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Int. J. Robotics Autom.最新文献

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A New Adaptive Neuro-Fuzzy Controller for Trajectory Tracking of Robot Manipulators 一种新的自适应神经模糊机器人轨迹跟踪控制器
Pub Date : 1900-01-01 DOI: 10.2316/Journal.206.2011.1.206-3401
D. C. Theodoridis, Y. Boutalis, M. Christodoulou
In this paper, an adaptive control method for trajectory tracking of robot manipulators, based on new neuro-fuzzy modelling is presented. The proposed control scheme uses a three-layer neural fuzzy network (NFN) to estimate system uncertainties. The function of robot system dynamics is first modelled by a fuzzy system, which in the sequel is approximated by a combination of high order neural networks (HONNs). The overall representation is linear in respect to the unknown NN weights leading to weight adaptation laws that ensure stability and convergence to unique global minimum of the error functional. Due to the adaptive neurofuzzy modelling, the proposed controller is independent of robot dynamics, since the free parameters of the neuro-fuzzy controller are adaptively updated to cope with changes in the system and the environment. Adaptation laws for the network parameters are derived, which ensure network convergence and stable control. A weight hopping technique is also introduced to ensure that the estimated weights stay within pre-specified bounds. The simulation results show very good approximation performance of the proposed representation as compared with a simple NN approximator and very good tracking abilities under disturbance torque compared to conventional computed torque PD control.
提出了一种基于神经模糊模型的机器人轨迹跟踪自适应控制方法。该控制方案采用三层神经模糊网络(NFN)来估计系统的不确定性。首先用模糊系统对机器人系统动力学函数进行建模,然后用高阶神经网络(honn)的组合对其进行逼近。对于未知的NN权值,总体表示是线性的,从而产生了权值自适应律,确保了稳定性和收敛到唯一的误差函数的全局最小值。由于神经模糊自适应建模,该控制器不受机器人动力学的影响,因为神经模糊控制器的自由参数可以自适应地更新以应对系统和环境的变化。推导了网络参数的自适应规律,保证了网络的收敛性和控制的稳定性。还引入了一种权重跳跃技术,以确保估计的权重保持在预先指定的范围内。仿真结果表明,与简单的神经网络近似器相比,该方法具有良好的逼近性能;与传统的计算转矩PD控制相比,该方法在扰动转矩下具有良好的跟踪能力。
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引用次数: 20
Saliency-based robust Features for Global Visual servoing 基于显著性的全局视觉伺服鲁棒特性
Pub Date : 1900-01-01 DOI: 10.2316/Journal.206.2016.5.206-4619
H. Abidi, M. Chtourou, Khaled Kaaniche, Hassen Mekki
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引用次数: 2
Fpga Embedded PD control of a 1 DOF manipulator with a pneumatic actuator 带气动执行器的1自由度机械手的Fpga嵌入式PD控制
Pub Date : 1900-01-01 DOI: 10.2316/Journal.206.2016.3.206-4362
Juan Manuel Ramos Arreguín, S. Tovar-Arriaga, J. E. Soto, M. Aceves-Fernández
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引用次数: 3
Intelligent Robotic Path Finding Methodologies with Fuzzy/Crisp Entropies and Learning 基于模糊/脆熵和学习的智能机器人寻径方法
Pub Date : 1900-01-01 DOI: 10.2316/Journal.206.2011.3.206-3511
C. Son
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引用次数: 9
Bend angle and contact force on Soft pneumatic gripper for grasping cylindrical-shaped Different-Sized Objects 柔性气动夹持器抓取圆柱形状不同尺寸物体的弯曲角和接触力
Pub Date : 1900-01-01 DOI: 10.2316/j.2021.206-0622
Venkatesan Vellaiyan, Shanmugam Subramaniam, Veerappan Arunachalam
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引用次数: 2
Optimization Algorithms for Node Allocation in Vision Guided Multi-robot Pattern formation 视觉引导多机器人模式形成中节点分配的优化算法
Pub Date : 1900-01-01 DOI: 10.2316/J.2021.206-0438
R. Senthilnathan, G. Vignesh, Arjun Venugopal, Selvakumaran Anish Kanna, Sundaramoorthy Sidhanathan
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引用次数: 0
Autonomous Visual servoing of a robot manipulator using Reinforcement Learning 基于强化学习的机械臂自主视觉伺服
Pub Date : 1900-01-01 DOI: 10.2316/Journal.206.2016.1.206-4235
M. Sadeghzadeh, D. Calvert, H. Abdullah
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引用次数: 1
Obstacle Recognition for Intelligent Vehicle based on radar and Vision fusion 基于雷达与视觉融合的智能车辆障碍物识别
Pub Date : 1900-01-01 DOI: 10.2316/J.2021.206-0478
Zhenhua Pan, Kewei Li, Hongbin Deng, Yiran Wei
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引用次数: 1
A Novel Segmentation method based on Grayscale wave for underwater Images 基于灰度波的水下图像分割新方法
Pub Date : 1900-01-01 DOI: 10.2316/Journal.206.2018.4.206-5095
Mingzhong Yan, Bin-Bin Huang, Daqi Zhu, Simon X. Yang
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引用次数: 0
Detection and Repair of Coverage Holes in Mobile Sensor Networks using sub-Voronoi cells 基于子voronoi单元的移动传感器网络覆盖孔检测与修复
Pub Date : 1900-01-01 DOI: 10.2316/Journal.206.2018.6.206-5092
Lixia Deng, Xin Ma, J. Gu, Yibin Li
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引用次数: 2
期刊
Int. J. Robotics Autom.
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