Augmented Observer-Based Nonsmooth Integral Trajectory Tracking Control for Autonomous Agricultural Vehicles With Measurement Noises

Abstract

Efficient and robust trajectory tracking control is crucial for autonomous agricultural vehicles (AAVs) operating under complex and noisy field environments. However, measurement noises, model uncertainties, and external disturbances severely degrade tracking accuracy in practice. To address these challenges, we propose an augmented observer-based nonsmooth integral trajectory tracking control scheme for AAVs based on a mixed-offset model. The framework integrates an augmented fixed-time extended state observer (AFESO) and a generalized proportional integral observer (GPIO) to handle the longitudinal and lateral–heading subsystems, respectively. The AFESO achieves fixed-time disturbance estimation with built-in noise attenuation, while the GPIO ensures accurate disturbance compensation. On this basis, a nonsmooth integral controller is designed to guarantee fast convergence and smooth actuator behavior, effectively mitigating chattering compared with conventional sliding mode-like methods. Rigorous stability analysis is conducted via the fixed-time Lyapunov theory to prove closed-loop convergence. Finally, both simulation and field experiments demonstrate that the proposed control scheme significantly improves tracking accuracy and robustness over existing approaches. Note to Practitioners—AAVs operate in field environments where uneven terrain and sensor noise introduce significant disturbances and measurement uncertainties. In practice, these factors degrade trajectory-tracking accuracy and may cause excessive actuator wear when high-gain or discontinuous controllers are used. This paper proposes an observer-based nonsmooth integral control framework to address these challenges. An augmented fixed-time ESO provides fast disturbance estimation with built-in noise attenuation in the longitudinal channel, while generalized proportional–integral observers enhance disturbance compensation in the lateral–heading subsystem. By integrating these observers with a nonsmooth integral controller, the proposed scheme achieves rapid error convergence while maintaining smooth control inputs, thereby reducing chattering and mechanical stress. The modular structure enables straightforward implementation on embedded platforms. The proposed framework provides practitioners with a robust and deployable trajectory-tracking solution that improves accuracy and disturbance rejection in noise-corrupted agricultural environments, supporting reliable and high-precision autonomous field operations.