用无量纲模型预测控制，让调好的控制器直接迁移到相似系统
Direct transfer of optimized controllers to similar systems using dimensionless MPC
- 基于无量纲化重构建模预测控制，实现闭环性能自动优化
- 在小车摆杆和赛车问题上验证，控制器可直接迁移至相似系统
- 支持跨尺度数据训练，适合工程仿真与实际系统迁移场景
缩比模型实验广泛应用于各类工程领域，以降低试验成本并克服全尺寸系统的限制。此类实验的有效性依赖于量纲分析和动态相似性原理。然而，将控制器迁移至全尺寸系统通常仍需额外调参。本文提出一种基于无量纲模型预测控制的方法，可自动优化闭环性能并实现控制器的直接迁移。经重构后，优化控制器的闭环行为可直接传递至新的动态相似系统。此外，无量纲形式允许在参数优化中使用不同尺度系统的数据。我们在小车摆杆抬升和赛车问题上进行了验证，采用强化学习或贝叶斯优化进行控制器参数调优。本文所用代码已公开于 https://github.com/josipkh/dimensionless-mpcrl。
原文摘要 · Abstract (English)
Scaled model experiments are commonly used in various engineering fields to reduce experimentation costs and overcome constraints associated with full-scale systems. The relevance of such experiments relies on dimensional analysis and the principle of dynamic similarity. However, transferring controllers to full-scale systems often requires additional tuning. In this paper, we propose a method to enable a direct controller transfer using dimensionless model predictive control, tuned automatically for closed-loop performance. With this reformulation, the closed-loop behavior of an optimized controller transfers directly to a new, dynamically similar system. Additionally, the dimensionless formulation allows for the use of data from systems of different scales during parameter optimization. We demonstrate the method on a cartpole swing-up and a car racing problem, applying either reinforcement learning or Bayesian optimization for tuning the controller parameters. Software used to obtain the results in this paper is publicly available at https://github.com/josipkh/dimensionless-mpcrl.
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