Abstract
In practical applications, noncircular turning tool feed system repeat the same control tasks over a finite time interval. But it does not have the ability to improve the tool position error from past repeated operations. This paper will use Partial Form Dynamic Linearization based Model-Free Adaptive Iterative Learning Control (PFDL-MFAILC) algorithm in noncircular turning tool feed. PFDL-MFAILC is a data-driven iterative learning control algorithms. The design of noncircular turning tool feed controller is just rely on input and output data. Simulation of PFDL-MFAILC algorithm show that the noncircular turning tool feed position error is improved as the number of repetitions increases. By contrast with PID control algorithm, position tracking accuracy of PFDL-MFAILC algorithm is significantly better than traditional PID control algorithm. After 60 iterations, the steady-state error of PFDL-MFAILC algorithm is much lower than the steady-state error of the PID algorithm. PFDL-MFAILC algorithm achieve the goal that improving position precision of noncircular turning tool feed and making noncircular turning tool feed have self-learning ability from past repeated operations.
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References
Zheng, D.: Research on derived CNC lathe system for large-sized non-circular middle-convex and varying oval piston. China Agricultural University, Beijing (2014)
Jin, S.-t.: On model free learning adaptive control and applications. Beijing Jiaotong University, Beijing (2008)
Zheng, J.: Model-free adaptive control with noise attenuation based on modified wavelet threshold denoising methods. Beijing Jiaotong University, Beijing (2016)
Guo, W.-p.: Application research of model free adaptive predictive control in grinding process. Hebei University of Technology, Hebei (2015)
Hou, Z.S., Jin, S.T.: Model Free Adaptive Control Theory and Applications. Science Press, Beijing (2013)
Cao, R.M.: Motion Control System Design and Implement Based on Data-Driven. National Defense Industry Press, Beijing (2012)
Yanhui, W.: Study on Model Free Adaptive Control for Nonlinear Systems. Yanshan University, Hebei (2015)
Cao, R.M., Zhou, H.X., Hou, Z.S.: Synthesis iterative learning control for noncircular turning tool feed. Electric Drive 09, 42–46 (2012)
Lei, W.: Design and Experimental Study of Poly Magnet Linear Voice Coil Motor. China Agricultural University, Beijing (2014)
Hou, M.: Research on high-precision high-speed high-frequency linear motor control method. China Agricultural University, Beijing (2016)
Zhang, Y.: The research of the error compensation and the technology of feedforward control for the permanent magnet synchronous linear motor. North University of China (2016)
Liu, Y.: Data-driven terminal iterative learning control with variant uncertainties. Qingdao University of Science and Technology (2016)
Acknowledgment
This work is supported by Beijing Natural Science Foundation of China (4142017), International Cooperation Project of National Natural Science Foundation of China (61120106009) and The National Natural Science Foundation of China (61261160497).
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© 2016 Springer Science+Business Media Singapore
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Yunjie, Z., Rongmin, C., Huixing, Z. (2016). Model-Free Adaptive Iterative Learning Control Based on Data-Driven for Noncircular Turning Tool Feed System. In: Zhang, L., Song, X., Wu, Y. (eds) Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems. AsiaSim SCS AutumnSim 2016 2016. Communications in Computer and Information Science, vol 644. Springer, Singapore. https://doi.org/10.1007/978-981-10-2666-9_1
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DOI: https://doi.org/10.1007/978-981-10-2666-9_1
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