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Computationally efficient solution of mixed integer model predictive control problems via machine learning aided Benders Decomposition
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DOI：10.1016/j.jprocont.2024.103207.png)
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Mixed integer Model Predictive Control (MPC) problems arise in the operation of systems where discrete and continuous decisions must be taken simultaneously to compensate for disturbances. The efficient solution of mixed integer MPC problems requires the computationally efficient online solution of mixed integer optimization problems, which are generally difficult to solve. In this paper, we propose a machine learningbased branch and check Generalized Benders Decomposition algorithm for the efficient solution of such problems. We use machine learning to approximate the effect of the complicating variables on the subproblem by approximating the Benders cuts without solving the subproblem, therefore, alleviating the need to solve the subproblem multiple times. The proposed approach is applied to a mixed integer economic MPC case study on the operation of chemical processes. We show that the proposed algorithm always finds feasible solutions to the optimization problem, given that the mixed integer MPC problem is feasible, and leads to a significant reduction in solution time (up to 97% or 50x ) while incurring small error (in the order of 1%) compared to the application of standard and accelerated Generalized Benders Decomposition.
Keyword:
Benders decomposition
Machine learning
Mixed integer MPC
Mixed integer optimization
Hybrid systems
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Explicit/multi-parametric model predictive control (MPC) of linear discrete-time systems by dynamic and multi-parametric programming通过动态和多参数编程对线性离散时间系统进行显式/多参数模型预测控制 (MPC).png)
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