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A neuro-controller for vibration control of load in a rotary crane system is proposed involving the rotation about the vertical axis only. As in a nonholonomic system, the vibration control method using a static continuous state feedback cannot stabilize the load swing. It is necessary to design a time-varying feedback controller or a discontinuous feedback controller. We propose a simple three-layered neural network as a controller (NC) with genetic algorithm-based (GA-based) training in order to control load swing suppression for the rotary crane system. The NC is trained by a real-coded GA, which substantially simplifies the design of the controller. It appeared that a control scheme with performance comparable to conventional methods can be obtained by a relatively simple approach. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008  相似文献
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虽然起重机系统的荷载摆动呈现出双摆特性比单摆特性更加接近于实际生产,但是,在这种情况下,系统特性分析和控制器的设计难度将大大增加.为此,本文提出一种基于S型曲线轨迹生成法实现旋转起重机两级残留摆角的抑制.首先根据拉格朗日运动方程推导出双摆旋转起重机的数学模型,再采用干扰观测器对该模型中的摩擦项、各子系统之间的耦合项进行补偿从而获得线性模型.由于各子系统中的一、二级摆角仍然是耦合的,因此采用模态分析对其进一步简化从而获得各自子系统的线性解耦模型.其次,分别为各子系统设计S型曲线轨迹,相关参数可通过求解代数方程获得.最后,数值仿真验证该方法的有效性.  相似文献
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