Model-Free Predictive Control: Introductory Algebraic Calculations, and a Comparison with HEOL and ANNs
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913801975103488 |
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| author | Join, Cédric Delaleau, Emmanuel Fliess, Michel |
| author_facet | Join, Cédric Delaleau, Emmanuel Fliess, Michel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_00443 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Model-Free Predictive Control: Introductory Algebraic Calculations, and a Comparison with HEOL and ANNs Join, Cédric Delaleau, Emmanuel Fliess, Michel Systems and Control Artificial Intelligence 49J99 I.2.8 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. |
| title | Model-Free Predictive Control: Introductory Algebraic Calculations, and a Comparison with HEOL and ANNs |
| topic | Systems and Control Artificial Intelligence 49J99 I.2.8 |
| url | https://arxiv.org/abs/2502.00443 |