无需模型的控制新方法，计算简单且效果接近传统建模方案。
Model-Free Predictive Control: Introductory Algebraic Calculations, and a Comparison with HEOL and ANNs
- 基于常系数微分方程重构无模型预测控制，替代动态规划等经典方法。
- 在化学反应器与双水箱系统上验证，性能仅略逊于需部分模型知识的HEOL。
- 挑战了机器学习建模在控制中的必要性，适合控制工程与强化学习研究者。
模型预测控制（MPC）虽广泛使用，但依赖精确模型。本文提出一种新型无模型预测控制（MFPC），通过常系数线性微分方程实现，融合最优控制新视角与近期无模型控制进展，取代动态规划、哈密顿-雅可比-贝尔曼方程及庞特里亚金极值原理。计算负担低，实现简便。以化学反应器和双水箱系统两个非线性案例验证该方法。与需部分过程模型知识的HEOL方法对比，性能仅略有优势。近期利用复杂神经网络对双水箱系统辨识的结果表明，控制乃至更广泛的AI任务中，完整建模与机器学习机制并非总是必需。
原文摘要 · Abstract (English)
Model predictive control (MPC) is a popular control engineering practice, but requires a sound knowledge of the model. Model-free predictive control (MFPC), a burning issue today, also related to reinforcement learning (RL) in AI, is reformulated here via a linear differential equation with constant coefficients, thanks to a new perspective on optimal control combined with recent advances in the field of model-free control (MFC). It is replacing Dynamic Programming, the Hamilton-Jacobi-Bellman equation, and Pontryagin's Maximum Principle. The computing burden is low. The implementation is straightforward. Two nonlinear examples, a chemical reactor and a two tank system, are illustrating our approach. A comparison with the HEOL setting, where some expertise of the process model is needed, shows only a slight superiority of the later. A recent identification of the two tank system via a complex ANN architecture might indicate that a full modeling and the corresponding machine learning mechanism are not always necessary neither in control, nor, more generally, in AI.
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