A non linear model predictive tracking controller for agricultural vehicles

It is expected that precision farming operations will increasingly rely on more complex automatic steering and navigation capabilities of agricultural vehicles. In this paper, a nonlinear model predictive tracking (NMPT) controller is presented for precision guidance in agricultural applications. The basic idea is to use a motion model for the vehicle and compute in real-time an optimal M-step-ahead control sequence, which minimizes the total M+1 step tracking error of the projected motion. Numerous simulations were performed and the NMPT consistently converged to the desired trajectories and followed them accurately, despite large initial errors and discontinuities in the desired velocities and orientations. The controller's performance was superior to pure-pursuit control and depended strongly on parameters such as the optimization horizon M, and the cost-weights assigned to the various tracking errors. The optimization horizon regulates a trade-off between tracking quality (large M) vs. consistently fast convergence (small M). The cost-weights affect tracking quality and also the shape of the path, by regulating trade-offs among position, orientation, and velocity errors. Overall, NMPT seems to offer a promising approach for advanced precision guidance applications, and deserves further investigation.