轮式探路机器人驱动控制系统研究

发布时间：2018-03-16 05:20

本文选题：轮式探路机器人　切入点：无刷直流电机　出处：《沈阳工业大学》2017年硕士论文　论文类型：学位论文

【摘要】：动力设备的高精度快速稳定控制一直是机器人研究的一个主要领域,而人工智能的高速发展更是推进了智能算法在智能控制的应用。模糊控制、神经网络自适应控制、遗传算法及滑模变结构控制等技术日益成熟,成为解决非线性复杂系统控制问题的解决方案。如何将智能算法无缝链接到轮式探路机器人驱动控制系统,从而提高其精度仍然成为控制领域的一大研究热点。本文采用Soildworks软件设计轮式探路机器人的底盘,建立一个四轮驱动的轮式探路机器人物理模型。根据机械原理进行动力学分析,进行运动学仿真确定机器人运动特性,再根据机器人的运动性能以及负载等要求选择合适的电机模型。然后选取一种无刷直流电机对机器人底盘车轮进行驱动并进行仿真分析。这种无刷直流电机的速度环控制器采用Anti-windup PI和积分滑模变结构控制两种方式,电流环采用电流滞环控制。通过对比,积分滑模控制器稳定性、快速性更为优异,但是积分滑模控制器对速度环控制时,存在抖振现象。为了解决抖振现象,引入一种BP神经网络来构成复合控制。设计的这种BP神经网络采取了在线更新学习的方式对滑模变结构系统进行调节,从而在减小无刷直流电机控制误差的同时,也削弱了无刷直流电机控制系统的抖振问题。本文构造一种滑模神经网络控制器对轮式探路机器人上的无刷直流电机进行速度环预测控制,与单独的积分滑模变结构控制的仿真结果相比较,抖振性得到改善,控制精度取得提高,从而验证设计的正确性和有效性,体现出该控制方法的优越性。
[Abstract]:The high precision, fast and stable control of power equipment has been a main field of robot research, and the rapid development of artificial intelligence has promoted the application of intelligent algorithm in intelligent control, fuzzy control, neural network adaptive control, neural network adaptive control, neural network adaptive control, neural network adaptive control, Genetic algorithm (GA) and sliding mode variable structure control (VSC) are becoming the solution to the control problem of nonlinear complex system. How to link the intelligent algorithm seamlessly to the driving control system of wheeled road detection robot, In order to improve its accuracy is still a research hotspot in the field of control. This paper uses Soildworks software to design the chassis of wheeled road detection robot. A physical model of a four-wheel drive wheeled road finding robot is established. According to the mechanical principle, the kinematics simulation is carried out to determine the kinematic characteristics of the robot. Then according to the motion performance and load requirements of the robot, the appropriate motor model is selected. Then, a brushless DC motor is selected to drive the robot chassis wheel, and the speed of the brushless DC motor is analyzed. The degree loop controller adopts Anti-windup Pi and integral sliding mode variable structure control. The current loop is controlled by current hysteresis loop. By contrast, the stability of the integral sliding mode controller is better than that of the integral sliding mode controller, but the chattering phenomenon exists when the integral sliding mode controller controls the velocity loop. A kind of BP neural network is introduced to form the compound control. The designed BP neural network adopts the on-line updating learning method to adjust the sliding mode variable structure system, thus reducing the control error of brushless DC motor at the same time. The chattering problem of brushless DC motor control system is also weakened. In this paper, a sliding mode neural network controller is constructed to predict the speed of brushless DC motor on wheeled robot. Compared with the simulation results of the single integral sliding mode variable structure control, the chattering property is improved and the control precision is improved, which verifies the correctness and effectiveness of the design and shows the superiority of the control method.
【学位授予单位】：沈阳工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP242

【参考文献】