无需物理模型，用数据驱动的MPC提升柔性缆绳机器臂控制精度
Data-Driven MPC with Data Selection for Flexible Cable-Driven Robotic Arms
- 基于输入输出数据构建隐式模型，融入MPC框架
- 数据筛选算法使每步求解时间缩短至4毫秒，提速近80%
- 实测定位精度达2.070毫米，轨迹跟踪误差比PID低62%
柔性缆绳驱动机器人臂（FCRAs）具有灵巧和柔顺运动的优势，但电缆的弹性、滞后和摩擦等固有特性给建模与控制带来挑战。本文提出一种仅依赖输入输出数据的模型预测控制（MPC）方法，无需物理模型即可提升控制精度。首先，基于输入输出数据构建隐式模型，并集成到MPC优化框架中；其次，引入数据选择算法（DSA），筛选最能表征系统特性的数据，将每步求解时间降至约4毫秒，效率提升近80%；最后，通过仿真研究超参数对跟踪误差的影响。所提方法在真实FCRA平台上验证，包括五点定位精度测试、五点响应跟踪测试及字母轨迹绘制。结果表明，平均定位精度约为2.070毫米；相较于平均跟踪误差为1.418°的PID方法，该方法实现平均跟踪误差0.541°。
原文摘要 · Abstract (English)
Flexible cable-driven robotic arms (FCRAs) offer dexterous and compliant motion. Still, the inherent properties of cables, such as resilience, hysteresis, and friction, often lead to particular difficulties in modeling and control. This paper proposes a model predictive control (MPC) method that relies exclusively on input-output data, without a physical model, to improve the control accuracy of FCRAs. First, we develop an implicit model based on input-output data and integrate it into an MPC optimization framework. Second, a data selection algorithm (DSA) is introduced to filter the data that best characterize the system, thereby reducing the solution time per step to approximately 4 ms, which is an improvement of nearly 80%. Lastly, the influence of hyperparameters on tracking error is investigated through simulation. The proposed method has been validated on a real FCRA platform, including five-point positioning accuracy tests, a five-point response tracking test, and trajectory tracking for letter drawing. The results demonstrate that the average positioning accuracy is approximately 2.070 mm. Moreover, compared to the PID method with an average tracking error of 1.418°, the proposed method achieves an average tracking error of 0.541°.
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