IEEE Trans Neural Netw Learn Syst. 2018 Oct;29(10):4791-4801. doi: 10.1109/TNNLS.2017.2770172. Epub 2017 Dec 27.
In recent decades, primal-dual neural networks, as a special type of recurrent neural networks, have received great success in real-time manipulator control. However, noises are usually ignored when neural controllers are designed based on them, and thus, they may fail to perform well in the presence of intensive noises. Harmonic noises widely exist in real applications and can severely affect the control accuracy. This work proposes a novel primal-dual neural network design that directly takes noise control into account. By taking advantage of the fact that the unknown amplitude and phase information of a harmonic signal can be eliminated from its dynamics, our deliberately designed neural controller is able to reach the accurate tracking of reference trajectories in a noisy environment. Theoretical analysis and extensive simulations show that the proposed controller stabilizes the control system polluted by harmonic noises and converges the position tracking error to zero. Comparisons show that our proposed solution consistently and significantly outperforms the existing primal-dual neural solutions as well as feedforward neural one and adaptive neural one for redundancy resolution of manipulators.
近几十年来，作为一种特殊类型的递归神经网络，原始对偶神经网络在实时机械手控制方面取得了巨大成功。然而，在基于这些网络设计神经控制器时，噪声通常被忽略，因此，在存在强烈噪声的情况下，它们可能无法良好运行。谐波噪声在实际应用中广泛存在，会严重影响控制精度。这项工作提出了一种新颖的原始对偶神经网络设计，该设计直接考虑了噪声控制。通过利用谐波信号的未知幅度和相位信息可以从其动态特性中消除这一事实，我们精心设计的神经控制器能够在有噪声的环境中实现对参考轨迹的精确跟踪。理论分析和大量仿真表明，所提出的控制器使受谐波噪声污染的控制系统稳定，并将位置跟踪误差收敛到零。比较表明，我们提出的解决方案在机械手冗余分辨率方面始终显著优于现有的原始对偶神经解决方案以及前馈神经解决方案和自适应神经解决方案。
