Active Disturbance Rejection Path-following Control for Self- driving Forklift Trucks with Geometry based Feedforward

Abstract

The self-driving forklift, as a promising technology to reduce the labor intensity of workers, can also improve the efficiency of logistics freight transportation. In this paper, a path-following controller that combines cascaded active disturbance rejection controller and geometry-based feedforward controller, is proposed. The cascaded controller, designed based on a kinematic model, minimizes the lateral error via the outer-loop by mitigating the desired heading direction, and then achieved by the inner loop through adjusting the steering angle. The deviation between the simplified kinematic model and the actual forklift motion is lumped as a total disturbance, to be observed by the extended state observer (ESO). In order to enhance the transient response, a geometry-based feedforward controller is developed, computing the desired steering angle through preview. The proposed method effectively improves the response speed and reduces the overshoot. The effectiveness of the algorithm is quantitatively evaluated in experiments.