Adaptive Antiswing Control for Underactuated Dual Overhead Crane System Using Neural Network
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摘要: 由于工业实践对运输能力提出了更高的要求, 双吊车的应用日益广泛. 然而其动力学模型非线性很强, 因此控制器结构十分复杂. 另一方面, 大型货物的摆动很难抑制, 这给双吊车的自动化带来了巨大的挑战. 为了处理以上问题, 首先, 采用神经网络准确地估计了系统的模型, 在此基础上提出了一种自适应防摆控制方法, 很好地实现了双吊车系统的防摆控制; 然后, 采用李雅普诺夫方法, 严格地证明了系统在平衡点的渐近稳定性; 最后, 通过大量的实验结果, 验证了该方法具有良好的性能.Abstract: There is a mounting application of dual overhead crane systems because of the higher requirements for transportation capacity in industrial practice. However, due to the strong nonlinearity of system dynamics, the designed controller is quite sophisticated. On the other hand, the swing of large cargos is more difficult to suppress, making the automation of dual overhead crane systems a huge challenge. To solve the above problems, an adaptive antiswing control method is proposed by utilizing a neural network to estimate the model of dual overhead crane systems, which achieves superior antiswing performance for dual overhead crane systems. Then, the asymptotic stability of the system at the equilibrium point is proved by the Lyapunov techniques. Finally, the control performance is validated by experimental results.
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Key words:
- Antiswing control /
- dual crane /
- adaptive control /
- neural network /
- underactuated system
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图 4 实验1: 系统控制输入
$F_1$ 、$F_2$ 和神经网络权值$W_1$ 、$W_2$ 、$V_1$ 、$V_2$ 的范数Fig. 4 Experiment 1: The control input
$F_1,\ F_2$ of system and the norm of the NN weights$W_1,\ W_2,\ V_1,\ V_2$ 表 1 实验1的性能指标
Table 1 Performance indices of Experiment 1
性能指标 本文方法 PD LQR PID SMC $e_{1xs}\,({\rm{ m}})$ ${\bf{-0.003}}$ −0.028 −0.032 −0.007 0.001 $e_{2xs}\,({\rm{ m}})$ −0.003 −0.004 −0.004 −0.004 ${\bf{-0.001}}$ $\theta_{1{\rm{max}}}\,(^\circ)$ ${\bf{0.582}}$ 7.149 3.666 7.746 1.357 $\theta_{2{\rm{max}}}\,(^\circ)$ ${\bf{0.636}}$ 5.908 4.387 5.401 2.481 $\theta_{3{\rm{max}}}\,(^\circ)$ ${\bf{0.002}}$ 0.472 0.187 0.452 0.056 $\theta_{1res}\,(^\circ)$ ${\bf{0.293}}$ 4.160 3.350 1.515 1.274 $\theta_{2res}\,(^\circ)$ ${\bf{0.000}}$ 2.926 3.053 1.273 1.145 $\theta_{3res}\,(^\circ)$ ${\bf{0.000}}$ 0.085 0.068 0.016 0.001 $\Delta_{x{\rm{max}}}\,({\rm{ m} })$ ${\bf{0.905}}$ 0.924 0.994 0.920 0.938 $\Delta_{x{\rm{min} } }\,({\rm{ m} })$ ${\bf{0.899}}$ 0.749 0.848 0.697 0.894 
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