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Explicit machine learning-based model predictive control of nonlinear processes via multi-parametric programming
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DOI：10.1016/j.compchemeng.2024.108689.png)
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Machine learning-based model predictive control (ML-MPC) has been developed to control nonlinear processes with unknown first-principles models. While ML models can capture nonlinear dynamics of complex systems, the complexity of ML models leads to increased computation time for real-time implementation of ML-MPC. To address this issue, in this work, we propose an explicit ML-MPC framework for nonlinear processes using multi-parametric programming. Specifically, a self-adaptive approximation algorithm is first developed to obtain a piecewise linear affine function that approximates the behaviors of ML models. Then, multi- parametric quadratic programming (mpQP) problems are formulated to generate the solution map for states in discretized state-space. Furthermore, to accelerate the implementation of explicit ML-MPC, a neighbor- first search algorithm is developed. Finally, an example of a chemical reactor is used to demonstrate the effectiveness of the explicit ML-MPC.
Keyword:
Machine learning
Explicit model predictive control
Multi-parametric programming
Nonlinear processes
Chemical process control
期刊
C
IF:
3.9
论文数:
8.1K
被引数:
1.7W
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