用可逆变换建模重型液压机械臂，控制精度提升超50%。
A Data-Driven Modeling and Motion Control of Heavy-Load Hydraulic Manipulators via Reversible Transformation
- 通过可逆变换构建可逆非线性模型，避免黑箱神经网络。
- 实测轨迹跟踪误差均方根降低至少50%，优于传统方法。
- 框架可快速迁移至不同液压控制设备，适合工业自动化场景。
本文提出一种面向工业重型液压机械臂无人化自动操作的数据驱动建模与混合运动控制框架。不同于直接使用神经网络黑箱模型，我们利用多层感知机对经可逆变换后的物理积分链系统动力学进行近似，构建可逆非线性模型。该模型采用监督学习离线训练，数据来自仿真或实验。整个混合控制框架由模型逆控制器（补偿非线性动力学）和比例-微分控制器（增强鲁棒性）组成，其稳定性通过李雅普诺夫理论证明。共仿真与实验结果表明，针对一款商用39吨级液压挖掘机的运动控制任务，所提方法使轨迹跟踪误差的均方根值相比传统方法至少降低50%。此外，通过对系统模型的分析，该框架可快速适配不同控制对象。
原文摘要 · Abstract (English)
This work proposes a data-driven modeling and the corresponding hybrid motion control framework for unmanned and automated operation of industrial heavy-load hydraulic manipulator. Rather than the direct use of a neural network black box, we construct a reversible nonlinear model by using multilayer perceptron to approximate dynamics in the physical integrator chain system after reversible transformations. The reversible nonlinear model is trained offline using supervised learning techniques, and the data are obtained from simulations or experiments. Entire hybrid motion control framework consists of the model inversion controller that compensates for the nonlinear dynamics and proportional-derivative controller that enhances the robustness. The stability is proved with Lyapunov theory. Co-simulation and Experiments show the effectiveness of proposed modeling and hybrid control framework. With a commercial 39-ton class hydraulic excavator for motion control tasks, the root mean square error of trajectory tracking error decreases by at least 50\% compared to traditional control methods. In addition, by analyzing the system model, the proposed framework can be rapidly applied to different control plants.
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