Adaptive prescribed performance path following control of autonomous ground vehicles with unknown tire cornering stiffness and actuator saturation.

This paper studies the path-following control of autonomous ground vehicles by introducing a prescribed performance control method. A neural network-based adaptive backstepping controller is proposed to handle the composite uncertainties arising from unknown modeling errors and time-varying cornering stiffness. In particular, a set of monotonic tube functions and an error transformation are introduced to guarantee that the tracking error always satisfies the prescribed transient and steady-state performance in the presence of unknown tire stiffness coefficients and actuator saturation. Compared with traditional prescribed performance methods, the proposed control scheme can quantitatively adjust overshoot, settling time, and steady-state accuracy. Another key merit is the construction of a proportional integral compensation signal to actively attenuate the oscillations of tracking errors and intermediate errors. Meanwhile, a boundary estimation method is proposed to eliminate the requirement for a priori knowledge of unknown tire cornering stiffness in the virtual control coefficient. Finally, the proposed method is verified through simulation examples.