在当前的电气应用中,变频器控制系统应用广泛,但面临的挑战也愈发明显。特别是在能耗管理方面,由于其缺乏智能调控频段能耗的能力,系统整体能耗偏高。为此,文章提出基于自适应比例-积分-微分(Proportional Integral Derivative,PID)算...在当前的电气应用中,变频器控制系统应用广泛,但面临的挑战也愈发明显。特别是在能耗管理方面,由于其缺乏智能调控频段能耗的能力,系统整体能耗偏高。为此,文章提出基于自适应比例-积分-微分(Proportional Integral Derivative,PID)算法的变频器节能控制系统设计。构建以微处理器为核心的变频器节能控制结构,将神经网络与PID控制器相结合,构造自适应PID控制器。结合变频器节能控制结构的能耗计算与反馈,通过自适应调节权值系数完成变频系数调整,降低各频段能耗,实现变频器节能控制研究。实验结果显示,该系统节能效果显著,能耗最高仅为20 J,且相较于对比文献,该系统运行稳定,运行时间短,为变频器节能控制运行提供了保障。展开更多
A closed-chain robot has several advantages over an open-chain robot, such as high mechanical rigidity, high payload, high precision. Accurate trajectory control of a robot is essential in practical-use. This paper pr...A closed-chain robot has several advantages over an open-chain robot, such as high mechanical rigidity, high payload, high precision. Accurate trajectory control of a robot is essential in practical-use. This paper presents an adaptive proportional integral differential （PID） control algorithm based on radial basis function （RBF） neural network for trajectory tracking of a two-degree-of-freedom （2-DOF） closed-chain robot. In this scheme, an RBF neural network is used to approximate the unknown nonlinear dynamics of the robot, at the same time, the PID parameters can be adjusted online and the high precision can be obtained. Simulation results show that the control algorithm accurately tracks a 2-DOF closed-chain robot trajectories. The results also indicate that the system robustness and tracking performance are superior to the classic PID method.展开更多
提出了一种改进Smith预估器的模糊自适应PID显微视觉伺服控制策略.该策略用于改善视觉伺服系统的控制品质.针对视觉迟延问题,建立了视觉伺服系统的分时模型.在对基于位置的动态"look and move"视觉伺服系统特性分析的基础上,...提出了一种改进Smith预估器的模糊自适应PID显微视觉伺服控制策略.该策略用于改善视觉伺服系统的控制品质.针对视觉迟延问题,建立了视觉伺服系统的分时模型.在对基于位置的动态"look and move"视觉伺服系统特性分析的基础上,建立了基于改进的Smith预估器的模糊自适应PID控制的视觉伺服系统结构.在微操作机器人平台上进行了微小零件的自动定位与抓取实验.实验与仿真结果表明,提出的视觉控制结构的伺服系统具有很好的控制品质,满足微操作应用要求.展开更多
文摘在当前的电气应用中,变频器控制系统应用广泛,但面临的挑战也愈发明显。特别是在能耗管理方面,由于其缺乏智能调控频段能耗的能力,系统整体能耗偏高。为此,文章提出基于自适应比例-积分-微分(Proportional Integral Derivative,PID)算法的变频器节能控制系统设计。构建以微处理器为核心的变频器节能控制结构,将神经网络与PID控制器相结合,构造自适应PID控制器。结合变频器节能控制结构的能耗计算与反馈,通过自适应调节权值系数完成变频系数调整,降低各频段能耗,实现变频器节能控制研究。实验结果显示,该系统节能效果显著,能耗最高仅为20 J,且相较于对比文献,该系统运行稳定,运行时间短,为变频器节能控制运行提供了保障。
基金Project supported bY the National Natural Science Foundation of China （Grant No.50375085）, and the Natural Science Foundation of Shandong Province （Grant No.Y2002F13）
文摘A closed-chain robot has several advantages over an open-chain robot, such as high mechanical rigidity, high payload, high precision. Accurate trajectory control of a robot is essential in practical-use. This paper presents an adaptive proportional integral differential （PID） control algorithm based on radial basis function （RBF） neural network for trajectory tracking of a two-degree-of-freedom （2-DOF） closed-chain robot. In this scheme, an RBF neural network is used to approximate the unknown nonlinear dynamics of the robot, at the same time, the PID parameters can be adjusted online and the high precision can be obtained. Simulation results show that the control algorithm accurately tracks a 2-DOF closed-chain robot trajectories. The results also indicate that the system robustness and tracking performance are superior to the classic PID method.
文摘提出了一种改进Smith预估器的模糊自适应PID显微视觉伺服控制策略.该策略用于改善视觉伺服系统的控制品质.针对视觉迟延问题,建立了视觉伺服系统的分时模型.在对基于位置的动态"look and move"视觉伺服系统特性分析的基础上,建立了基于改进的Smith预估器的模糊自适应PID控制的视觉伺服系统结构.在微操作机器人平台上进行了微小零件的自动定位与抓取实验.实验与仿真结果表明,提出的视觉控制结构的伺服系统具有很好的控制品质,满足微操作应用要求.
