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In this paper, the coordinated control problem of a tractor-trailer and a combine harvester is taken into account in the presence of model uncertainties by using the leader-following approach to track a reference trajectory for the first time. At first, a second-order leader-follower dynamic model is developed in Euler-Lagrange form which preserves all structural properties of the dynamic model. Then, the tractor-trailer is controlled to maintain a separation distance and a relative bearing angle with respect to the combine harvester. For this purpose, the controller is designed by using an adaptive robust neural network technique. In this control scheme, the parametric uncertainties such as masses, the moments of inertia and other physical parameters are estimated by a radial basis function neural network (RBFNN) and, then, nonparametric uncertainties such as unmodeled dynamics, friction and the slippage of wheels are compensated by D ow nl oa de d fr om jo c. kn tu .a c. ir at 1 5: 24 + 04 30 o n T hu rs da y A pr il 30 th 2 02 0 24 لرتنک گنهامه کي روتکارت رليرت و نيشام تشادرب لوصحم نيابمک اب هدافتسا زا لرتنک مواقم يقيبطت يبصع اسيرپ ،یدمحمرهم مانشوخ يعاجش Journal of Control, Vol. 13, No. 4, Winter 2020 دلج ،لرتنک هلجم 13 هرامش ، 4 ، ناتسمز 1398 the adaptive robust control term. Moreover, it will be shown that the controller makes the tracking errors converge to a small bound around the origin in the presence of uncertainties. The stability of the proposed controller is demonstrated by an analysis based on Lyapunov theory. Finally, the proposed control scheme is simulated by MATLAB software and its validity will be shown and it is compared with the backstepping controller.

Coordinated Control of a Tractor-Trailer and a Combine Harvester by Neural Adaptive Robust Control