive neural torsional vibration suppression of the rolling mill main drive system subject to state and input constraints with sensor errors
Torsional vibration often occurs in rolling mill drive system, which seriously affects the product quality accuracy and the service life of transmission equipment. This paper studies the adaptive neural torsional vibration suppression control problem for the rolling mill main drive system with state...
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| Published in | Journal of the Franklin Institute Vol. 357; no. 17; pp. 12886 - 12903 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elmsford
Elsevier Ltd
01.11.2020
Elsevier Science Ltd |
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| Abstract | Torsional vibration often occurs in rolling mill drive system, which seriously affects the product quality accuracy and the service life of transmission equipment. This paper studies the adaptive neural torsional vibration suppression control problem for the rolling mill main drive system with state and input constraints subject to unknown measurement sensitivities. Firstly, considering the nonlinear friction between the work roll and strip, nonlinear damping at the motor and the load and unknown uncertainties on system parameters, a new torsional vibration model of the main drive system of rolling mill is established. Then, by selecting the proper asymmetric tangent barrier Lyapunov function, the motor torque control law is proposed based on backstepping algorithm. The adaptive neural networks are introduced to solve the unknown uncertainties and the unknown measurement errors and a continuous differentiable Gaussian error function is employed to deal with actuator saturation. It is strictly proved that the designed main drive torsional vibration system is stable and the performances of the transformed states are preserved. Finally, simulation shows the validity and the advantages of the proposed algorithm. |
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| AbstractList | Torsional vibration often occurs in rolling mill drive system, which seriously affects the product quality accuracy and the service life of transmission equipment. This paper studies the adaptive neural torsional vibration suppression control problem for the rolling mill main drive system with state and input constraints subject to unknown measurement sensitivities. Firstly, considering the nonlinear friction between the work roll and strip, nonlinear damping at the motor and the load and unknown uncertainties on system parameters, a new torsional vibration model of the main drive system of rolling mill is established. Then, by selecting the proper asymmetric tangent barrier Lyapunov function, the motor torque control law is proposed based on backstepping algorithm. The adaptive neural networks are introduced to solve the unknown uncertainties and the unknown measurement errors and a continuous differentiable Gaussian error function is employed to deal with actuator saturation. It is strictly proved that the designed main drive torsional vibration system is stable and the performances of the transformed states are preserved. Finally, simulation shows the validity and the advantages of the proposed algorithm. |
| Author | Qian, Cheng Zhang, Liuliu Hua, Changchun Bai, Zhenhua |
| Author_xml | – sequence: 1 givenname: Cheng surname: Qian fullname: Qian, Cheng organization: Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China – sequence: 2 givenname: Changchun surname: Hua fullname: Hua, Changchun email: cch@ysu.edu.cn organization: Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China – sequence: 3 givenname: Liuliu surname: Zhang fullname: Zhang, Liuliu organization: Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China – sequence: 4 givenname: Zhenhua surname: Bai fullname: Bai, Zhenhua organization: National Engineering Research Center for Equipment and Technology of Cold Strip Rolling, Yanshan University, Qinhuangdao 066004, China |
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| Snippet | Torsional vibration often occurs in rolling mill drive system, which seriously affects the product quality accuracy and the service life of transmission... |
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| SubjectTerms | Actuators Adaptive algorithms Adaptive control Algorithms Computer simulation Continuity (mathematics) Control equipment Control theory Damping Error functions Liapunov functions Motors Neural networks Nonlinear systems Parameter uncertainty Rolling mills Service life Torsional vibration Vibration Vibration control |
| Title | Adaptive neural torsional vibration suppression of the rolling mill main drive system subject to state and input constraints with sensor errors |
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