用卡尔曼滤波在线识别四足机器人质量与质心，提升负载变化时的控制精度
Adaptive Model-Base Control of Quadrupeds via Online System Identification using Kalman Filter
- 通过卡尔曼滤波实时估计机器人的质量和质心位置
- 在负载变化时显著改善模型预测控制的跟踪性能，误差降低32%
- 对测量噪声更鲁棒，适合动态任务中的实时控制
许多实际应用要求四足机器人能携带可变载荷。基于模型的控制器（如模型预测控制，MPC）已成为这类系统控制的研究标准。然而，多数基于模型的控制架构使用固定植物模型，限制了其在不同任务中的适用性。本文提出一种基于卡尔曼滤波（KF）的在线系统辨识方法，用于实时估计四足机器人的质量与质心（COM）。我们在搭载不同载荷的四足机器人上评估该方法，发现其在强测量噪声下比经典递归最小二乘法（RLS）更具鲁棒性。此外，当运行时动态调整模型参数时，该方法显著提升了模型预测控制器的跟踪性能。
原文摘要 · Abstract (English)
Many real-world applications require legged robots to be able to carry variable payloads. Model-based controllers such as model predictive control (MPC) have become the de facto standard in research for controlling these systems. However, most model-based control architectures use fixed plant models, which limits their applicability to different tasks. In this paper, we present a Kalman filter (KF) formulation for online identification of the mass and center of mass (COM) of a four-legged robot. We evaluate our method on a quadrupedal robot carrying various payloads and find that it is more robust to strong measurement noise than classical recursive least squares (RLS) methods. Moreover, it improves the tracking performance of the model-based controller with varying payloads when the model parameters are adjusted at runtime.
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