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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Neural Network-Based Adaptive Antiswing Control of an Underactuated Ship-Mounted Crane With Roll Motions and Input Dead Zones
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Neural Network-Based Adaptive Antiswing Control of an Underactuated Ship-Mounted Crane With Roll Motions and Input Dead Zones

机译：基于神经网络的带有侧倾运动和输入死区的欠驱动船载起重机的自适应防摆控制

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摘要

As a type of indispensable oceanic transportation tools, ship-mounted crane systems are widely employed to transport cargoes and containers on vessels due to their extraordinary flexibility. However, various working requirements and the oceanic environment may cause some uncertain and unfavorable factors for ship-mounted crane control. In particular, to accomplish different control tasks, some plant parameters (e.g., boom lengths, payload masses, and so on) frequently change; hence, most existing model-based controllers cannot ensure satisfactory control performance any longer. For example, inaccurate gravity compensation may result in positioning errors. Additionally, due to ship roll motions caused by sea waves, residual payload swing generally exists, which may result in safety risks in practice. To solve the above-mentioned issues, this paper designs a neural network-based adaptive control method that can provide effective control for both actuated and unactuated state variables based on the original nonlinear ship-mounted crane dynamics without any linearizing operations. In particular, the proposed update law availably compensates parameter/structure uncertainties for ship-mounted crane systems. Based on a 2-D sliding surface, the boom and rope can arrive at their preset positions in finite time, and the payload swing can be completely suppressed. Furthermore, the problem of nonlinear input dead zones is also taken into account. The stability of the equilibrium point of all state variables in ship-mounted crane systems is theoretically proven by a rigorous Lyapunov-based analysis. The hardware experimental results verify the practicability and robustness of the presented control approach.
机译：作为一种必不可少的海洋运输工具，船用起重机系统因其非凡的灵活性而被广泛用于在船上运输货物和集装箱。但是，各种工作要求和海洋环境可能会给车载起重机的控制带来一些不确定和不利的因素。特别是，为了完成不同的控制任务，一些工厂参数（例如，动臂长度，有效负载质量等）经常发生变化;因此，大多数现有的基于模型的控制器不再能够确保令人满意的控制性能。例如，不正确的重力补偿可能会导致定位错误。另外，由于海浪引起的船舶侧倾运动，通常存在残余的有效载荷摆动，这在实践中可能导致安全风险。为了解决上述问题，本文设计了一种基于神经网络的自适应控制方法，该方法可以基于原始的非线性船用起重机动力学，而无需任何线性化操作，就可以为驱动状态变量和非驱动状态变量提供有效的控制。特别地，所提出的更新定律可有效地补偿船用起重机系统的参数/结构不确定性。基于二维滑动表面，动臂和绳索可以在有限的时间内到达其预设位置，并且可以完全抑制有效载荷的摆动。此外，还考虑了非线性输入死区的问题。严格的基于Lyapunov的分析从理论上证明了船用起重机系统中所有状态变量平衡点的稳定性。硬件实验结果验证了所提出的控制方法的实用性和鲁棒性。

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