Tracking precision of pre-planned
trajectories is essential for an auto-guided vehicle (AGV). The purpose of this
paper is to design a self-constructing wavelet neural network (SCWNN) method
for dynamical modeling and control of a 2-DOF AGV. In control systems of AGVs,
kinematical models have been preferred in recent research documents. However,
in this paper, to enhance the trajectory tracking performance through including
the AGV’s inertial effects in the control system, a learned dynamical model is
replaced to the kinematical kind. As the base of a control system, the
mathematical models are not preferred due to modeling uncertainties and
exogenous inputs. Therefore, adaptive dynamic and control models of AGV are
proposed using a four-layer SCWNN system comprising of the input, wavelet,
product, and output layers. By use of the SCWNN, a robust controller against
uncertainties is developed, which yields the perfect convergence of AGV to reference trajectories.
Owing to the adaptive structure, the number of nodes in the layers is adjusted
in online and thus the computational burden of the neural network methods is
decreased. Using software
simulations, the tracking performance of the proposed control system is
Cite this paper
Keighobadi, J. , Fazeli, K. and Shahidi, M. (2013) Self-Constructing Neural Network Modeling and Control of an AGV. Positioning, 4, 160-168. doi: 10.4236/pos.2013.42016.
 J. Keighobadi, M. B. Menhaj and M. Kabganian, “Feedback Linearization and Fuzzy Controllers for Trajectory Tracking of Wheeled Mobile Robots,” Kybernetes, Vol. 39, No. 1, 2010, pp. 83-106.
 J. Keighobadi, “Fuzzy Calibration of a Magnetic Compass for Vehicular Applications,” Mechanical Systems and Signal Processing, Vol. 25, No. 6, 2011, pp. 1973-1987.
 J. Keighobadi, M. J. Yazdanpanah and M. Kabganian, “An Enhanced Fuzzy H∞ Estimator Applied to Low-Cost Attitude-Heading Reference System,” Kybernetes, Vol. 40, No. 1, 2011, pp. 300-326.
 J. Keighobadi and M. B. Menhaj, “From Nonlinear to Fuzzy Approaches in Trajectory Tracking Control of Wheeled Mobile Robots,” Asian Journal of Control, Vol. 14, No. 4, 2012, pp. 960-973. doi:10.1002/asjc.480
 Y. Jun, “Adaptive Control of Nonlinear PID-Based Analog Neural Networks for a Nonholonomic Mobile Robot,” Neuro-Computing, Vol. 71, No. 7-9, 2008, pp. 1561-1565.
 Q. Zhang, “Using Wavelet Networks in Nonparametric Estimation,” IEEE Transactions on Neural Networks, Vol. 8, No. 2, 1998, pp. 227-236. doi:10.1109/72.557660
 J. Q. Guo, B. Y. Hai and D. X. Ai, “An Online Self-Constructing Wavelet Fuzzy Neural Network for Machine Condition Monitoring,” Proceeding of the 4th International Conference on Machine Learning and Cybernetics, Guangzhou, 18-21 August 2005, pp. 18-21.
 C. J. Lin, “Nonlinear Systems Control Using Self-Constructing Wavelet Networks,” Applied Soft Computing, Vol. 9, No. 1, 2009, pp. 71-79.
 J. Keighobadi and Y. Mohamadi, “Fuzzy Robust Trajectory Tracking Control of WMRs,” In: S. I. Ao, O. Castillo, and X. Huang, Eds., Intelligent Control and Innovative Computing, Springer, Singapore, 2012, pp. 77-90.
 L. Gaviphat, “Adaptive Self-Tuning Neuro Wavelet Network Controllers,” Ph.D. Thesis, Electrical Engineering Department, Blackburg, 1997.
 M. Y. Hu and W. Huo, “Robust and Adaptive Control of Nonholonomic Mechanical Systems with Application to Mobile Robots,” In: T. P. Leung and H. S. Qin, Eds., Advanced Topics in Nonlinear Control Systems, 2001, pp. 161-192.
 M. R. Sanner and J. J. E. Slotine, “Structureally Dynamic Wavelet Networks for Adaptive Control of Robotic Systems,” International Journal of Control, Vol. 70, No. 3, 1998, pp. 405-421. doi:10.1080/002071798222307
 F. J. Lin, R. J. Wai and M. P. Chen, “Wavelet Neural Network Control for Linear Ultrasonic Motor Drive via Adaptive Sliding-Mode Technique,” IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, Vol. 50, No. 6, 2003, pp. 686-698.