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Tractable Data-Driven Model Predictive Control Using One-Step Neural Networks Predictors
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DOI：10.1109/TASE.2024.3453668.png)
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Abstract
En 中文
Model Predictive Control (MPC) is a popular control strategy that relies on the availability of a prediction model to estimate future system trajectories over a finite time horizon. Recently, researchers have introduced Neural Networks (NNs) into the MPC framework for the development of data-driven prediction models. In MPC, the control actions are computed by solving iteratively, at each time-step, an optimization problem subject to state and input constraints. Finding the optimal solution to such a problem is a crucial challenge in the data-driven setting, due to the complexity and black-box nature of data-driven models such as NNs. This paper addresses this challenge by proposing a hierarchical deep NN formed by a set of cascading one-step NN predictors whose combination constitutes an interpretable prediction model over the entire prediction horizon. Thanks to the proposed NN architecture, it is shown that the resulting optimal control problem is tractable, as it can be solved by employing efficient iterative algorithms, and interpretable, so that input and state constraints can be enforced seamlessly. The effectiveness of the proposed method is validated through numerical simulations.
Keywords:
Artificial neural networks
Predictive models
Optimization
Predictive control
Computational modeling
Heuristic algorithms
Computer architecture
Model predictive control
deep neural networks
mixed-integer convex programming
data-driven control
Journal
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6.4
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4.9K
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1.6W