A Robust Adaptive Control using Fuzzy Neural Network for Robot Manipulators with Dead-Zone

  • D. Ha Vu hunan university https://orcid.org/0000-0002-0303-5781
  • Shoudao Huang College of Electrical and Information Engineering Hunan University, Changsha, China
  • T. Diep Tran College of Electrical and Information Engineering Hunan University, Changsha, China
  • T. Yen Vu Faculty of Electrical Engineering Saodo University, Chilinh, Vietnam
  • V. Cuong Pham Faculty of Electrical Engineering Hanoi University of Industry, Hanoi, Vietnam

Abstract

In this paper, a robust-adaptive-fuzzy-neural-network controller (RAFNNs) bases on dead zone compensator for industrial robot manipulators (RM) is proposed to dead the unknown model and external disturbance. Here, the unknown dynamics of the robot system is deal by using fuzzy neural network to approximate the unknown dynamics. The online training laws and estimation of the dead-zone are determined by Lyapunov stability theory and the approximation theory. In this proposal, the robust sliding-mode-control (SMC) is constructed to optimize parameter vectors, solve the approximation error and higher order terms. Therefore, the stability, robustness, and desired tracking performance of RAFNNs for RM are guaranteed. The simulations and experiments performed on three-link RM are provided in comparison with neural-network (NNs) and proportional-integral-derivative (PID) to demonstrate the robustness and effectiveness of the RAFNNs.

Author Biography

D. Ha Vu, hunan university
College of Electrical and Information Engineering, Hunan University, Hunan, P.R. China

References

[1] Baigzadehnoe, B.; Rahmani, Z.; Khosravi, A.; Rezaie, B. (2017). On position/force tracking control problem of cooperative robot manipulators using adaptive fuzzy backstepping approach, ISA Transactions, 70, 432–446, 2017.

[2] Bragina, A. A.; Shcherbakov, V. P.; Shiryaev, V. I. (2018). Synthesis of Adaptive Control of Robotic Manipulator by the Method of Lyapunov Functions, IFAC-PapersOnLine, 51, 298–303, 2018.

[3] Chen, C. (2011). Robust Self-Organizing Neural-Fuzzy Control With Uncertainty Observer for MIMO Nonlinear Systems, IEEE Transactions on Fuzzy Systems, 19, 694–706, 2011.

[4] Chung, C.; Chang, Y. (2013). Backstepping control of multi-input non-linear systems, IET Control Theory & Applications, 7, 1773–1779, 2013.

[5] He, J.; Luo, M.; Zhang, Q.; Zhao, J.; Xu, L. (2016). Adaptive Fuzzy Sliding Mode Controller with Nonlinear Observer for Redundant Manipulators Handling Varying External Force, Journal of Bionic Engineering, 13, 600–611, 2016.

[6] He, W.; Dong, Y.; Sun, C. (2015). Adaptive neural network control of unknown nonlinear affine systems with input deadzone and output constraint, ISA Transactions, 58, 96–104, 2015.

[7] Ik Han, S.; Lee, J. (2016). Finite-time sliding surface constrained control for a robot manipulator with an unknown deadzone and disturbance, ISA Transactions, 65, 307–318, 2016.

[8] Ishii, C.; Shen, T.; Tamura, K. (1997). Robust model-following control for a robot manipulator, IEE ProcControl Theory Appl, 144(1), 53–60, 1997.

[9] Jing, C.; Xu, H.; Niu, X. (2019). Adaptive sliding mode disturbance rejection control with prescribed performance for robotic manipulators, ISA transactions, 91, 41–51, 2019.

[10] Jing, Z.; Changyun, W.; Ying, Z. (2004). Adaptive backstepping control of a class of uncertain nonlinear systems with unknown backlash-like hysteresis, IEEE Transactions on Automatic Control, 49, 1751–1759, 2004.

[11] Karamali Ravandi, A.; Khanmirza, E.; Daneshjou, K. (2018). Hybrid force/position control of robotic arms manipulating in uncertain environments based on adaptive fuzzy sliding mode control, Applied Soft Computing, 70, 864–874, 2018.

[12] Khorashadizadeh S.; Sadeghijaleh, M. (2018). Adaptive fuzzy tracking control of robot manipulators actuated by permanent magnet synchronous motors, Computers & Electrical Engineering, 72, 100–111, 2018.

[13] Krstic, M.; Kanellakopoulos, I.; Kokotovic, P. V. (1995). Nonlinear Adaptive Control Design, New York, NY, USA: Wiley, 1995.

[14] Lewis, F. L.; Tim, K.; Wang, L. Z.; Li, Z. X. (1999). Deadzone compensation in motion control systems using adaptive fuzzy control system, IEEE Trans. Control. Syst. Technol, 7, 731–742, 1999.

[15] Peng, J.; Dubay, R. (2019). Adaptive fuzzy backstepping control for a class of uncertain nonlinear strict-feedback systems based on dynamic surface control approach, Expert Systems with Applications, 120, 239–252, 2019.

[16] Precup, R.; Tomescu, M.; Preitl, S. (2009). Fuzzy logic control system stability analysis based on Lyapunov’s direct method, International journal of computer, communication & control, IV, 415–426, 2009.

[17] Rossomando, F. G.; Soria, C.; Carelli, R. (2014). Sliding mode control for trajectory tracking of a non- holonomic mobile robot using adaptive neural networks, Control Engineering and Applied Informatics, 16, 12–21, 2014.

[18] Sabahi, F. (2018). Introducing validity into self-organizing fuzzy neural network applied to impedance force control, Fuzzy Sets and Systems, 337, 113–127, 2018.

[19] Selmic R. R.; Lewis, F. L. (2000). Deadzone compensation in motion control systems using neural networks, IEEE Transactions on Automatic Control, 45, 602–613, 2000.

[20] Slotine J. J. E.; Li, W. (1991). Applied Nonlinear Control, Prentice-Hall, Hoboken, NJ, 1991.

[21] Tsai, C.-H.; Chuang, H.-T. (2004). Deadzone compensation based on constrained RBF neural network, Journal of the Franklin Institute, 341, 361–374, 2004.

[22] Vrkalovic, S.; Lunca, E.; Borlea, I. (2018). Model-free sliding mode and fuzzy controllers for reverse osmosis desalination plants, International journal of Artificial intelligence, 16, 208–222, 2018.

[23] Wai, R.; Muthusamy, R. (2013). Fuzzy-Neural-Network Inherited Sliding-Mode Control for Robot Manipulator Including Actuator Dynamics, IEEE Transactions on Neural Networks and Learning Systems, 24, 274–287, 2013.

[24] Wai, R.; Muthusamy, R. (2014). Design of Fuzzy-Neural-Network-Inherited Backstepping Control for Robot Manipulator Including Actuator Dynamics, IEEE Transactions on Fuzzy Systems, 22, 709–722, 2014.

[25] Wen, C.; Zhou, J.; Liu, Z.; Su, H. (2011). Robust Adaptive Control of Uncertain Nonlinear Systems in the Presence of Input Saturation and External Disturbance, IEEE Transactions on Automatic Control, 56, 1672–1678, 2011.

[26] Wu, Y.; Huang, R.; Li, X.; Liu, S. (2019). Adaptive neural network control of uncertain robotic manipulators with external disturbance and time-varying output constraints, Neurocomputing, 323, 108–116, 2019.

[27] Ying, H. (2005). Structure and stability analysis of general mamdani fuzzy dynamic Models, International journal of intelligent systems, 20, 103–125, 2005.

[28] Ying, Z.; Changyun, W.; Yeng Chai, S. (2000). Adaptive backstepping control design for systems with unknown high-frequency gain, IEEE Transactions on Automatic Control, 45, 2350–2354, 2000.

[29] Zhou, D.; Shi, M.; Chao, F.; Lin, C. M.; Yang, L.; Shang, C.; Zhou, C. (2018). Use of human gestures for controlling a mobile robot via adaptive CMAC network and fuzzy logic controller, Neurocomputing, 282, 218–231, 2018.
Published
2019-11-17
How to Cite
VU, D. Ha et al. A Robust Adaptive Control using Fuzzy Neural Network for Robot Manipulators with Dead-Zone. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 14, n. 5, p. 692-710, nov. 2019. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/3621>. Date accessed: 04 july 2020. doi: https://doi.org/10.15837/ijccc.2019.5.3621.

Keywords

adaptive control, fuzzy neural networks, robot manipulators, unknown dead-zone