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Adaptive neural network sliding mode control of shipboard container cranes considering actuator backlash

机译：考虑执行器间隙的船用集装箱起重机自适应神经网络滑模控制

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摘要

Offshore container crane is a highly under-actuated nonlinear system whereas only two control inputs are employed for driving six system outputs. Controlling such a system is not easy since it faces with many challenges composed of actuator backlash, geometrical nonlinearities, seawater viscoelasticity, cable flexibility, strong wave and wind disturbances, and considerable lack of actuators. This article proposes a robust adaptive system for a ship-mounted container crane with the disadvantages mentioned above. The controller structure is constructed using second-order sliding mode control (SOSMC), and a modeling estimator is designed on the basis of radial basis function network (RBFN). While other adaptive control techniques only estimates system parameters, the adaptive RBFN algorithm approximates almost all the structure of a crane model, including system parameters. Simulations and experiments are conducted to verify the superiority of the proposed control system.
机译：海上集装箱起重机是一种高度欠驱动的非线性系统，而仅使用两个控制输入来驱动六个系统输出。控制这样的系统并不容易，因为它面临着许多挑战，包括致动器间隙，几何非线性，海水粘弹性，电缆柔韧性，强波浪和风扰动以及相当少的致动器。本文提出了一种具有上述缺点的用于船舶集装箱起重机的鲁棒自适应系统。采用二阶滑模控制（SOSMC）构造控制器结构，并基于径向基函数网络（RBFN）设计建模估计器。虽然其他自适应控制技术仅估算系统参数，但自适应RBFN算法几乎可以估算起重机模型的所有结构，包括系统参数。进行仿真和实验以验证所提出的控制系统的优越性。

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