用概率分布控制软体手指，提升不确定性下的运动精度
Model Predictive Control for a Soft Robotic Finger with Stochastic Behavior based on Fokker-Planck Equation
- 基于福克-普朗克方程建模软体机器人运动概率分布
- 仿真显示该方法能有效抑制柔性系统中的随机扰动
- 适合需高鲁棒性的软体机器人控制场景
软体机器人的固有柔性带来更强适应性与安全性，但也导致运动高度不确定且非线性。开环控制因缺乏反馈，在此类系统中表现不佳。尽管模型控制是潜在解决方案，但传统确定性模型难以处理不确定性。本文提出一种基于福克-普朗克方程（FPE）的随机控制策略——FPE-MPC，不直接控制状态，而是控制其概率分布。通过两个数值仿真案例验证了该方法在管理软体系统固有不确定性方面的有效性。
原文摘要 · Abstract (English)
The inherent flexibility of soft robots offers numerous advantages, such as enhanced adaptability and improved safety. However, this flexibility can also introduce challenges regarding highly uncertain and nonlinear motion. These challenges become particularly problematic when using open-loop control methods, which lack a feedback mechanism and are commonly employed in soft robot control. Though one potential solution is model-based control, typical deterministic models struggle with uncertainty as mentioned above. The idea is to use the Fokker-Planck Equation (FPE), a master equation of a stochastic process, to control not the state of soft robots but the probabilistic distribution. In this study, we propose and implement a stochastic-based control strategy, termed FPE-based Model Predictive Control (FPE-MPC), for a soft robotic finger. Two numerical simulation case studies examine the performance and characteristics of this control method, revealing its efficacy in managing the uncertainty inherent in soft robotic systems.
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