Model Free Adaptive Control Algorithm based on ReOSELM for Autonomous Driving Vehicles
作者单位：School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology The College of Computer Science and Artificial Intelligence Wenzhou University
会议名称：《第40届中国控制会议》
会议日期：2021年
学科分类：082304[工学-载运工具运用工程] 08[工学] 080204[工学-车辆工程] 0835[工学-软件工程] 0802[工学-机械工程] 080201[工学-机械制造及其自动化] 0823[工学-交通运输工程]
关 键 词：data-driven control autonomous driving vehicle model free adaptive control regularized online sequential extreme learning machine
摘 要：Different road conditions and dynamic environment bring significant challenges to the control system of autonomous driving vehicle（ADV）. As is known, historical data collected from ADV contains valuable information about control systems,therefore, it is a promising thing to study adaptive control algorithms that have certain learning ability. In order to improve the control performance of ADV and the efficiency in data usage, in this paper, a model free adaptive control algorithm based on regularized online sequential extreme learning machine（ReOSELM） is introduced, it is difficult to analyze the algorithm based on neural network, and the system stability by improved update algorithm of ReOSELM is proved. Simulation results indicate that the proposed algorithm is effective in improving control precision when ADV is turning, and experimental results on an autonomous driving vehicle show that this algorithm is effective in real environment.