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SCIENCE CHINA Information Sciences, Volume 64 , Issue 7 : 172205(2021) https://doi.org/10.1007/s11432-020-3043-y

Novel sliding-mode disturbance observer-based tracking control with applications to robot manipulators

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  • ReceivedMar 26, 2020
  • AcceptedAug 1, 2020
  • PublishedMay 18, 2021

Abstract


Acknowledgment

This work was supported in part by National Key Research and Development Project (Grant No. 2019YFB1- 312503), National Natural Science Foundation of China (Grant Nos. U1913209, 61720106012, 61873268, 61633016), Beijing Natural Science Foundation (Grant Nos. JQ19020, L182060), and Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDB32040000).


References

[1] Wang H, Cheah C C, Ren W. Passive Separation Approach to Adaptive Visual Tracking for Robotic Systems. IEEE Trans Contr Syst Technol, 2018, 26: 2232-2241 CrossRef Google Scholar

[2] Cheng L, Hou Z G, Tan M. Adaptive neural network tracking control for manipulators with uncertain kinematics, dynamics and actuator model. Automatica, 2009, 45: 2312-2318 CrossRef Google Scholar

[3] Sun T, Peng L, Cheng L. Composite Learning Enhanced Robot Impedance Control. IEEE Trans Neural Netw Learning Syst, 2020, 31: 1052-1059 CrossRef Google Scholar

[4] Sun T, Cheng L, Peng L. Learning impedance control of robots with enhanced transient and steady-state control performances. Sci China Inf Sci, 2020, 63: 192205 CrossRef Google Scholar

[5] Pan Y, Yu H. Composite learning robot control with guaranteed parameter convergence. Automatica, 2018, 89: 398-406 CrossRef Google Scholar

[6] Ding S, Li S. Second-order sliding mode controller design subject to mismatched term. Automatica, 2017, 77: 388-392 CrossRef Google Scholar

[7] Shao S, Chen M. Sliding-mode-disturbance-observer-based adaptive neural control of uncertain discrete-time systems. Sci China Inf Sci, 2020, 63: 149204 CrossRef Google Scholar

[8] Pan Y, Yu H, Er M J. Adaptive Neural PD Control With Semiglobal Asymptotic Stabilization Guarantee. IEEE Trans Neural Netw Learning Syst, 2014, 25: 2264-2274 CrossRef Google Scholar

[9] Sun T, Cheng L, Wang W. Semiglobal exponential control of Euler-Lagrange systems using a sliding-mode disturbance observer. Automatica, 2020, 112: 108677 CrossRef Google Scholar

[10] Ding S, Chen W H, Mei K. Disturbance Observer Design for Nonlinear Systems Represented by Input-Output Models. IEEE Trans Ind Electron, 2020, 67: 1222-1232 CrossRef Google Scholar

[11] Wen-Hua Chen , Ballance D J, Gawthrop P J. A nonlinear disturbance observer for robotic manipulators. IEEE Trans Ind Electron, 2000, 47: 932-938 CrossRef Google Scholar

[12] Yang Y, Du J, Liu H. A Trajectory Tracking Robust Controller of Surface Vessels With Disturbance Uncertainties. IEEE Trans Contr Syst Technol, 2014, 22: 1511-1518 CrossRef Google Scholar

[13] Back J, Shim H. Adding robustness to nominal output-feedback controllers for uncertain nonlinear systems: A nonlinear version of disturbance observer. Automatica, 2008, 44: 2528-2537 CrossRef Google Scholar

[14] Yan W, Pang C K, Du C. Disturbance observer-based multirate control for rejecting periodic disturbances to the Nyquist frequency and beyond. Automatica, 2017, 82: 49-58 CrossRef Google Scholar

[15] Huang J, Ri S, Fukuda T. A Disturbance Observer Based Sliding Mode Control for a Class of Underactuated Robotic System With Mismatched Uncertainties. IEEE Trans Automat Contr, 2019, 64: 2480-2487 CrossRef Google Scholar

[16] Chen W H. Disturbance Observer Based Control for Nonlinear Systems. IEEE/ASME Trans Mechatron, 2004, 9: 706-710 CrossRef Google Scholar

[17] Wang X, Li S, Yu X. Distributed Active Anti-Disturbance Consensus for Leader-Follower Higher-Order Multi-Agent Systems With Mismatched Disturbances. IEEE Trans Automat Contr, 2017, 62: 5795-5801 CrossRef Google Scholar

[18] Yang J, Zheng W X, Li S. Design of a Prediction-Accuracy-Enhanced Continuous-Time MPC for Disturbed Systems via a Disturbance Observer. IEEE Trans Ind Electron, 2015, 62: 5807-5816 CrossRef Google Scholar

[19] Young K D, Utkin V I, Ozguner U. A control engineer's guide to sliding mode control. IEEE Trans Contr Syst Technol, 1999, 7: 328-342 CrossRef Google Scholar

[20] Xinkai Chen , Komada S, Fukuda T. Design of a nonlinear disturbance observer. IEEE Trans Ind Electron, 2000, 47: 429-437 CrossRef Google Scholar

[21] Lu Y S, Cheng C M, Cheng C H. Non-overshooting PI control of variable-speed motor drives with sliding perturbation observers. Mechatronics, 2005, 15: 1143-1158 CrossRef Google Scholar

[22] Wang B, Dong Z, Yu Y. Static-Errorless Deadbeat Predictive Current Control Using Second-Order Sliding-Mode Disturbance Observer for Induction Machine Drives. IEEE Trans Power Electron, 2018, 33: 2395-2403 CrossRef ADS Google Scholar

[23] Yu-Sheng Lu . Sliding-Mode Disturbance Observer With Switching-Gain Adaptation and Its Application to Optical Disk Drives. IEEE Trans Ind Electron, 2009, 56: 3743-3750 CrossRef Google Scholar

[24] Chen M, Wu Q X, Cui R X. Terminal sliding mode tracking control for a class of SISO uncertain nonlinear systems. ISA Trans, 2013, 52: 198-206 CrossRef Google Scholar

[25] Chen M, Shi P, Lim C C. Robust Constrained Control for MIMO Nonlinear Systems Based on Disturbance Observer. IEEE Trans Automat Contr, 2015, 60: 3281-3286 CrossRef Google Scholar

[26] Zhu Y, Qiao J, Guo L. Adaptive Sliding Mode Disturbance Observer-Based Composite Control With Prescribed Performance of Space Manipulators for Target Capturing. IEEE Trans Ind Electron, 2019, 66: 1973-1983 CrossRef Google Scholar

[27] Makkar C, Hu G, Sawyer W G. Lyapunov-Based Tracking Control in the Presence of Uncertain Nonlinear Parameterizable Friction. IEEE Trans Automat Contr, 2007, 52: 1988-1994 CrossRef Google Scholar

[28] Ge S S, Hang C C, Lee T H, et al. Stable Adaptive Neural Network Control. New York: Springer Science & Business Media, 2013. Google Scholar

  • Figure 1

    (Color online) Tracking performances of one-link manipulator by proposed SMDOB-based controller (solid line) and SMDOB-based controller (dashed line) in [26].

  • Figure 2

    (Color online) Tracking performances of one-link manipulator without SMDOB-based compensation.

  • Figure 3

    (Color online) Tracking performances of two-link manipulator by proposed SMDOB-based controller (solid line) and SMDOB-based controller (dashed line) in [26].

  • Figure 4

    (Color online) Tracking performances of two-link manipulator without SMDOB.

  • Table 1  

    Table 1Parameters for the SMDOB in [26]

    Selection Parameters
    SMDOB $c_0=10,~l_0=0.001,~\Theta_0=[0.2,0.4]^{\rm~T},~\bar{\gamma}=1,~\lambda_2=5,~\lambda_3=0.5,~\delta_0=0.1,~l_1=0.001$
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