Parameter self‐learning feedforward compensation‐based active disturbance rejection for path‐following control of self‐driving forklift trucks

Abstract

In this paper, for self‐driving forklift, a path‐following framework based on composite disturbance rejection that combines a cascaded active disturbance rejection controller and an online estimator of model parameters is proposed on the basis of a geometry feedforward controller. The model‐based cascaded active disturbance rejection control (ADRC), in particular, is composed of an outer‐loop controller and an inner‐loop controller, with the outer‐loop controller reducing lateral error by manipulating the desired heading direction and the inner‐loop controller achieving heading control by adjusting the steering angle. The unmodeled dynamics of the actual forklift motion are denoted as total disturbances, which can be timely estimated by the extended state observer (ESO). Moreover, to increase the transient response, a parameter self‐learning based geometry feedforward controller is designed in conjunction with the forklift characteristics. The stability analysis is presented and the effectiveness of the composite algorithm is quantitatively evaluated in experiments, demonstrating the superiority over the conventional pure pursuit algorithm and the only use of ADRC.