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Real-time Collision-free Path Planning of Robot Manipulators using Neural Network Approaches 总被引:4,自引:0,他引:4
In this paper, a novel neural network approach to real-time collision-free path planning of robot manipulators in a nonstationary environment is proposed, which is based on a biologically inspired neural network model for dynamic trajectory generation of a point mobile robot. The state space of the proposed neural network is the joint space of the robot manipulators, where the dynamics of each neuron is characterized by a shunting equation or an additive equation. The real-time robot path is planned through the varying neural activity landscape that represents the dynamic environment. The proposed model for robot path planning with safety consideration is capable of planning a real-time comfortable path without suffering from the too close nor too far problems. The model algorithm is computationally efficient. The computational complexity is linearly dependent on the neural network size. The effectiveness and efficiency are demonstrated through simulation studies. 相似文献
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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. 相似文献
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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. 相似文献
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基于T-S型模糊神经网络的轮式机器人避障方法研究 总被引:1,自引:0,他引:1
针对超声波传感器产生的不确定信息,研究了一种基于Takagi-Sugeno(T-S)模型的模糊神经网络信息融合避障方法;对超声波传感器所获得的数据进行融合,建立控制器输入信号和机器人速度输出之间的模式映射关系;在MATLAB环境下对模糊神经网络避障算法进行了仿真,最后在实际环境中进行避障实验;实验结果表明,该算法具有较好的准确性和鲁棒性,能够适用于移动机器人的导航需要. 相似文献
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直接自适应动态递归模糊神经网络控制及其应用 总被引:1,自引:0,他引:1
针对某些仿射非线性系统中各状态变量间呈微分关系的特点,本文提出仅取某些可测状态变量
作为动态递归模糊神经网络(dynamic recurrent fuzzy neural network, DRFNN) 的输入,而由DRFNN 的反馈矩阵
描述系统内部动态关系的直接自适应DRFNN 控制算法,克服了将系统所有变量作为输入的传统模糊神经网
络(traditioanl fuzzy neural network, TFNN) 因某些不可测状态变量所导致的不可实现问题.在电液伺服系统中的
应用结果表明:直接自适应DRFNN 控制算法相对于TFNN 控制算法对系统稳态特性的改善具有较大的优越
性. 相似文献
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给出了一种电机驱动机器手中非线性机电模型的模糊鲁棒闭环控制系统,此控制系统可处理非结构环境下的三个主要的智能机器人导航问题:自动化规划、快速连续导航中的避障、处理结构和(或)非结构不确定性。 相似文献
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In this paper a controller based on neural networks is proposed toachieve output trajectory tracking of rigid robot manipulators. Neuralnetworks used here are one hidden layer ones so that their outputs dependlinearly on the parameters. Our method uses a decomposed connectioniststructure. Each neural network approximate a separate element of thedynamical model. These approximations are used to perform an adaptive stablecontrol law. The controller is based on direct adaptive techniques and theLyapunov approach is used to derive the adaptation laws of the netsparameters. By using an intrinsic physical property of the manipulator, thesystem is proved to be stable. The performance of the controller depends onthe quality of the approximation, i.e. on the inherent reconstruction errorsof the exact functions. 相似文献
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针对并联机器人数学模型不完全确知并包含外部扰动的非线性多变量系统,提出一种基于模糊神经网络运算法则(FNNA)的自适应控制策略。将各个支链的模糊规则通过神经网络进行在线训练并得出模糊规则的权重并将此运用于在线辨识非线性自适应控制系统的未知动态,有效抑制了系统的数学模型不精确所产生的误差及外部扰动。仿真结果表明该控制方法明显提高了控制系统的轨迹跟踪性能,并对外部干扰及系统的非线性具有很强的鲁棒性。 相似文献
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采用高斯函数作为模糊隶属函数,将模糊控制与神经网络相结合。利用神经网络实现模糊推理,运用了一种模糊高斯基函数神经网络,并用于两关节机器人的轨迹跟踪控制。仿真结果表明,该网络对机器人轨迹跟踪控制具有很好的效果,是一种行之有效的控制方法。 相似文献
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This paper shows the results obtained in controlling a mobile robot by means of local recurrent neural networks based on a radial basis function (RBF) type architecture. The model used has a Finite Impulse Response (FIR) filter feeding back each neuron's output to its own input, while using another FIR filter as a synaptic connection. The network parameters (coefficients of both filters) are adjusted by means of the gradient descent technique, thus obtaining the stability conditions of the process. As a practical application the system has been successfully used for controlling a wheelchair, using an architecture made up by a neurocontroller and a neuroidentifier. The role of the latter, connected up in parallel with the wheelchair, is to propagate the control error to the neurocontroller, thus cutting down the control error in each working cycle. 相似文献
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基于神经网络的机器人轨迹跟踪控制 总被引:2,自引:1,他引:2
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。 相似文献
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Industrial Robot Navigation and Obstacle Avoidance Employing Fuzzy Logic 总被引:10,自引:0,他引:10
This paper proposes a novel conceptual approach based on fuzzy logic to solve the local navigation and obstacle avoidance problem for industrial 3-dof robotic manipulators. The proposed system is divided into separate fuzzy units, which control individually each manipulator link. The fuzzy rule-base of each unit combines a repelling influence, which is related to the distance between the manipulator and the nearby obstacles, with the attracting influence produced by the angular difference, between the actual and the final manipulator configuration, to generate a new actuating command for each link. It can be considered as an on-line local navigation method for the generation of instantaneous collision-free trajectories. The strategy has been successfully applied to manipulators in different simulated workspace environments providing collision-free paths. Some of the simulation results obtained are included. 相似文献
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This paper presents a new frequency-dependent direct adaptive scheme for the optimal and/or suboptimal tracking of the motion of a prescribed model. The idea is based on a closed-loop control scheme in which an
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H
optimal/suboptimal controller is applied in parallel with a direct adaptive technique to guide a robot manipulator to follow a certain prescribed trajectory. The H
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compensators have to be proper and positively bounded with respect to all dynamic frequencies. Robustness issues are addresses in the paper by lumping all the nonlinear dynamic terms such as the centrifugal and Coriolis effects as well as the mechanical and electrical friction forces of the robot arm, into a general unstructured uncertainty term. 相似文献
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提出一种基于动态递归神经网络的自适应PID控制方案,该控制系统由神经网络辨识器和神经网络控制器组成。辨识器采用单隐层的动态递归神经网络,网络结构为2-4-1;辨识算法为动态BP算法;控制器采用两层线性结构的神经网络,输入为系统偏差及其一阶、二阶微分,因此具有增量型PID控制结构。应用该控制系统对一非线性时变系统进行仿真研究,仿真结果表明该控制方案不仅具有良好的跟踪特性,而且对系统参数变化具有较强的鲁棒性。 相似文献
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This paper presents a pattern discrimination method for electromyogram (EMG) signals for application in the field of prosthetic control. The method uses a novel recurrent neural network based on the hidden Markov model. This network includes recurrent connections, which enable modeling time series, such as EMG signals. Weight coefficients in the network can be learned using a well-known back-propagation through time algorithm. Pattern discrimination experiments were conducted to demonstrate the feasibility and performance of the proposed method. We were able to successfully discriminate forearm motions using the EMG signals, and achieved considerably high discrimination performance compared with other discrimination methods. 相似文献
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This paper is concerned with PID position regulation of robot manipulators actuated by permanent magnet synchronous motors (PMSMs). We present a global asymptotic stability proof when the electric dynamics of these actuators is taken into account. Our controller is so simple that it differs from standard field oriented control (SFOC) of PMSMs in only three simple nonlinear terms that have to be added and a nonlinear PID controller which is used instead of a classical PID controller. Thus, our proposal represents the closest result to SFOC of PMSMs provided with a formal global asymptotic stability proof. We present an advancement, if modest, towards presenting a global stability proof for SFOC when used in robotics. 相似文献