Dual dynamic programming for multi-scale mixed-integer MPC

• DOI: 10.1016/j.compchemeng.2021.107265
• Publication Date: 2021-02-27T09:15:26Z
• Authors: Ranjeet Kumar; Michael J. Wenzel; Mohammad N. ElBsat; Michael J. Risbeck; Kirk H. Drees; Victor M. Zavala
• Keywords: Model Predictive Control; Time horizon

We propose a dual dynamic integer programming (DDIP) framework for solving multi-scale mixed-integer model predictive control (MPC) problems. Such problems arise in applications that involve long horizons and/or fine temporal discretizations as well as mixed-integer states and controls (e.g., scheduling logic and discrete actuators). The approach uses a nested cutting-plane scheme that performs forward and backward sweeps along the time horizon to adaptively approximate cost-to-go functions. The DDIP scheme proposed can handle general MPC formulations with mixed-integer controls and states and can perform forward-backward sweeps over block time partitions. We demonstrate the performance of the proposed scheme by solving mixed-integer MPC problems that arise in the scheduling of central heating, ventilation, and air-conditioning (HVAC) plants. We show that the proposed scheme is scalable and dramatically outperforms state-of-the-art mixed-integer solvers.<br/>32 pages, 13 figures. arXiv admin note: text overlap with arXiv:2002.10065<br/>