【作者】 唐得志；

【导师】 王洪瑞； 宋维公；

【作者基本信息】 燕山大学 ， 控制理论与控制工程， 2003， 硕士

【摘要】 机器人的控制问题无论在理论界还是工程界多年来一直倍受人们的关注。当机器人系统模型是精确知道的时候，反馈线性化技术可以很好的解决其控制问题，然而现实的操作过程中机器人动力学模型的各个参数可能发生变化，同时还受到环境干扰和负载变化等许多不确定因素的影响，因此有必要对现有的控制方法加以改进。本论文以具有完整动力学模型的机器人系统，即不确定机器人系统为研究对象，在现有文献的基础上，重点探讨了基于智能算法的各种鲁棒控制策略。本论文第1章介绍了机器人的发展概况和机器人控制理论概况；第2章给出控制器设计所需的数学知识和机器人动力学模型；第3章提出一种模糊自适应控制与滑模监督项相结合的控制方法，其中模糊控制以其简单的规则集和隶属度函数有效的补偿了系统的不确定性，然后利用一个低抖振的滑模监督项消除了模糊控制系统的逼近误差；第4章提出了基于Backstepping方法的不确定机器人模糊神经网络控制，这里模糊神经网络学习了系统理想的反馈线性化控制律，并采用一个鲁棒项补偿了模糊神经网络的学习误差，整个控制器的设计过程都是基于Backstepping设计方法，有效的保证了系统的全局稳定性；第5章针对工业机器人仅有关节位置测量的情况，提出了基于RBF神经网络的滑模观测器来观测关节的速度信号，将滑模方法融入观测器的设计提高了其抗干扰能力，这里RBF神经网络补偿了机器人系统的各种不确定性，避免了回归矩阵的计算和对惯性矩阵先验知识的要求，并充分考虑控制器与观测器的相互影响，保证了重构的速度信号能代替实际的速度信号用到控制策略的反馈回路中，仿真结果证明了所提方法的正确性。更多还原

【Abstract】 The control problems of robotic manipulators have received great attention in theoretical and engineering for many years. When the robot model is exactly known, the technique of feedback linearization in nonlinear systems can solve the problem very well. However, the parameters of dynamic model of robotic manipulators may also be subject to change when the manipulator goes about its task. Meanwhile, the system can be influenced by uncertainties such as external disturbance and payload change. Therefore it is necessary to improve these existing control methods In this dissertation, the system of robotic manipulators with entire dynamic model, namely, the robotic system with uncertainties is regarded as controlled plant. The various robust control schemes based intelligent algorithms are developed using the references available.The first chapter of dissertation gives a brief description about the developing situation and control theory of robot. The second chapter introduces necessary mathematic knowledge of controller design and the dynamic model of robot. The third chapter put forward a fuzzy adaptive control method combining sliding supervisory control term. The fuzzy controller associated with simple rule base and membership function effectively compensate the system uncertainties. Afterward utilizing a sliding supervisory control term which produces a low chattering eliminate the approaching error of fuzzy control system. The fourth chapter bring forward the fuzzy neural network control of uncertain robot using backstepping method. Here fuzzy neural network is used to learn the ideal feedback linearization control law, and through adopting a robust term compensate learning error via fuzzy neural network. The overall designing process of controller is based on backstepping method and the whole stability of system is effectively guaranteed.<WP=6>The fifth chapter introduces a sliding observer using RBF neural network to observe the speed signal aiming at only having position measurement in industrial robot. The observer combining sliding method improve the ability of resisting disturbance. RBF neural network is used to compensate various uncertainties of robot. In addition, the method avoid the computation of regressor matrix and the demand of the priori knowledge of inertia matrix. Taking into account the dynamic interactions between the observer and controller dynamics, it can be ensured that the fictitious speed signal can substitute the real speed signal and is employed in the feedback loop. Simulation results prove the effectiveness of proposed methods.更多还原