Autonomous tractors, with path tracking as one of the key technologies, have become a research hotspot in recent years. However, the nonlinearity of and uncertainties in the autonomous tractor path tracking process can lead to challenges in terms of control. Although the model-based control method has been widely used in path tracking and achieved good results, only a limited number of model-free adaptive control (MFAC) methods have been developed for path tracking. This paper proposes an MFAC path tracking controller for autonomous tractors based on the use of a preview heading deviation method in which the preview heading angle is used as an input to control the steering, enabling the controller to achieve a small lateral error and stable control. In the simulation, the tracking effects of the designed controller, proportional integral differential (PID) and pure pursuit controllers at different speeds were compared. The results show that the proposed controller has good tracking ability and adaptability and can achieve high tracking accuracy. Finally, the tracking errors of the designed and PID controllers were compared in a real vehicle test, with the results verifying the effectiveness and practicality of the designed controller.

A path-following control method based on dual heuristic programming (DHP) is proposed to address the problem whereby unmanned articulated vehicles need to set different controller parameters to track complex routes and cannot adapt to complex routes. First, the path-following control system structure is designed based on the articulated vehicle experimental platform, and the error model is derived based on the kinematic model of the articulated vehicle. Second, the payoff function is designed considering the error and stability indices, and the actor and critic of the path-following control method based on the DHP algorithm are approximated using a multilayer feedforward neural network. Finally, the path-following quality of the method is verified using simulations and real vehicle tests and compared with the conventional pure tracking and linear-quadratic regulator methods. The results show that the DHP-based method is able to follow complex reference paths and obtain better control results than the traditional methods without iterative tuning of the controller parameters, which improves the adaptiveness of the articulated vehicle path-following control method to complex environments.