论文标题
贝叶斯优化对软机器人技术的样品模型预测控制设计
Sample-efficient Model Predictive Control Design of Soft Robotics by Bayesian Optimization
论文作者
论文摘要
本文提出了一种使用贝叶斯优化的电缆驱动的软机器人技术设计模型预测控制（MPC）的样品效率的数据驱动方法。所提出的方法没有对软机器人的复杂动力学进行建模，而是使用贝叶斯优化来搜索最佳的低维预测模型及其关联的控制器，以最大程度地减少闭环响应的目标函数。预测模型是通过贝叶斯优化从每次迭代中的闭环输入输出数据进行更新的。然后，基于更新的预测模型设计线性MPC，并根据闭环响应进行评估。与直接搜索控制器参数不同，闭环系统稳定性和输入/输出约束可以在MPC设计中轻松处理。经过几次迭代后，可以获得（子）最佳控制器的收敛解决方案，从而最大程度地减少用户定义的闭环性能索引。通过对电缆执行的软机器人进行高保真模拟来模拟和验证所提出的方法。模拟结果表明，所提出的方法可以在没有先验模型的情况下为软机器人实现所需的跟踪控制器。
This paper presents a sample-efficient data-driven method to design model predictive control (MPC) for cable-actuated soft robotics using Bayesian optimization. Instead of modeling the complex dynamics of the soft robots, the proposed approach uses Bayesian optimization to search the best-guessed low-dimensional prediction model and its associated controller to minimize the objective function of closed-loop responses. The prediction model is updated by Bayesian optimization from the closed-loop input-output data in each iteration. A linear MPC is then designed based on the updated prediction model, and evaluated based on the closed-loop responses. Different from directly searching controller parameters, the closed-loop system stability, and inputs/outputs constraints can be easily handled in the MPC design. After a few iterations, a convergent solution of a (sub-)optimal controller can be obtained, which minimizes the user-defined closed-loop performance index. The proposed method is simulated and validated by a high-fidelity simulation of a cable-actuated soft robot. The simulation results demonstrate that the proposed approach can achieve desired tracking controller for the soft robot without a prior-known model.
