车式自动导航车轨迹跟踪控制方法研究

发布时间：2018-11-24 14:54

【摘要】：移动机器人是研发较早的一类机器人,已悄然在当前生产生活中得到了广泛的应用。自动导航车(Aut o mate d Guide d Vehic le,AGV),是一种融合了机械、电子、传感器、无线通信、人工智能等先进技术的一类轮式移动机器人。它兼具感知、规划和决策等功能,能够实现按照规定导引线路行驶,具有较强的抗干扰能力和目标识别能力,在制造加工业、自动化港口码头、医药卫生以及特殊行业有着独特的应用优势。在自动化码头装卸系统中,它是一种负责集装箱水平运输的全自动搬运工具,是实现堆场无人化作业的重要组成部分。考虑到码头作业中的人为因素、天气因素和人工成本,发展及开拓AGV应用技术有助于我们提高生产效率和降低意外风险。本文将围绕四轮车式自动导航车的这一对象的轨迹跟踪控制展开研究。首先,对车式自动导航车建立运动学模型和动力学模型,就反演法控制方法,介绍了一种新的虚拟控制量并在运动学模型的基础上设计轨迹跟踪控制器,然后运用李雅普诺稳定性判据对该系统的全局一致稳定性进行理论分析,并进行仿真实验。其次,对蛙跳算法进行研究,针对蛙跳算法易陷入局部最优的缺点,提出了改进的蛙跳算法(I mpro ve d S huffled Fro g Leap ing A lgor ith m,IS FLA),运用测试函数检验其优化性能,并对该算法进行收敛性分析。然后将IS FLA算法应用于车式自动导航车中,仿真实验结果表明,该导航车对目标轨迹的跟踪精度有所提高。最后,将自动导航车的运动学模型与动力学模型相结合,在第三章研究内容的基础上,针对AGV的初始时刻误差跳变大的问题,提出结合运动学和动力学模型的串级控制策略,其中,外环控制采用反演法来设计运动学模型的控制律,内环控制引入滑模变结构控制的方法来设计驱动力控制器。在设计滑模变结构控制的控制律时,采用双曲正切函数替代符号函数,且采用模糊切换增益的方法消除滑模变结构控制引起的系统抖振现象,以保证系统运行状态平滑。仿真实验表明,该控制策略提高了导航车跟踪目标轨迹的精度。
[Abstract]:Mobile robot is a kind of robot that was developed earlier and has been widely used in current production and life. Automatic Navigation vehicle (Aut o mate d Guide d Vehic le,AGV) is a kind of wheeled mobile robot which combines the advanced technology of machinery, electronics, sensor, wireless communication, artificial intelligence and so on. It has the functions of perception, planning and decision making, it can drive according to the prescribed guiding line, it has strong anti-interference ability and target recognition ability, and it can be used in manufacturing processing industry and automatic port wharf. Medical and health care and special industries have unique application advantages. In the automatic terminal loading and unloading system, it is a kind of automatic handling tool in charge of container horizontal transportation, which is an important part of realizing the unmanned operation of the yard. Considering the human factors, weather factors and labor cost in dock operation, the development and development of AGV application technology will help us to improve production efficiency and reduce accident risk. In this paper, the trajectory tracking control of this object of four-wheeled automatic navigation vehicle will be studied. First of all, the kinematics model and dynamic model of vehicle automatic navigation vehicle are established. A new virtual control quantity is introduced and a trajectory tracking controller is designed on the basis of kinematics model. Then the global uniform stability of the system is theoretically analyzed by using the Lyapunov stability criterion, and the simulation experiment is carried out. Secondly, this paper studies the leapfrog algorithm, aiming at the disadvantage that the leapfrog algorithm is easy to fall into local optimum, an improved leapfrog algorithm, (I mpro ve d S huffled Fro g Leap ing A lgor ith mi is FLA), is put forward to test its optimization performance by using test function. The convergence of the algorithm is analyzed. Then the IS FLA algorithm is applied to the vehicle automatic navigation vehicle. The simulation results show that the tracking accuracy of the vehicle is improved. Finally, the kinematics model and dynamic model of automatic navigation vehicle are combined. Based on the research content of chapter 3, aiming at the problem of large error jump in the initial moment of AGV, a cascade control strategy combining kinematics and dynamics model is proposed. The outer loop control uses the inverse method to design the control law of the kinematics model, and the inner loop control introduces the sliding mode variable structure control method to design the driving force controller. In the design of the control law of sliding mode variable structure control, the hyperbolic tangent function is used to replace the symbol function, and the fuzzy switching gain is used to eliminate the chattering phenomenon caused by sliding mode variable structure control, so as to ensure the smooth running state of the system. The simulation results show that the control strategy improves the precision of tracking the target.
【学位授予单位】：上海电机学院
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP242

【参考文献】