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.     

An iterative learning scheme for motion control of robots using neural networks: A case study

In this paper, an iterative learning controller using neural networks has been studied for the motion control of robotic manipulators. Simulations of a two-link robot have demonstrated that the proposed control scheme for robotic manipulators can greatly reduce tracking errors after a few trials. Our modification of the original back-propagation algorithm is employed in the neural network, resulting in a much faster learning rate. The results of simulation have also shown that the proposed iterative learning controller has a faster rate of convergence and better robustness.     

采用遗传算法训练对角递归神经网络预测控制器

本文提出了一种基于广义预测控制的神经网络预测控制方案．预测控制器由对角递归 神经网络预测控制器和前向神经网络静态补偿器组成．两种神经网络均采用遗传算法进行训 练．仿真实验表明,对于带纯时延的非线性被控对象,采用遗传算法设计的对角递归神经网 络预测控制器具有令人满意的控制性能．     

A Neural Net Predictive Control for Telerobots with Time Delay

This paper extends the Smith Predictor feedback control structure to unknown robotic systems in a rigorous fashion. A new recurrent neural net predictive control (RNNPC) strategy is proposed to deal with input and feedback time delays in telerobotic systems. The proposed control structure consists of a local linearized subsystem and a remote predictive controller. In the local linearized subsystem, a recurrent neural network (RNN) with on-line weight tuning algorithm is employed to approximate the dynamics of the time-delay-free nonlinear plant. The remote controller is a modified Smith predictor for the local linearized subsystem which provides prediction and maintains the desirable tracking performance. Stability analysis is given in the sense of Lyapunov. The result is an adaptive compensation scheme for unknown telerobotic systems with time delays, uncertainties, and external disturbances. A simulation of a two-link robotic manipulator is provided to illustrate the effectiveness of the proposed control strategy.     

Neuro-fuzzy position control of demining tele-operation system based on RNN modeling

The paper considers the neuro-fuzzy position control of multi-finger robot hand in tele-operation system—an active master–slave hand system (MSHS) for demining. Recently, fuzzy control systems utilizing artificial intelligent techniques are also being actively investigated in robotic area. Neural network with their powerful learning capability are being sought as the basis for many adaptive control systems where on-line adaptation can be implemented. Fuzzy logic on the other hand has been proved to be rather popular in many control system applications providing a rule-base like structure. In this paper, the design and optimization process of fuzzy position controller is supported by learning techniques derived from neural network where a radial basis function (RBF) neural network is implemented to learn fuzzy rules and membership functions with predictor of recurrent neural network (RNN) model. The results of experiment show that based on the predictive capability of RNN model neuro-fuzzy controller with good adaptation and robustness capability can be designed.     

A Neural Network Adaptive Controller for End-effector Tracking of Redundant Robot Manipulators

In this paper we propose a neural network adaptive controller to achieve end-effector tracking of redundant robot manipulators. The controller is designed in Cartesian space to overcome the problem of motion planning which is closely related to the inverse kinematics problem. The unknown model of the system is approximated by a decomposed structure neural network. Each neural network approximates a separate element of the dynamical model. These approximations are used to derive an adaptive stable control law. The parameter adaptation algorithm is derived from the stability study of the closed loop system using Lyapunov approach with intrinsic properties of robot manipulators. Two control strategies are considered. First, the aim of the controller is to achieve good tracking of the end-effector regardless the robot configurations. Second, the controller is improved using augmented space strategy to ensure minimum displacements of the joint positions of the robot. Simulation examples are also presented to verify the effectiveness of the proposed approach.     

Adaptive control of nonlinear PID-based analog neural networks for a nonholonomic mobile robot

An adaptive controller of nonlinear PID-based analog neural networks is developed for the velocity- and orientation-tracking control of a nonholonomic mobile robot. A superb mixture of a conventional PID controller and a neural network, which has powerful capability of continuously online learning, adaptation and tackling nonlinearity, brings us the novel nonlinear PID-based analog neural network controller. It is appropriate for a kind of plant with nonlinearity uncertainties and disturbances. Computer simulation for a differentially driven nonholonomic mobile robot is carried out in the velocity- and orientation-tracking control of the nonholonomic mobile robot. The effectiveness of the proposed control algorithm is demonstrated through the simulation experiment, which shows its superior performance and disturbance rejection.     

球形移动机器人基于神经网络的反馈线性化运动控制研究与实验

实现了一种对球形移动机器人的滚动速度进行控制的方法.球形移动机器人的控制输入和状态输出间存在难以精确数学描述的非线性关系,本文采用径向基函数神经网络,以在线训练的方式建立了球形机器人输入与输出的非线性映射;然后采用反馈线性化方法,设计了球形机器人的速度控制器,该控制器由反馈线性化控制器和减小神经网络逼近误差的补偿控制器构成;给出了该控制器的实现步骤.多次实验结果表明,该方法可以实现球形移动机器人稳定的速度控制.     

Design of Adaptive Robot Control System Using Recurrent Neural Network

The use of a new Recurrent Neural Network (RNN) for controlling a robot manipulator is presented in this paper. The RNN is a modification of Elman network. In order to solve load uncertainties, a fast-load adaptive identification is also employed in a control system. The weight parameters of the network are updated using the standard Back-Propagation (BP) learning algorithm. The proposed control system is consisted of a NN controller, fast-load adaptation and PID-Robust controller. A general feedforward neural network (FNN) and a Diagonal Recurrent Network (DRN) are utilised for comparison with the proposed RNN. A two-link planar robot manipulator is used to evaluate and compare performance of the proposed NN and the control scheme. The convergence and accuracy of the proposed control scheme is proved.     

神经网络自适应滑模控制的不确定机器人轨迹跟踪控制

提出一种针对机器人跟踪控制的神经网络自适应滑模控制策略。该控制方案将神经网络的非线性映射能力与滑模变结构和自适应控制相结合。对于机器人中不确定项,通过RBF网络分别进行自适应补偿,并通过滑模变结构控制器和自适应控制器消除逼近误差。同时基于Lyapunov理论保证机器手轨迹跟踪误差渐进收敛于零。仿真结果表明了该方法的优越性和有效性。     

基于卷积神经网络的手术机器人控制系统设计

针对传统系统控制精准度低的问题,提出了基于卷积神经网络的手术机器人控制系统设计。根据基于卷积神经网络的手术机器人控制原理,设计控制系统总体结构,选用PCI插槽上直接内插CAN适配卡作为上位机核心组件,采用C++编写的Lib库和DLL库为驱动程序提供适配卡。通过下位机三个节点,处理相关信号,并进行量程转换和越限判断,确保机器人不会失控。选用80C592微控制器设计关节驱动节点结构,以高速工作方式向控制器提供向总线的差动发送和接受能力,避免外界干扰。设计基于视觉的持镜臂,为手术过程提供上下、左右、前后的运动的手术视野。分别采用FN3002力传感器和MPS-M拉线式位移传感器获取相关传感数据,采用卷积神经网络深度学习方法,设计持镜臂运动控制步骤,采用VC++6.0工具,控制软件程序,避免抖动或者误操作主手现象的出现。由实验结果可知,该系统持镜臂轨迹规划与期望轨迹一致,简化了控制系统的复杂性。     

不确定机器人的自适应神经网络控制与学习

针对具有未知动态的电驱动机器人,研究其自适应神经网络控制与学习问题.首先,设计了稳定的自适应神经网络控制器,径向基函数(RBF)神经网络被用来逼近电驱动机器人的未知闭环系统动态,并根据李雅普诺夫稳定性理论推导了神经网络权值更新律.在对回归轨迹实现跟踪控制的过程中,闭环系统内部信号的部分持续激励(PE)条件得到满足.随着PE条件的满足,设计的自适应神经网络控制器被证明在稳定的跟踪控制过程中实现了电驱动机器人未知闭环系统动态的准确逼近.接着,使用学过的知识设计了新颖的学习控制器,实现了闭环系统稳定、改进了控制性能.最后,通过数字仿真验证了所提控制方法的正确性和有效性.     

高速公路匝道回归神经网络控制器的设计

提出用回归神经网络进行入口匝道控制的思路。阐述了Elman回归神经网络原理与入口匝道控制原理,选取上、下游时间占有率和车速作为匝道控制器的输入量,并设计了Elman回归神经网络入口匝道控制器,采用一种改进的算法对回归神经网络进行训练。仿真实验表明,该控制器学习误差小,泛化能力好,具有良好的应用前景。     

基于机器人的神经网络预测控制算法

针对预测控制机理在处理非线性模型控制存在较大的困难,提出了将BP神经网络和广义预测控制（GPC）相结合后应用于网络控制系统的思想,构造了神经网络预测控制算法,其实质是用BP神经网络作为预测模型,产生预测信号,对系统进行反馈校正,并通过误差迭代求取广义预测的最优控制律,从而克服了对非线性系统难以辨识模型的困难,利用神经网络“黑箱”的功能达到对非线性系统的预测控制。以机器人为控制对象进行仿真,取得了较好的控制效果。     

基于神经网络的时滞非线性系统的广义预测控制

针对一类时滞非线性被控对象,提出一种基于RBF神经网络的广义预测自校正控制方案,在广义预测控制中,采用RBF神经网络建立被控对象的多步预测模型,并不断修正预测输出,提高预测输出的精度.控制器则采用GPC隐式修正算法,不用辨识对象的模型参数,大大减少了计算量.经过仿真研究,与常规的PID自适应控制方法相比较,证明了该方法的优越性,预测控制误差小,实时性好,动态响应快.     

机器人鲁棒轨迹跟踪控制系统

根据鲁棒控制理论和机器人的动态特性,针对机器人系统中存在的不确定性因素,利用不确定性的上界设计了一种鲁棒控制器,并将之用于机器人的跟踪轨迹控制,给出了仿真实验结果并与PID控制的结果进行了比较,仿真实验结果表明所设计的鲁棒控制器与PID控制器相比,具有很好的动态特性和很强的鲁棒性。     

Fuzzy and Recurrent Neural Network Motion Control among Dynamic Obstacles for Robot Manipulators

An integration of fuzzy controller and modified Elman neural networks (NN) approximation-based computed-torque controller is proposed for motion control of autonomous manipulators in dynamic and partially known environments containing moving obstacles. The fuzzy controller is based on artificial potential fields using analytic harmonic functions, a navigation technique common used in robot control. The NN controller can deal with unmodeled bounded disturbances and/or unstructured unmodeled dynamics of the robot arm. The NN weights are tuned on-line, with no off-line learning phase required. The stability of the closed-loop system is guaranteed by the Lyapunov theory. The purpose of the controller, which is designed as a neuro-fuzzy controller, is to generate the commands for the servo-systems of the robot so it may choose its way to its goal autonomously, while reacting in real-time to unexpected events. The proposed scheme has been successfully tested. The controller also demonstrates remarkable performance in adaptation to changes in manipulator dynamics. Sensor-based motion control is an essential feature for dealing with model uncertainties and unexpected obstacles in real-time world systems.     

未建模误差补偿Elman网络直接广义预测控制

针对一类非线性系统,提出了Elman网络直接广义预测控制算法。先将非线性系统等价转换成线性系统,然后对未建模动态误差进行估计,最后利用Elman网络进行预测控制器设计,并根据跟踪误差对控制器参数中的未知向量进行自适应调整,理论证明了误差收敛到原点一个小邻域内。该方法不需要求解Diophantine方程和矩阵求逆,只需要辨识一个参数θ（k）,因此减少了在线计算量,提高了实时性。仿真结果验证了该方法的有效性。     

基于改进型BP网络的并联机器人自适应力控制

并联机器人力控制是并联机器人研究的一个热点和难点,引起了许多学者的关注,并取得了一定的成果。多数使用了传统的力控制研究方法。该文中,作者将神经网络引入并联机器人的力控制中,并介绍了一种改进型BP神经网络,以及其学习算法和网络的训练过程,并结合实际并联机器人6-SPS并联机器人,设计出基于改进型BP神经网络的并联机器人自适应力控制器,并进行了仿真和实验研究,通过研究表明所设计的控制器是可行和有效的。