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Application of intelligent sliding mode control with moving sliding surface for overhead cranes

机译:移动面滑动智能控制技术在桥式起重机中的应用

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In this paper, neural-based fuzzy logic sliding mode control with moving sliding surface has been designed for supervision of an overhead crane. A mathematical model has been established of the crane, and equations of motion have been obtained. First, the suitable sliding surface coefficient has been determined for the fixed sliding surface in the design of sliding mode control. The sliding surface has been moved by using neural-based fuzzy logic algorithm to eliminate disadvantage of the regular sliding mode control. By application of this control algorithm, the control performance incredibly increased. In the application, during the carriage of the load to a target which was 1 m away by a crane with 3 kg of load and 100 cm of rope length, the parameters of effected controllers were updated and their training was realized. In order to display the insensitiveness of the controller to parametric uncertainty, the value of the load was taken as 8 kg and the length of the rope was taken as 3 m and controls for a different target were realized. MATLAB program was used for numerical solutions, and results were examined graphically. Obtained results displayed the success of the algorithm of neural-based fuzzy logic sliding mode control.
机译:在本文中,设计了基于神经的具有可移动滑动表面的模糊逻辑滑模控制,以对桥式起重机进行监控。建立了起重机的数学模型,并获得了运动方程。首先,在滑模控制的设计中已经为固定的滑动表面确定了合适的滑动表面系数。通过使用基于神经的模糊逻辑算法来移动滑动表面,从而消除了常规滑动模式控制的缺点。通过应用该控制算法,控制性能得到了不可思议的提高。在该应用中,在用3 kg负载和100 cm绳索长度的起重机将负载运送到1 m外的目标的过程中,更新了受影响的控制器的参数并实现了对其的训练。为了显示控制器对参数不确定性的不敏感程度,将负荷值设为8 kg,将绳索长度设为3 m,并实现了针对不同目标的控制。将MATLAB程序用于数值解,并对结果进行图形检查。取得的成果证明了基于神经网络的模糊逻辑滑模控制算法的成功。

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