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。     

Robust Backstepping Control of Robotic Systems Using Neural Networks

Neural network (NN) controllers for the robust back stepping control of robotic systems in both continuous and discrete-time are presented. Control action is employed to achieve tracking performance for unknown nonlinear system. Tuning methods are derived for the NN based on delta rule. Novel weight tuning algorithms for the NN are obtained that are similar to -modification in the case of continuous-time adaptive control. Uniform ultimate boundedness of the tracking error and the weight estimates are presented without using the persistency of excitation (PE) condition. Certainty equivalence is not used and regression matrix is not computed. No learning phase is needed for the NN and initialization of the network weights is straightforward. Simulation results justify the theoretical conclusions.     

考虑柔性关节的机械臂自适应运动控制策略研究

具有柔性关节的轻型机械臂因其自重轻、响应迅速、操作灵活等优点,取得了广泛应用;针对具有柔性关节的机械臂系统的关节空间轨迹跟踪控制系统动力学参数不精确的问题,提出一种结合滑模变结构设计的自适应控制器算法;通过自适应控制的思想对系统动力学参数进行在线辨识,并采用Lyapunov方法证明了闭环系统的稳定性;仿真结果表明,该控制策略保证了机械臂系统对期望轨迹的快速跟踪,具有良好的跟踪精度,系统具有稳定性。     

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.     

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.     

Design of a Robust Adaptive Neural Tracking Controller

A robust neural tracking controller is designed based on the conic sector theory. An adaptive dead zone scheme is employed to enhance robustness of the system. The proposed algorithm does not require knowledge of either the upper bound of disturbance or the bound on the norm of the estimate parameter. A complete convergence proof is provided based on the sector theory to deal with the nonlinear system. Simulation results are presented to control a two-link direct drive robot and show the performance of the tracking controller.     

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.     

Robust Adaptive Control of Robots Using Neural Network: Global Stability

A desired compensation adaptive law‐based neural network (DCAL‐NN) controller is proposed for the robust position control of rigid‐link robots. The NN is used to approximate a highly nonlinear function. The controller can guarantee the global asymptotic stability of tracking errors and boundedness of NN weights. In addition, the NN weights here are tuned on‐line, with no offline learning phase required. When compared with standard adaptive robot controllers, we do not require linearity in the parameters, or lengthy and tedious preliminary analysis to determine a regression matrix. The controller can be regarded as a universal reusable controller because the same controller can be applied to any type of rigid robots without any modifications. A comparative simulation study with different robust and adaptive controllers is included.     

不依赖模型的机器人鲁棒自适应跟踪控制(英)

提出了一种新颖的鲁棒自适应控制策略，用于不确定性机器人的轨迹跟踪．它不需要任何模型知识，唯一需了解的是系统的阶数和输出的位置及速度状态。理论和仿真均证明，系统的不确定性诸如摩擦力、外部扰动及未建模动力学带来的不确定性影响，均可被设计的控制律补偿，最后可保证全局指数收敛或全局一致最后有界的结果．另外，本文还给出了跟踪误差的暂态测量．     

基于神经网络的鲁棒自适应控制

考虑摩擦及外界干扰的情况下,针对具有不确定性参数的机器人系统,提出一种基于神经网络动态补偿的鲁棒自适应控制策略,采用神经网络在线补偿控制器以克服系统的外部扰动,未建模动力学部分等非参数不定性带来的影响,从而提高了系统的动态性能和稳态精度,并对闭环系统稳定性进行了证明,仿真结果表明,所提方法具有良好的跟踪性能和较强的鲁棒性。     

Robust Adaptive Sliding Mode Neural Networks Control for Industrial Robot Manipulators

International Journal of Control, Automation and Systems - This paper proposes an original robust adaptive controller by using Radial Basis Function Neural networks (RBFNNs) for industrial robot...     

基于递归神经网络的一类非线性无模型系统的自适应控制

给出了基于递归神经网络非线性无模型的自适应控制方案，它具有灵活、简单、方法等特点，可以处理传统方法和非线性无模型系统自适应控制方法不能控制或控制效果不理想的非线性对象。理论分析和仿真结果证明了这种方法的优越性。     

Locomotion Control of a Hydraulically Actuated Hexapod Robot by Robust Adaptive Fuzzy Control with Self-Tuned Adaptation Gain and Dead Zone Fuzzy Pre-compensation

Hydraulically actuated robotic mechanisms are becoming popular for field robotic applications for their compact design and large output power. However, they exhibit nonlinearity, parameter variation and flattery delay in the response. This flattery delay, which often causes poor trajectory tracking performance of the robot, is possibly caused by the dead zone of the proportional electromagnetic control valves and the delay associated with oil flow. In this investigation, we have proposed a trajectory tracking control system for hydraulically actuated robotic mechanism that diminishes the flattery delay in the output response. The proposed controller consists of a robust adaptive fuzzy controller with self-tuned adaptation gain in the feedback loop to cope with the parameter variation and disturbances and a one-step-ahead fuzzy controller in the feed-forward loop for hydraulic dead zone pre-compensation. The adaptation law of the feedback controller has been designed by Lyapunov synthesis method and its adaptation rate is varied by fuzzy self-tuning. The variable adaptation rate helps to improve the tracking performance without sacrificing the stability. The proposed control technique has been applied for locomotion control of a hydraulically actuated hexapod robot under independent joint control framework. For tracking performance of the proposed controller has also been compared with classical PID controller, LQG state feedback controller and static fuzzy controller. The experimental results exhibit a very accurate foot trajectory tracking with very small tracking error with the proposed controller.     

基于神经网络的反馈式自适应逆控制系统

由于在建立非线性逆模型时采用带有复杂非线性函数的滤波器来完成,而由此带来了结构复杂、运算量大等缺点,在实际运用上受到了很多限制.为此,采用较为简单的线性逆控制方式与单层神经网络相结合构成逆控制结构,其良好的非线性特性使系统具有逼近任何非线性模型的能力,且结构简单实用.应用举例表明这种方法在运算量和控制性能上均取得了非常好的效果.     

A Robust Adaptive Terminal Sliding Mode Control for Rigid Robotic Manipulators

In this paper, a robust adaptive terminal sliding mode controller is developed for n-link rigid robotic manipulators with uncertain dynamics. An MIMO terminal sliding mode is defined for the error dynamics of a closed loop robot control system, and an adaptive mechanism is introduced to estimate the unknown parameters of the upper bounds of system uncertainties in the Lyapunov sense. The estimates are then used as controller parameters so that the effects of uncertain dynamics can be eliminated and a finite time error convergence in the terminal sliding mode can be guaranteed. Also, a useful bounded property of the derivative of the inertial matrix is explored, the convergence rate of the terminal sliding variable vector is investigated, and an experiment using a five bar robotic manipulator is carried out in support of the proposed control scheme.     

基于LabVIEW的神经网络自适应逆控制系统

目前基于人工神经网络的非线性自适应逆控制研究主要集中在Matlab仿真研究方面,无法直接推广为实际应用。为此,采用基于LabVIEW的动态神经网络非线性自适应逆控制方法,首先在LabVIEW中建立动态神经网络结构及在线学习算法,并依此建立非线性对象的辨识器和逆控制器等模型;然后构建完整的非线性对象自适应逆控制系统,并在LabVIEW环境中通过仿真验证了系统性能。通过配置相应的数据采集设备,该系统可以直接推广为实际应用。     

Mesh网中高效无死锁自适应路由算法

提出了一种新的应用于三维Mesh网中的无死锁路由算法.在当今的商用多计算机系统中,二维和三维的Mesh网是多处理器网络最为常用的拓扑结构之一.在应用于Mesh网的平面自适应路由(Planar Adaptive Routing)算法中,每条物理通道只需三条虚拟通道就可以有效地在三维以及更高维的Mesh网中避免死锁的产生.然而,采用该算法,网络拓扑一维和三维分别有两条和一条虚拟通道始终处于空闲状态.该文所提出的算法针对三维Mesh网,每条物理通道只需两条虚拟通道就可以有效地避免死锁.文中通过充分的模拟数据验证了此算法的有效性.     

Velocity Estimation for Robot Manipulators Using Neural Network

In robot manipulators, optical incremental encoders are widely used as the transducers to monitor joint position and velocity information. With incremental encoder, positional information is determined as discrete data relative to a reference (home) position. However, velocity information can only be deduced by processing the position data. In this paper, a method of using a neural network to estimate the velocity information of robotic joint from discrete position versus time data is proposed and evaluated. The architecture of the neural net and the training methodology are presented and discussed.This approach is then applied to estimate the joint velocity of a SCARA robot while performing an electronic component assembly task. Based on computer simulations, comparison of the accuracy of the neural network estimator with two other well established velocity estimation algorithms are made. The neural net approach can maintain good performance even in the presence of measurement noises.     

基于神经网络的非线性多模型自适应控制

针对一类非线性离散动态系统,设计了一个自适应控制方案。为了保证在任意时刻均能为被控的动态系统选择最好的控制器,方案基于输入输出数据为系统定义一个线性预测模型,并在此基础上设计能够保证闭环系统所有信号有界的线性鲁棒自适应控制器,同时定义一个非线性预测模型,再基于径向基神经网络设计一个旨在提高系统控制性能的非线性自适应控制器。通过比较2个控制器预测的系统输出性能,设计合理的开关切换规则。控制方案能将系统稳定性控制和性能优化的控制分离并单独实现,使得系统能在保证稳定性前提下,借助神经网络控制器良好的追踪能力有效提高自适应控制效果。最后通过仿真例子说明了系统稳定和提高输出追踪效果可以同时得到保证。     

基于神经网络的一类非线性系统自适应H∞控制

基于神经网络提出一种自适应Ｈ∞控制方法。控制器由等效控制器和Ｈ∞控制器两部分组成,用神经网络逼近未知非线性函数,Ｈ∞控制器用于减弱外部及神经网络逼近误差对跟踪误差的影响。所设计的控制器不仅保证了闭环控制系统的稳定性,而且使外部干扰及神经网络逼近误差对跟踪误差的影响减小到预定的性能指标。