在约束和干扰下的系统辨识：一种贝叶斯估计方法
System Identification under Constraints and Disturbance: A Bayesian Estimation Approach
AI总结：
本文提出了一种贝叶斯估计方法，用于高精度联合估计机器人状态轨迹和物理参数，通过引入物理一致的约束和能量观测，提升了系统辨识的准确性和鲁棒性。
摘要：
我们介绍了一种贝叶斯系统辨识（SysID）框架，用于高精度地联合估计机器人的状态轨迹和物理参数。该框架嵌入了物理一致的逆动力学、接触和回路闭合约束，以及完整的关节摩擦模型作为硬性、分阶段的等式约束。它依赖于基于能量的回归器来增强参数可观测性，支持对惯性和驱动参数的等式和不等式先验，强制动态一致的干扰投影，并通过能量观测来补充本体感觉测量，以消除非线性摩擦效应的歧义。为确保可扩展性，我们推导出一个参数化的等式约束黎卡提递推公式，保持了问题的带状结构，实现了时间跨度上的线性复杂度，并开发了计算高效的导数。在代表性机器人系统上的仿真研究，以及在配备Z1臂的Unitree B1上的硬件实验，表明了比前向动力学和解耦识别基线更快的收敛速度、更低的惯性和摩擦估计误差以及改进的接触一致性。当部署在模型预测控制框架中时，所得到的模型在挑战性环境中运动的跟踪性能方面产生了可测量的改进。
英文摘要：
We introduce a Bayesian system identification (SysID) framework for jointly estimating robot's state trajectories and physical parameters with high accuracy. It embeds physically consistent inverse dynamics, contact and loop-closure constraints, and fully featured joint friction models as hard, stage-wise equality constraints. It relies on energy-based regressors to enhance parameter observability, supports both equality and inequality priors on inertial and actuation parameters, enforces dynamically consistent disturbance projections, and augments proprioceptive measurements with energy observations to disambiguate nonlinear friction effects. To ensure scalability, we derive a parameterized equality-constrained Riccati recursion that preserves the banded structure of the problem, achieving linear complexity in the time horizon, and develop computationally efficient derivatives. Simulation studies on representative robotic systems, together with hardware experiments on a Unitree B1 equipped with a Z1 arm, demonstrate faster convergence, lower inertial and friction estimation errors, and improved contact consistency compared to forward-dynamics and decoupled identification baselines. When deployed within model predictive control frameworks, the resulting models yield measurable improvements in tracking performance during locomotion over challenging environments.
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