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Intelligent control of overhead gantry cranes

机译:高架龙门起重机的智能控制

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Overhead gantry crane systems have been integral part of many heavy industrial applications needing material handling and transportation. This can easily be seen in emerging countries growing rapidly. Single force control input applied to crane moves it to a desired position but also triggers undesired swing on the payload which makes it an interesting control problem for this underactuated system. Given an application and a work environment constraint the response time to the position error vs. the maximum allowable amplitude of the payload swing may vary. An intelligent control methodology has been given such an overhead crane system with an optimization parameter compromising between response rate and swing amplitude. The proposed controller does not rely on the knowledge of the crane dynamics making the solution applicable changing operation conditions, so it is a universal controller. Robust adaptive properties are established via a neural network based multi-loop controller architecture. Customization to fit two specific application requirements has been illustrated in the simulations. Stability proofs are given in the sense of Lyapunov. Simulation examples are shown to illustrate the effectiveness of the proposed controller that does not rely on prior knowledge of system dynamics.
机译:高架龙门起重机系统已成为许多需要物料搬运和运输的重工业应用的组成部分。在迅速增长的新兴国家中很容易看出这一点。施加在起重机上的单力控制输入将其移动到所需位置,但也会触发有效载荷上的意外摆动,这使该欠驱动系统成为一个有趣的控制问题。在给定应用和工作环境约束的情况下,对位置误差的响应时间与有效载荷摆幅的最大允许幅度之间的关系可能会有所不同。已经给出了一种智能控制方法,这种桥式起重机系统具有在响应率和摆动幅度之间折衷的优化参数。所提出的控制器不依赖于起重机动力学的知识,因此该解决方案适用于不断变化的运行条件,因此它是通用控制器。通过基于神经网络的多回路控制器架构来建立鲁棒的自适应特性。仿真中显示了满足两个特定应用程序要求的定制。 Lyapunov给出了稳定性证明。显示了仿真示例,以说明所提出的控制器的有效性,该控制器不依赖于系统动力学的现有知识。

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