Intelligent Sweeper Path Tracking Control Based on Optimal Iterative Learning

Abstract

This paper studies the path tracking control system of intelligent sweeper. According to the fact that the intelligent sweeper mainly carries out the cleaning and sprinkling work in a fixed section periodically and has strong repeatability, this paper studies the path tracking control problem of the intelligent sweeper based on the optimal iterative learning control method. Firstly, the traditional intelligent sweeper path tracking system is transformed into the pre-sighting deviation angle tracking system. And the dynamic linearization data model is obtained by using the improved iterative dynamic linearization method. Secondly, an iterative expansion observer is added in this paper to deal with uncertain disturbances such as turbulence and measurement error in the path tracking control system of intelligent sweeper. It compensates for the unknown disturbance. Finally, the parameter iteration update rate and the optimal learning control rate of the intelligent sweeper control system are proposed. The optimal iterative control method with an iterative expansion observer can effectively utilize the repeated information in the path tracking process of the intelligent sweeper and effectively estimate the unknown disturbance. And it only utilizes the input and output information of the system, avoiding the difficult problem of modeling. Simulation results show the effectiveness of the proposed method.