用神经网络增强滑模控制，提升机械臂轨迹跟踪精度。
Development of an Adaptive Sliding Mode Controller using Neural Networks for Trajectory Tracking of a Cylindrical Manipulator
- 结合神经网络的自适应滑模控制，动态补偿系统不确定性。
- 仿真显示轨迹跟踪误差小、响应快、可靠性高。
- 适合3D打印等对精度要求高的工业自动化场景。
圆柱形机械臂广泛应用于工业自动化，尤其在3D打印等新兴技术中前景广阔。然而，非线性模型在存在系统不确定性的条件下进行轨迹控制仍面临挑战，常导致精度与可靠性下降。为此，本文提出一种集成神经网络的自适应滑模控制器（ASMC-NN），利用滑模控制的鲁棒性与神经网络的自适应能力，有效应对不确定性与动态变化。仿真结果表明，所提方法在轨迹跟踪中表现出高精度、快速响应和良好可靠性，为3D打印等应用提供了有前景的解决方案。
原文摘要 · Abstract (English)
Cylindrical manipulators are extensively used in industrial automation, especially in emerging technologies like 3D printing, which represents a significant future trend. However, controlling the trajectory of nonlinear models with system uncertainties remains a critical challenge, often leading to reduced accuracy and reliability. To address this, the study develops an Adaptive Sliding Mode Controller (ASMC) integrated with Neural Networks (NNs) to improve trajectory tracking for cylindrical manipulators. The ASMC leverages the robustness of sliding mode control and the adaptability of neural networks to handle uncertainties and dynamic variations effectively. Simulation results validate that the proposed ASMC-NN achieves high trajectory tracking accuracy, fast response time, and enhanced reliability, making it a promising solution for applications in 3D printing and beyond.
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