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Motion posture control for power cable maintenance robot in typical operation conditions

机译:电力电缆维护机器人的运动姿势控制在典型操作条件下

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Purpose In the extreme power environment of flexible transmission line, wind load, high voltage and strong electromagnetic interference, the motion performance of the robot manipulator is strongly affected by the extreme environment. Therefore, this study aims to improve the manipulator motion control performance of power cable maintenance robot and effectively reduce the influence of specific operation environment on the robot manipulator motion posture. Design/methodology/approach The mathematical model under three typical operation conditions, namely, flexible line, wind load and strong electromagnetic field have been established, correspondingly the mapping relationship between different environment parameters and robot operation conditions are also given. Based on the nonlinear approximation feature of neural network, a back propagation (BP) neural network is adopted to solve the posture control problems. The power cable line sag, robot tile angle caused by wind load and spatial field strength are the input signals of the BP network in the robot motion posture control method. Findings Through the training and learning of the BP network, the output control variables are used to compensate the actual robot operation posture. The simulation experiment verifies the effectiveness of the proposed algorithm, and compared with the conventional proportional integral differential (PID) control, the method has high real-time performance and sound stability. Finally, field operation experiments further validate the engineering feasibility of the control method, and at the same time, the proposed control method has the remarkable characteristics of sound universality, adaptability and easy expansion. Originality/value A multi-layer control architecture which is suitable for smart grid platform maintenance is proposed and a robot system platform for network operation and maintenance management is constructed. The human-machine-environment coordination and integration mode and intelligent power system management platform can be realized which greatly improves the intelligence of power system management. Mathematical models of the robot under three typical operation conditions of flexible wire wind load and strong electromagnetic field are established and the mapping relationship between different environmental parameters and the robot operation conditions is given. Through the non-linear approximation characteristics of BP network, the control variables of the robot joints can be obtained and the influence of extreme environment on the robot posture can be compensated. The simulation results of MATLAB show that the control algorithm can effectively restrain the influence of uncertain factors such as flexible environment, wind load and strong electromagnetic field on the robot posture. It satisfied the design requirements of fast response, high tracking accuracy and good stability of the control system. Field operation tests further verify the engineering practicability of the algorithm.
机译:目的在极端动力环境中的柔性传输线,风力负荷,高电压和强电磁干扰,机器人机械手的运动性能受到极端环境的强烈影响。因此,本研究旨在改善电力电缆维护机器人的操纵器运动控制性能,并有效地降低特定操作环境对机器人操纵器运动姿势的影响。设计/方法/接近三个典型操作条件下的数学模型,即柔性线,风力荷载和强电磁场,相应地还给出了不同环境参数和机器人操作条件之间的映射关系。基于神经网络的非线性逼近特征,采用反向传播(BP)神经网络来解决姿势控制问题。电力电缆线凹槽,由风力负荷和空间场强引起的机器人瓦片角是机器人运动姿势控制方法中BP网络的输入信号。通过培训和学习BP网络的调查结果,输出控制变量用于补偿实际的机器人操作姿势。仿真实验验证了所提出的算法的有效性,与传统的比例积分差分(PID)控制相比,该方法具有高实时性能和声音稳定性。最后,现场操作实验进一步验证了控制方法的工程可行性,同时,所提出的控制方法具有卓越的声音普遍性特性,适应性和易扩散。原创性/值建议适用于智能电网平台维护的多层控制架构,并构建了用于网络操作和维护管理的机器人系统平台。可以实现人机环境协调和集成模式和智能电力系统管理平台,这大大提高了电力系统管理的智能。建立了柔性线风负荷和强电磁场三个典型操作条件下机器人的数学模型,并给出了不同环境参数与机器人操作条件的映射关系。通过BP网络的非线性近似特性,可以获得机器人关节的控制变量,并且可以补偿极端环境对机器人姿势的影响。 MATLAB的仿真结果表明,控制算法可以有效地抑制柔性环境,风力荷载和强电磁场等不确定因素对机器人姿势的影响。它满足了快速响应,高跟踪精度和控制系统稳定性的设计要求。现场操作测试进一步验证算法的工程实用性。

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