共查询到20条相似文献,搜索用时 15 毫秒
1.
Nowadays, gas welding applications on vehicle’s parts with robot manipulators have increased in automobile industry. Therefore, the speed of end-effectors of robot manipulator is affected on each joint during the welding process with complex trajectory. For that reason, it is necessary to analyze the noise and vibration of robot’s joints for predicting faults. This paper presents an experimental investigation on a robot manipulator, using neural network for analyzing the vibration condition on joints. Firstly, robot manipulator’s joints are tested with prescribed of trajectory end-effectors for the different joints speeds. Furthermore, noise and vibration of each joint are measured. And then, the related parameters are tested with neural network predictor to predict servicing period. In order to find robust and adaptive neural network structure, two types of neural predictors are employed in this investigation. The results of two approaches improved that an RBNN type can be employed to predict the vibrations on industrial robots. 相似文献
2.
3.
Masayoshi Tabuse Tetsuro Kitazoe Tatsuro Shinchi Akinobu Todaka 《Artificial Life and Robotics》2002,6(1-2):52-58
This article describes a new approach for control systems for an autonomous mobile robot by using sandwiches of two different
types of neural network. One is a neural network with competition and cooperation, and is used for recognizing sensor information
where synaptic coupling are fixed. The second is a neural network with adaptive synaptic couplings corresponding to a genotype
in a creature, and used for self-learning for the wheel controls. In a computer simulation model, we were successful in obtaining
four types of robot with good performance when going along a wall. The model also showed robustness in a real environment.
This work was presented, in part, at the Sixth International Symposium on Artificial Life and Robotics, Tokyo Japan, January
15–17, 2001 相似文献
4.
魏佩敏 《计算机工程与应用》2007,43(7):208-210,214
提出一种神经网络与PD并行控制的机器人学习控制系统。为了加快神经网络的学习算法,在数字复合正交神经网络的基础上给出一种模拟复合正交神经网络的学习算法,以两关节机器人为对象仿真结果表明,该控制方法使机器人跟踪期望轨迹,其系统响应、跟踪精度和鲁棒性优于常规的控制方法,位置跟踪获得了满意的控制效果。该模拟神经控制器为不确定系统的控制提供了一种新的途径。 相似文献
5.
In this paper, an adaptive neural network control scheme for robot manipulators with actuator nonlinearities is presented. The control scheme consists of an adaptive neural network controller and an actuator nonlinearities compensator. Since the actuator nonlinearities are usually included in the robot driving motor, a compensator using radial basis function (RBF) network is proposed to estimate the actuator nonlinearities and eliminate their effects. Subsequently, an adaptive neural network controller that neither requires the evaluation of inverse dynamical model nor the time-consuming training process is given. In addition, GL matrix and its product operator are introduced to help prove the stability of the closed control system. Considering the adaptive neural network controller and the RBF network compensator as the whole control scheme, the closed-loop system is proved to be uniformly ultimately bounded (UUB). The whole scheme provides a general procedure to control the robot manipulators with actuator nonlinearities. Simulation results verify the effectiveness of the designed scheme and the theoretical discussion. 相似文献
6.
针对机械臂运动轨迹控制中存在的跟踪精度不高的问题,采用了一种基于EC-RBF神经网络的模型参考自适应控制方案对机械臂进行模型辨识与轨迹跟踪控制。该方案采用了两个RBF神经网络,运用EC-RBF学习算法,采用离线与在线相结合的方法来训练神经网络,一个用来实现对机械臂进行模型辨识,一个用来实现对机械臂轨迹跟踪控制。对二自由度机械臂进行仿真,结果表明,使用该控制方案对机械臂进行轨迹跟踪控制具有较高的控制精度,且因采用EC-RBF学习算法使网络具有更快的训练速度,从而使得控制过程较迅速。 相似文献
7.
8.
In this article, an adaptive neural controller is developed for cooperative multiple robot manipulator system carrying and manipulating a common rigid object. In coordinated manipulation of a single object using multiple robot manipulators simultaneous control of the object motion and the internal force exerted by manipulators on the object is required. Firstly, an integrated dynamic model of the manipulators and the object is derived in terms of object position and orientation as the states of the derived model. Based on this model, a controller is proposed that achieves required trajectory tracking of the object as well as tracking of the desired internal forces arising in the system. A feedforward neural network is employed to learn the unknown dynamics of robot manipulators and the object. It is shown that the neural network can cope with the unknown nonlinearities through the adaptive learning process and requires no preliminary offline learning. The adaptive learning algorithm is derived from Lyapunov stability analysis so that both error convergence and tracking stability are guaranteed in the closed loop system. Finally, simulation studies and analysis are carried out for two three-link planar manipulators moving a circular disc on specified trajectory. 相似文献
9.
Many map-building algorithms using ultrasonic sensors have been developed for mobile robot applications. In indoor environments,
the ultrasonic sensor system gives some uncertain data. To compensate for this effect, a new feature extraction method using
neural networks is proposed. A new, effective representation of the target is defined, and the reflection wave data patterns
are learnt using neural networks. As a consequence, the targets are classified as planes, corners, or edges, which all frequently
occur in indoor environments. We constructed our own robot system for the experiments which were carried out to show the performance.
This work was presented in part at the 7th International Symposium on Artificial Life and Robotics, Oita, Japan, January 16–18,
2002 相似文献
10.
A methodology for the artificial realization of expert human skill is described. Artificial human skill was realized in the
problem of contour control of mechatronic servo systems including robot manipulators and machine tools. The merits of the
artificial human skill thus obtained are discussed.
This work was presented, in part, at the International Symposium on Artificial Life and Robotics, Oita, Japan, February 18–20,
1996. 相似文献
11.
针对同一噪声源的多传感信号,采用自适应模糊神经网络系统(AFNNS)设计自适应噪声抵消器.采用AFNNS获取多路信息融合的权系数和自适应噪声抵消器的系数,基于AFNNS的自适应噪声抵消器不仅能获取信号的最佳估计,并且能克服模型和噪声存在的不确定性和不完备性.仿真结果表明,该自适应噪声抵消器的设计方法简单易行,去噪声效果优于基于平均法的去噪效果. 相似文献
12.
We propose a neural network model generating a robot arm trajectory. The developed neural network model is based on a recurrent-type neural network (RNN) model calculating the proper arm trajectory based on data acquired by evaluation functions of human operations as the training data. A self-learning function has been added to the RNN model. The proposed method is applied to a 2-DOF robot arm, and laboratory experiments were executed to show the effectiveness of the proposed method. Through experiments, it is verified that the proposed model can reproduce the arm trajectory generated by a human. Further, the trajectory of a robot arm is successfully modified to avoid collisions with obstacles by a self-learning function.This work was presented, in part, at the 9th International Symposium on Artificial Life and Robotics, Oita, Japan, January 28–30, 2004 相似文献
13.
Fuzzy sliding mode control for a robot manipulator 总被引:1,自引:0,他引:1
This work presents the design of a robust control system using a sliding mode controller that incorporates a fuzzy control
scheme. The presented control law superposes a sliding mode controller and a fuzzy logic controller. A fuzzy tuning scheme
is employed to improve the performance of the control system. The proposed fuzzy sliding mode control (FSMC) scheme utilizes
the complementary cooperation of the traditional sliding mode control (SMC) and the fuzzy logic control (FLC). In other words,
the proposed control scheme has the advantages which it can guarantee the stability in the sense of Lyapunov function theory
and can ameliorate the tracking errors, compared with the FLC and SMC, respectively. Simulation results for the trajectory
tracking control of a two-link robot manipulator are presented to show the feasibility and robustness of the proposed control
scheme.
This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January
31–February 2, 2008 相似文献
14.
Meng-Bi Cheng Wu-Chung Su Ching-Chih Tsai 《International journal of systems science》2013,44(3):408-425
This article presents a robust tracking controller for an uncertain mobile manipulator system. A rigid robotic arm is mounted on a wheeled mobile platform whose motion is subject to nonholonomic constraints. The sliding mode control (SMC) method is associated with the fuzzy neural network (FNN) to constitute a robust control scheme to cope with three types of system uncertainties; namely, external disturbances, modelling errors, and strong couplings in between the mobile platform and the onboard arm subsystems. All parameter adjustment rules for the proposed controller are derived from the Lyapunov theory such that the tracking error dynamics and the FNN weighting updates are ensured to be stable with uniform ultimate boundedness (UUB). 相似文献
15.
Kei Sugihara Masayoshi Tabuse Tatsuro Shinchi Tetsuro Kitazoe 《Artificial Life and Robotics》2001,5(3):159-164
This article describes a new approach to control systems for a mobile robot Khepera by using a neural network with competition
and cooperation as the processing unit for the robot sensors. Competition makes only one neuron active, while cooperation
keeps them all active. In our research, we find that the Khepera controlled by this neural network can maintain a smoother
trajectory than when it is controlled by the output values of its own sensors, especially in noisy environments.
This work was presented in part at the Fifth International Symposium on Artificial Life and Robotics, Oita, Japan, January
26–28, 2000 相似文献
16.
This note points out that the proof of Theorem 3 in Kim, Lewis, and Dawson (Automatica 36(9) 1355) has a mistake. Additionally, it is presented as the correction of the theorem. 相似文献
17.
Mathematical essence and structures of the feedforward neural networks are investigated in this paper. The interpolation mechanisms of the feedforward neural networks are explored. For example, the well-known result, namely, that a neural network is an universal approximator, can be concluded naturally from the interpolative representations. Finally, the learning algorithms of the feedforward neural networks are discussed. 相似文献
18.
Hidenori Kawamura Hiroyuki Iizuka Toshihiko Takaya Azuma Ohuchi 《Artificial Life and Robotics》2009,13(2):504-507
We report on the cooperative control of multiple neural networks for an indoor blimp robot. In our research group, the indoor
blimp robot has been studied to achieve various flying robot applications. The objective of this article is to propose a robust
controller that can adapt to mechanical accidents such as the breakdown of propellers. In our proposed method, each propeller
thrust is independently calculated by a small neural network. We confirm the advantage of the proposed method against the
control by a single large neural network.
This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January
31–February 2, 2008 相似文献
19.
Jun Xia Yujia Zhang Min Wang Andy Annamalai 《International journal of systems science》2019,50(3):638-651
Conventional Neural Network (NN) control for robots uses radial basis function (RBF) and for n-link robot with online control, the number of nodes and weighting matrix increases exponentially, which requires a number of calculations to be performed within a very short duration of time. This consumes a large amount of computational memory and may subsequently result in system failure. To avoid this problem, this paper proposes an innovative NN robot control using a dimension compressed RBF (DCRBF) for a class of n-degree of freedom (DOF) robot with full-state constraints. The proposed DCRBF NN control scheme can compress the nodes and weighting matrix greatly and provide an output that meets the prescribed tracking performance. Additionally, adaption laws are designed to compensate for the internal and external uncertainties. Finally, the effectiveness of the proposed method has been verified by simulations. The results indicate that the proposed method, integral Barrier Lyapunov Functions (iBLF), avoids the existing defects of Barrier Lyapunov Functions (BLF) and prevents the constraint violations. 相似文献
20.
Manuel A. Duarte-Mermoud Alejandro M. Suárez Danilo F. Bassi 《Neural computing & applications》2006,15(1):18-25
The behavior of a multivariable predictive control scheme based on neural networks applied to a model of a nonlinear multivariable real process, consisting of a pressurized tank is investigated in this paper. The neural scheme consists of three neural networks; the first is meant for the identification of plant parameters (identifier), the second one is for the prediction of future control errors (predictor) and the third one, based on the two previous, compute the control input to be applied to the plant (controller). The weights of the neural networks are updated on-line, using standard and dynamic backpropagation. The model of the nonlinear process is driven to an operation point and it is then controlled with the proposed neural control scheme, analyzing the maximum range over the neural control works properly. 相似文献