This paper proposes an input shaping technique for efficient payload swing control of a tower crane with cable length variations. Artificial neural network is utilized to design a zero vibration derivative shaper that can be updated according to different cable lengths as the natural frequency and damping ratio of the system changes. Unlike the conventional input shapers that are designed based on a fixed frequency, the proposed technique can predict and update the optimal shaper parameters according to the new cable length and natural frequency. Performance of the proposed technique is evaluated by conducting experiments on a laboratory tower crane with cable length variations and under simultaneous tangential and radial crane motions. The shaper is shown to be robust and provides low payload oscillation with up to 40% variations in the natural frequency. With a 40% decrease in the natural frequency, the superiority of the artificial neural network–zero vibration derivative shaper is confirmed by achieving at least a 50% reduction in the overall and residual payload oscillations when compared to the robust zero vibration derivative and extra insensitive shapers designed based on the average operating frequency. It is envisaged that the proposed shaper can be further utilized for control of tower cranes with more parameter uncertainties.

中文翻译：

---

使用基于神经网络的输入整形器控制塔式起重机的有效载荷摆动

本文提出了一种输入整形技术，用于在电缆长度变化的情况下有效控制塔式起重机的有效载荷摆动。利用人工神经网络设计零振动导数整形器，该整形器可以随着系统固有频率和阻尼比的变化，根据不同的电缆长度进行更新。与基于固定频率设计的传统输入整形器不同，所提出的技术可以根据新的电缆长度和固有频率预测和更新最佳整形器参数。所提出技术的性能通过在具有电缆长度变化和同时切向和径向起重机运动的实验室塔式起重机上进行实验来评估。该整形器被证明是坚固的，并提供低有效载荷振荡，固有频率变化高达 40%。随着固有频率降低 40%，与稳健的零振动导数和额外的不灵敏相比，人工神经网络零振动导数整形器的优越性通过实现至少 50% 的整体和残余有效载荷振荡减少得到证实基于平均工作频率设计的整形器。预计所提出的整形器可以进一步用于控制具有更多参数不确定性的塔式起重机。与基于平均工作频率设计的稳健的零振动导数和额外的不灵敏整形器相比，人工神经网络零振动导数整形器的优越性通过实现至少 50% 的总体和残余有效载荷振荡减少得到证实。预计所提出的整形器可以进一步用于控制具有更多参数不确定性的塔式起重机。与基于平均工作频率设计的稳健的零振动导数和额外的不灵敏整形器相比，人工神经网络零振动导数整形器的优越性通过实现至少 50% 的整体和残余有效载荷振荡减少得到证实。预计所提出的整形器可以进一步用于控制具有更多参数不确定性的塔式起重机。