Deep Learning Explicit Differentiable Predictive Control Laws for Buildings
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Deep LearningModel Predictive ControlSimilar Papers 제목 키워드 기반
Benchmarking Model Predictive Control Algorithms in Building Optimization Testing Framework (BOPTEST)
We present a data-driven modeling and control framework for physics-based building emulators. Our approach consists of: (a) Offline training of differentiable surrogate models that accelerate model evaluations, provide c…
BenchmarkingModel Predictive ControlZero-Shot Function Encoder-Based Differentiable Predictive Control
We introduce a differentiable framework for zero-shot adaptive control over parametric families of nonlinear dynamical systems. Our approach integrates a function encoder-based neural ODE (FE-NODE) for modeling system dy…
Self-Supervised LearningDifferentiable Predictive Control with Safety Guarantees: A Control Barrier Function Approach
We develop a novel form of differentiable predictive control (DPC) with safety and robustness guarantees based on control barrier functions. DPC is an unsupervised learning-based method for obtaining approximate solution…
FormModel Predictive ControlLearning explicit predictive controllers: theory and applications
In this paper, we deal with data-driven predictive control of linear time-invariant (LTI) systems. Specifically, we show for the first time how explicit predictive laws can be learnt directly from data, without needing t…
LEMMASelf-Excitation: An Enabler for Online Thermal Estimation and Model Predictive Control of Buildings
This paper investigates a method to improve buildings' thermal predictive control performance via online identification and excitation (active learning process) that minimally disrupts normal operations. In previous stud…
Active LearningModel Predictive Control