汽车驾驶机器人研究中的一个关键问题就是多机械手的协调控制。为了实现驾驶机器人换档机械手和油门、离合、制动机械腿的综合协调控制,最终实现对给定循环行驶工况的车速跟踪,首先建立了基于Saridis G N三级控制架构的驾驶机器人递阶控制模型体系结构,然后在此基础上提出了驾驶机器人多机械手协调控制方法,并设计了油门/离合器协调控制器和油门/制动切换控制器。试验结果表明,本文提出的方法能合理协调控制汽车驾驶机器人油门、制动、离合机械腿和换挡机械手,实现了车辆的平稳起步,平顺换挡以及对给定车速的跟踪。
In order to improve the accuracy and reliability of the driving fatigue detection based on a single feature, a new detection algorithm based on multiple features is proposed. Two direct driver＇s facial features reflecting fatigue and one indirect vehicle behavior feature indicating fatigue are considered. Meanwhile, T-S fuzzy neural network（TSFNN）is adopted to recognize the driving fatigue of drivers. For the structure identification of the TSFNN, subtractive clustering（SC） is used to confirm the fuzzy rules and their correlative parameters. Moreover, the particle swarm optimization （PSO）algorithm is improved to train the TSFNN. Simulation results and experiments on vehicles show that the proposed algorithm can effectively improve the convergence speed and the recognition accuracy of the TSFNN, as well as enhance the correct rate of driving fatigue detection.
