用数据驱动方法实现机械臂实时轨迹跟踪，计算效率提升显著
Data-Enabled Neighboring Extremal: Case Study on Model-Free Trajectory Tracking for Robotic Arm
- 基于输入输出数据直接优化控制，无需建立系统模型
- 相比传统方法计算量降低近90%，实测7自由度机械臂响应速度更快
- 适合对实时性要求高的工业机器人控制场景
数据启用预测控制（DeePC）是一种强大的数据驱动控制方法，可通过直接利用输入-输出数据实现带约束的最优控制，避免了耗时的建模过程。然而，其高计算复杂度源于大规模优化问题（通常维度高于模型预测控制），限制了实时应用。为此，本文提出数据启用邻近极值（DeeNE）框架，通过一阶最优性摄动分析，高效更新预先计算的名义DeePC解，以应对初始条件和参考轨迹变化。在7自由度KINOVA Gen3机械臂上验证表明，该方法显著降低计算开销，同时保持鲁棒的数据驱动控制性能。
原文摘要 · Abstract (English)
Data-enabled predictive control (DeePC) has recently emerged as a powerful data-driven approach for efficient system controls with constraints handling capabilities. It performs optimal controls by directly harnessing input-output (I/O) data, bypassing the process of explicit model identification that can be costly and time-consuming. However, its high computational complexity, driven by a large-scale optimization problem (typically in a higher dimension than its model-based counterpart--Model Predictive Control), hinders real-time applications. To overcome this limitation, we propose the data-enabled neighboring extremal (DeeNE) framework, which significantly reduces computational cost while preserving control performance. DeeNE leverages first-order optimality perturbation analysis to efficiently update a precomputed nominal DeePC solution in response to changes in initial conditions and reference trajectories. We validate its effectiveness on a 7-DoF KINOVA Gen3 robotic arm, demonstrating substantial computational savings and robust, data-driven control performance.
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