Deep Learning Explicit Differentiable Predictive Control Laws for Buildings
| Autor: | Draguna Vrabie, Elliott Skomski, Soumya Vasisht, Aaron Tuor, Ján Drgoňa |
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| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer science business.industry Deep learning Systems and Control (eess.SY) Electrical Engineering and Systems Science - Systems and Control Machine Learning (cs.LG) System model Constraint (information theory) Model predictive control Nonlinear system Optimization and Control (math.OC) Control and Systems Engineering Control theory Law FOS: Electrical engineering electronic engineering information engineering FOS: Mathematics Penalty method Artificial intelligence Differentiable function business Mathematics - Optimization and Control |
| Zdroj: | IFAC-PapersOnLine. 54:14-19 |
| ISSN: | 2405-8963 |
| DOI: | 10.1016/j.ifacol.2021.08.518 |
| Popis: | We present a differentiable predictive control (DPC) methodology for learning constrained control laws for unknown nonlinear systems. DPC poses an approximate solution to multiparametric programming problems emerging from explicit nonlinear model predictive control (MPC). Contrary to approximate MPC, DPC does not require supervision by an expert controller. Instead, a system dynamics model is learned from the observed system’s dynamics, and the neural control law is optimized offline by leveraging the differentiable closed-loop system model. The combination of a differentiable closed-loop system and penalty methods for constraint handling of system outputs and inputs allows us to optimize the control law’s parameters directly by backpropagating economic MPC loss through the learned system model. The control performance of the proposed DPC method is demonstrated in simulation using learned model of multi-zone building thermal dynamics. |
| Databáze: | OpenAIRE |
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