基于神经网络的机器人智能控制

本文综述了智能控制与机器人控制的特点.并在智能控制的框架下,重点论述了神经网络控制在机器人控制中的应用及基于神经网络的机器人各种控制方法,同时指出今后的研究方向,为神经网络控制乃至智能控制在机器人控制中的应用提供了参考.     

神经网络在机器人位置和力混合控制中的应用

本文首次将神经网络应用于完成周期性任务的机器人力和位置混合控制。在研究系统动力学和系统稳定性基础上，提出了一种学习算法。最后进行了系统仿真实验以证明这种方法的可行性。     

模糊神经网络在机器人控制中的应用

本文首先阐明模糊理论和神经网络技术的结合形式,主要体现在四个方面;以及二者与人工智能技术之间的关系,在知识处理过程中,这两种技术往往可以相互代替,取长补短;机器人在从事装配、空间对接、医疗手术等与环境有接触的作业时,需要对机器人进行力控制,以达到最佳的作业效果,文章中介绍了模糊神经网络在机器人控制中的应用——并联机器人自适应力控制器。最后,针对医用骨科机器人,谈一谈模糊神经网络技术在研究中应用的可能性。     

人工神经网络在机器人控制中的应用

本文介绍了人工神经网络在机器人运动学、动力学机器人控制中的应用现状,指出了其中存在的困难,并提出了可能的研究方向.     

神经网络控制的应用

神经网络首先用于解决模式识别问题。随着80年代神经网络理论的突破性进展，神经网络控制引起控制界的广泛注意。由于系统的复杂性，存在多种不确定性及难以确切描述的非线性。现代工业对工业过程控制不仅要求控制的精确性，还要求控制的鲁律性、实时性、容错性及对控制参数的自适应和学习能力。传统适应控制方法存在局限性，传统控制未能从根本上解决控制问题，所以智能控制得到迅速发展。神经网络在系统辩识、模式识别、信号处理、图象处理、故障诊断等方面得到广泛应用。神经网络控制是从机理上对人脑进行简单结构模拟的新型控制和辩识方…     

基于神经网络的机器人智能控调

本文综述了智能控制与机器人控制的特点,并在智能控制的框架下,重点论述了神经网络控制在机器人控制中的应用及其于神经网络的机器人各种控制方法,同时指出今后的研究方向,为神经网络控制乃至智能控制在机器人控制中的应用提供了参考。     

不确定性机器人的神经网络补偿控制

本文研究具有不确定性的机器人的轨迹跟踪控制问题。提出了一种由计算力矩控制器和神经网络补偿控制器构成的控制方案。探讨了一种用神经网络估计机器人系统不确定性的途径。给出了神经补偿控制器的设计方法,并证明了闭环系统的收敛性。仿真结构表明所提方案具有很好的鲁棒性和抗干扰能力。     

智能控制及其在足球机器人中的应用

介绍了智能控制的发展历史与现状,智能控制的主要方法与当前的研究热点以及智能控制发展的前景与展望。同时介绍了各种控制方法在足球机器人领域的应用。     

基于神经网络的机器人轨迹跟踪控制

针对机器人模型未知情况，讨论了用神经网络和反馈控制实现机械手的跟踪控制。提出一种基于参考误差的投影算法来训练网络权值，训练后网络输出能逼近期望的前馈力矩，并从理论上证明跟踪误差的收敛性。仿真结果表明方案具有较好的跟踪性能和较强的抗干扰能力。     

Neural Network-Based Vision for Precise Control of a Walking Robot

This article describes a connectionist vision system for the precise control of a robot designed to walk on the exterior of the space station. The network learns to use video camera input to determine the displacement of the robot's gripper relative to a hole in which the gripper must be inserted. Once trained, the network's output is used to control the robot, with a resulting factor of five fewer missed gripper insertions than occur when the robot walks without sensor feedback. The neural network visual feedback techniques described could also be applied in domains such as manufacturing, where precise robot positioning is required in an uncertain environment.     

人工神经网络在伺服最优鲁棒控制中的应用

提出了一种基于神经网络的伺服最优鲁棒控制，介绍了利用神经网络的学习特性对被控对象的模型不确定性进行补偿和控制。仿真结果表明，所控制器优于一般伺服控制的性能，并有较强的鲁棒性。     

Stable Sampled-data Adaptive Control of Robot Arms Using Neural Networks

Stable neural network-based sampled-data indirect and direct adaptivecontrol approaches, which are the integration of a neural network (NN)approach and the adaptive implementation of the discrete variable structurecontrol, are developed in this paper for the trajectory tracking control ofa robot arm with unknown nonlinear dynamics. The robot arm is assumed tohave an upper and lower bound of its inertia matrix norm and its states areavailable for measurement. The discrete variable structure control servestwo purposes, i.e., one is to force the system states to be within the stateregion in which neural networks are used when the system goes out of neuralcontrol; and the other is to improve the tracking performance within the NNapproximation region. Main theory results for designing stable neuralnetwork-based sampled data indirect and direct adaptive controllers aregiven, and the extension of the proposed control approaches to the compositeadaptive control of a flexible-link robot is discussed. Finally, theeffectiveness of the proposed control approaches is illustrated throughsimulation studies.     

Performance Improvement of Robot Continuous-Path Operation through Iterative Learning Using Neural Networks

In this article, an approach to improving the performance of robot continuous-path operation is proposed. This approach utilizes a multilayer feedforward neural network to compensate for model uncertainty associated with the robotic operation. Closed-loop stability and performance are analyzed. It is shown that the closed-loop system is stable in the sense that all signals are bounded: it is further proved that the performance of the closed-loop system is improved in the sense that certain error measure of the closed-loop system decreases as the network learning process is iterated. These analytical results are confirmed by computer simulation. The effectiveness of the proposed approach is demonstrated through a laboratory experiment.     

Neural and Neurofuzzy FELA Adaptive Robot Control Using Feedforward and Counterpropagation Networks

In this paper, the application of neural networks and neurofuzzy systems to the control of robotic manipulators is examined. Two main control structures are presented in a comparative manner. The first is a Counter Propagation Network-based Fuzzy Controller (CPN-FC) which is able to self-organize and correct on-line its rule base. The self-tuning capability of the fuzzy logic controller is attained by taking advantage of the structural equivalence between the fuzzy logic controller and a counterpropagation network. The second control structure is a more familiar neural adaptive controller based on a feedforward (MLP) network. The neural controller learns the inverse dynamics of the robot joints, and gradually eliminates the model uncertainties and disturbances. Both schemes cooperate with the computed torque control algorithm, and in that way the reduction of their complexity is achieved. The ability of adaptive fuzzy systems to compete with neural networks in difficult control problems is demonstrated. A sufficient set of numerical results is included.     

改进的生物激励神经网络的机器人路径规划

介绍了基于生物激励神经网络的移动机器人路径规划。机器人的路径生成过程是由神经网络组成动态变化的神经元活性值状态路线图实现的。通过神经元活性值的传播,机器人被吸引到目标点,而同时障碍物使自己处在活性值最低点,起到推开机器人避碰的目的。仿真研究表明该方法生成的由起始点到目标点的路径是连续的、平滑的、避障的,不会陷入U形障碍物,与障碍物形状和所处位置无关,能对快速变化的环境做出迅速反应。但在当前位置邻近位置中具有最大活性值的位置不惟一的情况下,产生路径可能不理想,即到达目标点的避障路径是较长的,而不是最短或者是接近最短的。文中对该不足进行了分析,并提出了改进方法,使生成路径是最短的或是接近最短。对改进方法进行了仿真,实验结果证明该方法是有效的和可行的。     

一类神经网络智能PID控制算法的分析与改进

分析了一类神经网络智能ＰＩＤ控制算法的机理及该类算法存在的一些不足之处，提出一种基于输出多步预测的改进算法。仿真及应用表明，该方法对非线性和时变对象有较强的适应能力。     

基于模糊神经网络的机器人滑模自适应控制

滑模控制（SMC）响应快,对系统参数和外部扰动呈不变性,可保证系统的渐近稳定性,但其缺点是控制存在很强的抖动;而模糊神经网络（FNN）具有模糊系统和神经网络共同的特点。将滑模控制和模糊神经网络控制有机结合,利用简单得到的学习信号对模糊神经网络进行在线学习,通过平滑切换函数实现直接自适应控制策略。对两连杆机械手的仿真研究表明,在存在模型误差和外部扰动的情况下,该方案既能达到高精度快速跟踪的目的,又能有效减小滑模控制的抖动问题。     

Robust Adaptive Dead Zone Technology for Fault-Tolerant Control of Robot Manipulators Using Neural Networks

In this paper, a multi-layered feed-forward neural network is trained on-line by robust adaptive dead zone scheme to identify simulated faults occurring in the robot system and reconfigure the control law to prevent the tracking performance from deteriorating in the presence of system uncertainty. Consider the fact that system uncertainty can not be known a priori, the proposed robust adaptive dead zone scheme can estimate the upper bound of system uncertainty on line to ensure convergence of the training algorithm, in turn the stability of the control system. A discrete-time robust weight-tuning algorithm using the adaptive dead zone scheme is presented with a complete convergence proof. The effectiveness of the proposed methodology has been shown by simulations for a two-link robot manipulator.