In this paper, the problem of load transportation and robust mitigation of payload oscillations in uncertain tower-cranes is addressed. This problem is tackled through a control scheme based on the philosophy of activ...In this paper, the problem of load transportation and robust mitigation of payload oscillations in uncertain tower-cranes is addressed. This problem is tackled through a control scheme based on the philosophy of active-disturbance-rejection. Here, a general disturbance model built with two dominant components: polynomial and harmonic, is stated. Then, a disturbance observer is formulated through state-vector augmentation of the tower-crane model. Thus, better performance of estimations for system states and disturbances is achieved. The control law is then formulated to actively reject the disturbances but also to accommodate the closed-loop system dynamics even under system uncertainty. The proposed control schema is validated via experimentation using a small-scale tower-crane,and compared with other relevant active disturbance rejection control(ADRC)-based techniques. The experimental results show that the proposed control scheme is robust under parametric uncertainty of the system, and provides improved attenuation of payload oscillations even under system uncertainty.展开更多
针对传统人工操控塔式起重机在运输货物时易导致路径拐点多、负载摆动大的问题,提出一种改进的人工鱼群塔式起重机智能路径规划的新算法。根据塔式起重机的工作环境,建立三维的地图环境模型来模拟障碍物较多的复杂建筑环境,并结合起重...针对传统人工操控塔式起重机在运输货物时易导致路径拐点多、负载摆动大的问题,提出一种改进的人工鱼群塔式起重机智能路径规划的新算法。根据塔式起重机的工作环境,建立三维的地图环境模型来模拟障碍物较多的复杂建筑环境,并结合起重机在建筑场所的运行特点,对传统人工鱼群算法(artificial fish swarm algorithm, AFSA)进行改进,采用自适应策略让鱼群在寻优过程中的状态不断变化,及时调整自身的移动步长和视野,并基于生存竞争机制对人工鱼的随机行为进行改进,在一定程度上改善了算法的寻优能力,利用三次方样条数据插值拟合曲线得到更适合塔式起重机的光滑避障路径。仿真结果表明,改进后的算法为塔式起重机在障碍物较多的复杂建筑环境下找到一条最优避障路径。展开更多
构建了智能吊装规划体系,旨在通过路径搜索与轨迹优化的双链条创新,提升塔式起重机系统在复杂受限空间中的避障能力和吊运过程的动态稳定性。首先,基于动力学分析建立了系统的非线性模型,并进行微分平坦分析,为运动规划提供简单直接的...构建了智能吊装规划体系,旨在通过路径搜索与轨迹优化的双链条创新,提升塔式起重机系统在复杂受限空间中的避障能力和吊运过程的动态稳定性。首先,基于动力学分析建立了系统的非线性模型,并进行微分平坦分析,为运动规划提供简单直接的表达方式;然后,针对塔式起重机系统在复杂环境下的路径规划问题,提出基于方向偏置的改进版双向快速探索随机树(directional-biased bidirectional rapidly-exploring random tree*,DB-BiRRT*)算法,引入融合区域概率采样的目标偏置机制和基于改进势场函数的方向引导机制优化节点扩展过程,提高路径规划的效率和质量;最后,充分考虑避障和减摆等系统全状态约束条件,采用基于非均匀有理B样条曲线的多目标优化方法进行轨迹规划,获得最小化吊运时间、能耗和负载摆角的最优轨迹。仿真结果验证了所提方法的有效性和优越性。展开更多
文摘In this paper, the problem of load transportation and robust mitigation of payload oscillations in uncertain tower-cranes is addressed. This problem is tackled through a control scheme based on the philosophy of active-disturbance-rejection. Here, a general disturbance model built with two dominant components: polynomial and harmonic, is stated. Then, a disturbance observer is formulated through state-vector augmentation of the tower-crane model. Thus, better performance of estimations for system states and disturbances is achieved. The control law is then formulated to actively reject the disturbances but also to accommodate the closed-loop system dynamics even under system uncertainty. The proposed control schema is validated via experimentation using a small-scale tower-crane,and compared with other relevant active disturbance rejection control(ADRC)-based techniques. The experimental results show that the proposed control scheme is robust under parametric uncertainty of the system, and provides improved attenuation of payload oscillations even under system uncertainty.
文摘针对传统人工操控塔式起重机在运输货物时易导致路径拐点多、负载摆动大的问题,提出一种改进的人工鱼群塔式起重机智能路径规划的新算法。根据塔式起重机的工作环境,建立三维的地图环境模型来模拟障碍物较多的复杂建筑环境,并结合起重机在建筑场所的运行特点,对传统人工鱼群算法(artificial fish swarm algorithm, AFSA)进行改进,采用自适应策略让鱼群在寻优过程中的状态不断变化,及时调整自身的移动步长和视野,并基于生存竞争机制对人工鱼的随机行为进行改进,在一定程度上改善了算法的寻优能力,利用三次方样条数据插值拟合曲线得到更适合塔式起重机的光滑避障路径。仿真结果表明,改进后的算法为塔式起重机在障碍物较多的复杂建筑环境下找到一条最优避障路径。
文摘构建了智能吊装规划体系,旨在通过路径搜索与轨迹优化的双链条创新,提升塔式起重机系统在复杂受限空间中的避障能力和吊运过程的动态稳定性。首先,基于动力学分析建立了系统的非线性模型,并进行微分平坦分析,为运动规划提供简单直接的表达方式;然后,针对塔式起重机系统在复杂环境下的路径规划问题,提出基于方向偏置的改进版双向快速探索随机树(directional-biased bidirectional rapidly-exploring random tree*,DB-BiRRT*)算法,引入融合区域概率采样的目标偏置机制和基于改进势场函数的方向引导机制优化节点扩展过程,提高路径规划的效率和质量;最后,充分考虑避障和减摆等系统全状态约束条件,采用基于非均匀有理B样条曲线的多目标优化方法进行轨迹规划,获得最小化吊运时间、能耗和负载摆角的最优轨迹。仿真结果验证了所提方法的有效性和优越性。
