基于参数优化的桥式起重机防摆控制研究

Research on Anti-swing Control for Bridge Cranes Based on Parameter Optimization

摘要

针对外部未知作用力所导致的桥式起重机起吊过程中负载摆动明显的问题,设计了一种基于改进蜜獾算法参数优化的神经网络滑模控制器(SMC)。首先,使用径向基函数(RBF)神经网络对桥式起重机系统动力学模型中的未知作用力进行有效拟合,再将其拟合值输入SMC,以保证桥式起重机系统起吊过程中的渐进稳定性。然后,引入Chebyshev混沌种群机制和高斯种群变异策略对标准蜜獾算法进行改进,以增强其对RBF滑模控制器(RBFSMC)中速率参数值的寻优能力,从而削弱系统抖振。最后,通过与SMC、RBFSMC、粒子群优化-RBFSMC控制器进行定位跟踪性能、输出驱动力和防摆控制效果对比,证明所设计的控制器在桥式起重机精确定位和消摆方面具有较好的控制效果。

Abstract

To address the significant load swing caused by unknown external forces during the hoisting process of bridge cranes, a neural network Sliding Mode Controller (SMC) with parameter optimization based on an improved honey badger algorithm is proposed. First, a Radial Basis Function (RBF) neural network is employed to effectively approximate the unknown external forces in the dynamic model of the crane system. The output of this approximation is then fed into the SMC to ensure the asymptotic stability of the crane system during the hoisting process. Next, the Chebyshev chaos population mechanism and Gaussian mutation strategy are introduced to improve the standard honey badger algorithm, enhancing its optimization ability for the rate parameters of the RBFSMC and thereby reducing system oscillations. Finally, through comparative simulations with SMC, RBFSMC, and particle swarm optimization-RBFSMC controllers, the proposed controller is shown to achieve better positioning, output driving force, and anti-swing control performance, demonstrating its superior effectiveness in precise positioning and sway suppression for the crane system.

关键词

蜜獾算法 / RBF神经网络 / 滑模控制器 / 桥式起重机

Key words

honey badger algorithm / RBF neural network / sliding mode controller / bridge crane

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基金资助

天津市自然科学基金资助项目(20YDTPJC00840)