用物理神经网络实现软体机器人的实时高精度控制
Adaptive Model-Predictive Control of a Soft Continuum Robot Using a Physics-Informed Neural Network Based on Cosserat Rod Theory
- 基于柯西罗德理论的物理信息神经网络加速动力学建模
- 仿真与实测均达3毫米以内定位误差，控制频率70赫兹
- 适合需要高精度动态控制的柔性机器人研究者
软连续体机器人（SCRs）的动态控制具有广阔应用前景，但因精确动力学模型计算成本高而难以实现。现有数据驱动方法如Koopman算子法通常缺乏适应性，且无法重建完整机器人形变，限制了实际应用。本文提出一种基于域解耦物理信息神经网络（DD-PINN）的实时非线性模型预测控制（MPC）框架，具备可调弯曲刚度。该DD-PINN作为柯西罗德模型的代理模型，加速比高达44,000倍，并用于无迹卡尔曼滤波器中，从末端执行器位置测量值估计状态和弯曲柔度。在GPU上以70赫兹运行的非线性进化型MPC在仿真中实现了动态轨迹跟踪与设定点控制，末端位置误差低于3毫米（占驱动器长度的2.3%）。真实实验中控制器达到相似精度，加速度最高达3.55米/秒²。
原文摘要 · Abstract (English)
Dynamic control of soft continuum robots (SCRs) holds great potential for expanding their applications, but remains a challenging problem due to the high computational demands of accurate dynamic models. While data-driven approaches like Koopman-operator-based methods have been proposed, they typically lack adaptability and cannot reconstruct the full robot shape, limiting their applicability. This work introduces a real-time-capable nonlinear model-predictive control (MPC) framework for SCRs based on a domain-decoupled physics-informed neural network (DD-PINN) with adaptable bending stiffness. The DD-PINN serves as a surrogate for the dynamic Cosserat rod model with a speed-up factor of up to 44,000. It is also used within an unscented Kalman filter for estimating the model states and bending compliance from end-effector position measurements. We implement a nonlinear evolutionary MPC running at 70 Hz on the GPU. In simulation, it demonstrates accurate tracking of dynamic trajectories and setpoint control with end-effector position errors below 3 mm (2.3\% of the actuator's length). In real-world experiments, the controller achieves similar accuracy and accelerations up to 3.55 m/s2.
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