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Hybrid trigonometric compound function neural networks for tracking control of a nonholonomic mobile robot

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Abstract

The purpose of this paper is to propose a hybrid trigonometric compound function neural network (NN) to improve the NN-based tracking control performance of a nonholonomic mobile robot with nonlinear disturbances. In the mobile robot control system, two NN controllers embedded in the closed-loop control system have the simple continuous learning and rapid convergence capability without the dynamics information of the mobile robot to realize the tracking control of the mobile robot. The neuron functions of the hidden layer in the three-layer feedforward network structure consist of the compound cosine function and the compound sine function combining a cosine or a sine function with a unipolar sigmoid function. The main advantages of this NN-based mobile robot control system are better real-time control capability and control accuracy by use of the proposed NN controllers for a nonholonomic mobile robot with nonlinear disturbances. Through simulation experiments applied to the nonholonomic mobile robot with the nonlinear disturbances of dynamics uncertainty and external disturbances, the simulation results show that the proposed NN control system of a nonholonomic mobile robot has better real-time control capability and control accuracy than the compound cosine function NN control system of a nonholonomic mobile robot and then verify the effectiveness of the proposed hybrid trigonometric compound function NN controller for improving the tracking control performance of a nonholonomic mobile robot with nonlinear disturbances.

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References

  1. Canudasde Wit C, Khennouf H, Samson C, Sordalen OJ (1993) Nonlinear control design for mobile robots. In: Zheng YF (ed) Recent Trends in Mobile Robots, Vol. 11. World Scientific, Singapore, pp 121–156

    Google Scholar 

  2. Ye J (2008) Tracking control for nonholonomic mobiles: Integrating the analog neural network into the backstepping technique. Neurocomputing 71:3373–3378

    Article  Google Scholar 

  3. Yang JM, Kim JH (1999) Sliding mode control for trajectory tracking of nonholonomic wheeled mobile robots. IEEE Trans. Robotics and Automation 15:578–587

  4. Fukao T, Nakagawa H, Adachi N (2000) Adaptive tracking control of a nonholonomic mobile robot. IEEE Trans. Robotics and Automation 16:609–615

    Article  Google Scholar 

  5. Pourboghrat F, Karlsson MP (2000) Adaptive control of dynamic mobile robots with nonholonomic constraints. Computer Electrical Engineering 28:241–253

    Article  Google Scholar 

  6. Oriolo G, Luca AD, Vendittelli M (2002) WMR control via dynamic feedback linearization: design, implementation and experimental validation. IEEE Trans. Control System Technology 10:835–852

  7. Das T, Kar IN, Chaudhury S (2006) Simple neuraon-based adaptive controller for a nonholonomic mobile robot including actuator dynamics. Neurocomputing 69:2140–2151

    Article  Google Scholar 

  8. Ye J (2008) Adaptive control of nonlinear PID-based analog neural network for a nonholonomic mobile robot. Neurocomputing 71:1561–1565

    Article  Google Scholar 

  9. Hu T, Yang S (2001) A novel tracking control method for a wheeled mobile robot. In: Proc. of 2nd Workshop on Computational Kinematics, Seoul, Korea, pp. 104–116.

  10. Fierro R, Lewis FL (1998) Control of a nonholonomic mobile robot using neural networks. IEEE Trans. Neural Networks 9(4):389–400

    Article  Google Scholar 

  11. Hu T, Yang SX, Wang F, Mittal GS (2002) A Neural Network Controller for a Nonholonomic Mobile Robot with Unknown Robot Parameters. In: Proceedings of IEEE International Conference on Robotics & Automation, Washington, DC, pp. 3540–3545.

  12. Park BS, Yoo SJ, Park JB, Choi YH (2009) Adaptive neural sliding mode control of nonholonomic wheeled mobile robots with model uncertainty. IEEE Transactions on Control Systems Technology 17(1):207–214

    Article  Google Scholar 

  13. Mohareri O, Dhaouadi R, Rad AB (2012) Indirect adaptive tracking control of a nonholonomic mobile robot via neural networks. Neurocomputing 88:54–66

    Article  Google Scholar 

  14. Li Y, Zhu L, Sun M (2013) Adaptive neural-network control of mobile robot formations including actuator dynamics. Applied Mechanics and Materials 303–306:1768–1773

    Article  Google Scholar 

  15. Ye J (2013) Tracking control of a nonholonomic mobile robot using compound cosine function neural networks. Intelligent Service Robotics 6(4):191–198

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Electrical and Information Engineering, Shaoxing University, 508 Huancheng West Road, Shaoxing , 312000, Zhejiang Province, People’s Republic of China

    Jun Ye

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  1. Jun Ye
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Correspondence to Jun Ye.

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Ye, J. Hybrid trigonometric compound function neural networks for tracking control of a nonholonomic mobile robot. Intel Serv Robotics 7, 235–244 (2014). https://doi.org/10.1007/s11370-014-0155-9

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  • DOI: https://doi.org/10.1007/s11370-014-0155-9

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