Skip to main content
SpringerLink
Log in

Trajectory tracking control of wheeled mobile manipulator based on fuzzy neural network and extended Kalman filtering

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

For robot trajectory tracking control, it is necessary to model inverse dynamics system sufficiently well to allow high-performance control. However, for complex robots such as wheeled mobile manipulators (WMMs), it is often difficult to model the dynamics system owing to system uncertainties, nonlinearity, and coupling. In this paper, we propose an effective tracking control method based on fuzzy neural network (FNN) and extended Kalman filter (EKF) to achieve WMM followed reference trajectory efficiently. The FNN is trained to generate a feedforward torque. In order to increase the computational efficiency and precision of the training algorithm, the EKF is used to sequentially update both the output weights and centers of the FNN. The effectiveness of the proposed control algorithm is confirmed through system experiments.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Atawnih A, Papageorgioua D (2016) Kinematic control of redundant robots with guaranteed joint limit avoidance. Robot Auton Syst 79:122–131

    Article  Google Scholar 

  2. Li L, Gruver WA (2001) Kinematic control of redundant robots and the motion optimizability measure. IEEE Trans Syst Man Cybern B Cybern 31(1):155–160

    Article  Google Scholar 

  3. Lin H, Chen C (2011) A hybrid control policy of robot arm motion for assistive robots. IEEE Int Proc Inf Autom 33(7):163–168

    Google Scholar 

  4. Masoud S, Masoud A (2000) Constrained motion control using vector potential fields. IEEE Trans Syst Man Cybern A 30(2):251–272

    Article  Google Scholar 

  5. Falco P, Natale C (2011) On the stability of closed-loop inverse kinematics algorithms for redundant robots. IEEE Trans Robot 27(4):780–784

    Article  Google Scholar 

  6. Bloch AM, Reyhanoglu M (1992) Control and stabilization of nonholonomic dynamic systems. IEEE Trans Autom Control 37(11):1746–1757

    Article  MathSciNet  MATH  Google Scholar 

  7. Samson C (1995) Control of chained systems: application to path following and time-varying point-stabilization of mobile robots. IEEE Trans Autom Control 40(1):64–77

    Article  MathSciNet  MATH  Google Scholar 

  8. Sordalen OJ, Egeland O (1995) Exponential stabilization of nonholonomic chained systems. IEEE Trans Autom Control 40(1):35–49

    Article  MathSciNet  MATH  Google Scholar 

  9. Wang ZP, Ge SS, Lee TH (2004) Robust adaptive neural network control of uncertain nonholonomic systems with strong nonlinear drifts. IEEE Trans Syst Man Cybern B Cybern 34(5):2048–2059

    Article  Google Scholar 

  10. Dong WJ, Huo W, Tso SK, Xu WL (2000) Tracking control of uncertain dynamic nonholonomic system and its application to wheeled mobile robots. IEEE Trans Robot Autom 16(6):870–874

    Article  Google Scholar 

  11. Dong WJ, Xu WL (2001) Adaptive tracking control of uncertain nonholonomic dynamic system. IEEE Trans Autom Control 46(3):450–454

    Article  MathSciNet  MATH  Google Scholar 

  12. Oya M, Su CY, Katoh R (2003) Robust adaptive motion/force tracking control of uncertain nonholonomic mechanical systems. IEEE Trans Robot Autom 19(1):175–181

    Article  Google Scholar 

  13. Shojaei K, Shahri AM (2012) Adaptive robust time varying control of uncertain nonholonomic robotic systems. IET Control Theory Appl 6(1):90–102

    Article  MathSciNet  Google Scholar 

  14. Li Z, Ge SS, Adams M, Wijesoma WS (2008) Robust adaptive control of uncertain force/motion constrained nonholonomic mobile manipulators. Automatica 44(3):776–784

    Article  MathSciNet  Google Scholar 

  15. Li Z, Yang YP, Li JX (2010) Adaptive motion/force control of mobile under-actuated manipulators with dynamics uncertainties by dynamic coupling and output feedback. IEEE Trans Control Syst Technol 5(18):1068–1079

    Article  Google Scholar 

  16. Li Z, Ge SS, Adams M, Wijesoma WS (2008) Adaptive robust output-feedback motion/force control of electrically driven nonholonomic mobile manipulators. IEEE Trans Control Syst Technol 16(6):1308–1315

    Article  Google Scholar 

  17. Hua CC, Yang Y, Guan X (2013) Neural network-based adaptive position tracking control for bilateral teleoperation under constant time delay. Neurocomputing 113:204–212

    Article  Google Scholar 

  18. Lin CM, Hsu CF (2005) Recurrent neural network based adaptive backstepping control for induction servomotors. IEEE Trans Ind Electron 52(6):1677–1684

    Article  Google Scholar 

  19. Lin FJ, Wai RJ (2003) Robust recurrent fuzzy neural network control for linear synchronous motor drive system. Neurocomputing 50:365–390

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

  21. Fierro R, Lewis FL (1998) Control of a nonholonomic mobile robot using neural networks. IEEE Trans Neural Netw 9(4):589–600

    Article  Google Scholar 

  22. Xu D, Zhao D, Yi J, Tan X (2009) Trajectory tracking control of omnidirectional wheeled mobile manipulators: robust neural network-based sliding mode approach. IEEE Trans Syst Man Cybern B Cybern 39(3):788–799

    Article  Google Scholar 

  23. de Sousa C, Hemerly EM Jr., Galvao RKH (2002) Adaptive control for mobile robot using wavelet networks. IEEE Trans Syst Man Cybern B Cybern 32(4):493–504

    Article  Google Scholar 

  24. Park BS, Yoo SJ, Choi YH (2009) Adaptive neural sliding mode control of nonholonomic wheeled mobile robots with model uncertainty. IEEE Trans Control Syst Technol 17(1):207–214

    Article  Google Scholar 

  25. Broomhead D, Lowe D (1988) Multivariable functional interpolation and adaptive networks. Complex Syst 2:321–355

    MathSciNet  MATH  Google Scholar 

  26. Bezdek J, Keller J, Krishnapuram R, Kuncheva L, Pal H (1999) Will the real Iris data please stand up? IEEE Trans Fuzzy Syst 7:368–369

    Article  Google Scholar 

  27. Moody J, Darken C (1989) Fast learning in networks of locally-tuned processing units. Neural Comput 1:289–303

    Article  Google Scholar 

  28. Chen S, Cowan C, Grant P (1991) Orthogonal least squares learning algorithm for radial basis function networks. IEEE Trans Neural Netw 2:302–309

    Article  Google Scholar 

  29. Chen S, Wu Y, Luk B (1999) Combined genetic algorithm optimization and regularized orthogonal least squares learning for radial basis function networks. IEEE Trans Neural Netw 10:1239–1243

    Article  Google Scholar 

  30. Duro R, Reyes J (1999) Discrete-time backpropagation for training synaptic delay-based artificial neural networks. IEEE Trans Neural Netw 10:779–789

    Article  Google Scholar 

  31. Shah S, Palmieri F, Datum M (1992) Optimal filtering algorithms for fast learning in feedforward neural networks. Neural Netw 5:779–787

    Article  Google Scholar 

  32. Sum J, Leung C, Young G, Kan W (1999) On the Kalman Filtering method in neural network training and pruning. IEEE Trans Neural Netw 10:161–166

    Article  Google Scholar 

  33. Zhang Y, Li X (1999) A fast U-D factorization-based learning algorithm with applications to nonlinear system modeling and identification. IEEE Trans Neural Netw 10:930–938

    Article  Google Scholar 

  34. Obradovic D (1996) On-line training of recurrent neural networks with continuous topology adaptation. IEEE Trans Neural Netw 7:222–228

    Article  Google Scholar 

  35. Puskorius G, Feldkamp L (1994) Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks. IEEE Trans Neural Netw 5:279–297

    Article  Google Scholar 

  36. Birgmeier M (1995) A fully Kalman-trained radial basis function network for nonlinear speech modeling. IEEE international conference on neural networks, Perth, Western Australia, pp 259–264

  37. Nabney IT (1996) Practical methods of tracking of non-stationary time series applied to real world problems. In: Rogers SK, Ruck DW (eds) AeroSense’96: applications and science of artificial neural networks II, SPIE Proc. No. 2760, pp 152–163

  38. Connor J, Martin R, Atlas L (1994) Recurrent neural networks and robust time series prediction. IEEE Trans Neural Netw 5(2):240–254

    Article  Google Scholar 

  39. Puskorious G, Feldkamp L (1994) Neural control of nonlinear dynamic systems with Kalman filter trained recurrent networks. IEEE Trans Neural Netw 5(2):279–297

    Article  Google Scholar 

  40. Nelson AT, Wan EA (1997) Neural speech enhancement using dual extended Kalman filtering, ICNN, pp 2171–2175

  41. Ding L, Gao H, Xia K, Liu Z, Tao J, Liu Y (2012) Adaptive sliding mode control of mobile manipulators with Markovian switching joints. J Appl Math 10(3):812–836

    MathSciNet  MATH  Google Scholar 

  42. Wu SQ, Er MJ, Gao Y (2001) A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks. IEEE Trans Fuzzy Syst 9(4):578–594

    Article  Google Scholar 

  43. Xia K, Ding L, Gao H, Deng Z, Liu G, Wu Y (2016) Switch control for operating constrained mechanisms using a rescuing mobile manipulator with multiple working modes. In: Advanced Robotics and Mechatronics (ICARM), International Conference on IEEE, pp 139–146

  44. Gao H, Xia K, Ding L, Deng Z, Liu Z, Liu G (2015) Optimized control for longitudinal slip ratio with reduced energy consumption. Acta Astronaut (115):1–17

  45. Wu Q, Rete W (2000) Dynamic fuzzy neural networks-a novel approach to function approximation. IEEE Trans Syst Man Part B-Cybern 30(2):358–364

    Article  Google Scholar 

  46. Chen S, Cowan CFN, Grant PM (1991) Orthogonal least squares learning algorithm for radial basis function networks. IEEE Trans Neural Netw 2(2):302–309

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Key Basic Research Development Plan Project (973) (2013CB035502), National Natural Science Foundation of China (Grant No. 61370033/51275106), Harbin Talent Program for Distinguished Young Scholars (NO. 2014RFYXJ001). Fundamental Research Funds for the Central Universities (Grant No. HIT.BRETIII.201411), Foundation of Chinese State Key Laboratory of Robotics and Systems (Grant No. SKLRS201401A01), Postdoctoral Youth Talent Foundation of Heilongjiang Province, China (Grant No. LBH-TZ0403), and “111” Project (B07018).

Author information

Authors and Affiliations

  1. State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, 150001, China

    Kerui Xia, Haibo Gao, Liang Ding, Zongquan Deng, Zhen Liu & Changyou Ma

  2. Department of Aerospace Engineering, Ryerson University, Toronto, ON, M5B 2K3, Canada

    Guangjun Liu

Authors
  1. Kerui Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Haibo Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Liang Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Guangjun Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zongquan Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Zhen Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Changyou Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Haibo Gao.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xia, K., Gao, H., Ding, L. et al. Trajectory tracking control of wheeled mobile manipulator based on fuzzy neural network and extended Kalman filtering. Neural Comput & Applic 30, 447–462 (2018). https://doi.org/10.1007/s00521-016-2643-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-016-2643-7

Keywords

Search

Navigation