Adaptive Neural-Fuzzy controller design combined with LQR to control the position of gantry crane

Dinh Do Van
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Abstract

As the world grows, the demand for transporting goods is increasing, the number of goods in factories and ports is increasing, to transport all these goods, cranes are indispensable. In fact, currently, crane rigs working in factories and ports operate with low stability, when working or the phenomenon of swaying of the load occurs, leading to inaccurate positioning, loss of safe transportation of goods. To overcome these shortcomings, the paper proposes the design of a neural-fuzzy adaptive controller combined with an LQR controller (ANFIS-LQR) to control the forklift's position in the shortest time to achieve the desired exact position. At the same time, we want to control the deflection angle of the load so that the vibration when working is minimal. To check and evaluate the quality and stability of the system; the proposed design controller is simulated on MATLAB/Simulink software in the case of changes in system parameters and noise affecting the gantry crane system. To evaluate the superiority of the paper compared with published works, the author compares ANFIS-LQR with other published control methods such as DE-PID, Fuzzy-PD, Fuzzy dual and Fuzzy sliding, the simulation results show that the neural-fuzzy adaptive controller combined with the proposed LQR controller works well t_xlvt=2.1s , t_xlgt=3.5s, 0max=0.3(rad).
结合LQR的自适应神经模糊控制器设计对龙门起重机的位置进行控制
随着世界的发展,运输货物的需求越来越大,工厂和港口的货物数量越来越多,要运输所有这些货物,起重机是必不可少的。事实上,目前在工厂和港口工作的起重机运行稳定性较低,在工作时或出现载荷摇摆的现象,导致定位不准确,失去货物的安全运输。为了克服这些缺点,本文提出了一种结合LQR控制器的神经模糊自适应控制器(ANFIS-LQR)的设计,以在最短的时间内控制叉车的位置,以达到期望的精确位置。同时,我们要控制负载的偏转角度,使工作时的振动最小。检查和评价系统的质量和稳定性;在MATLAB/Simulink软件上对所设计的控制器进行了系统参数和噪声变化对龙门起重机系统影响的仿真。为了评价本文与已发表的文献相比的优越性,作者将anfiss -LQR与其他已发表的控制方法如DE-PID、Fuzzy- pd、Fuzzy对偶和Fuzzy滑动进行了比较,仿真结果表明,与所提出的LQR控制器相结合的神经模糊自适应控制器效果良好(t_xlvt=2.1s, t_xlgt=3.5s, 0max=0.3(rad))。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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