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
assessed.
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