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Novel Adaptive Sliding Mode Control with Nonlinear Disturbance Observer for SMT Assembly Machine

Author

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  • Rongrong Qian
  • Minzhou Luo
  • Yao Zhao
  • Jianghai Zhao

Abstract

This paper presents a novel adaptive sliding mode control based on nonlinear sliding surface with disturbance observer (ANSMC-DOB) for precision trajectory tracking control of a surface mount technology (SMT) assembly machine. A two-degree-of-freedom model with time-varying parameter uncertainties and disturbances is built to describe the first axial mode of the pick-place actuation axis of the machine. According to the principle of variable damping ratio coefficient which makes the system have a nonovershoot transient response and a short settling time in the second-order system, the nonlinear sliding surface is designed for the sliding mode control (SMC). Since the upper bound value of the disturbances is unknown, the adaptive gain estimation is applied to replace the switching gain in the SMC. In order to settle the problem of SMC unrobust to the mismatched parameter uncertainties and disturbances, the nonlinear disturbance observer is introduced to estimate the mismatched disturbances and form the novel controller of ANSMC-DOB. The stability of sliding surfaces and control laws are verified by the Lyapunov functions. The simulation research and comparative experiments are conducted to verify the improvement of positioning accuracy and robustness by the proposed ANSMC-DOB in the SMT assembly machine.

Suggested Citation

  • Rongrong Qian & Minzhou Luo & Yao Zhao & Jianghai Zhao, 2016. "Novel Adaptive Sliding Mode Control with Nonlinear Disturbance Observer for SMT Assembly Machine," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-14, March.
  • Handle: RePEc:hin:jnlmpe:9602483
    DOI: 10.1155/2016/9602483
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    Cited by:

    1. Jiunn-Shiou Fang & Jason Sheng-Hong Tsai & Jun-Juh Yan & Chang-He Tzou & Shu-Mei Guo, 2019. "Design of Robust Trackers and Unknown Nonlinear Perturbation Estimators for a Class of Nonlinear Systems: HTRDNA Algorithm for Tracker Optimization," Mathematics, MDPI, vol. 7(12), pages 1-20, November.

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