Deep Learning Explicit Differentiable Predictive Control Laws for Buildings

• URL: http://arxiv.org/abs/2107.11843v1
• Date: Sun, 25 Jul 2021 16:47:57 GMT
• Title: Deep Learning Explicit Differentiable Predictive Control Laws for Buildings
• Authors: Jan Drgona, Aaron Tuor, Soumya Vasisht, Elliott Skomski and Draguna Vrabie
• Abstract summary: 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)
• Score: 1.4121977037543585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Dropout MPC: An Ensemble Neural MPC Approach for Systems with Learned Dynamics [0.0]
  We propose a novel sampling-based ensemble neural MPC algorithm that employs the Monte-Carlo dropout technique on the learned system model. The method aims in general at uncertain systems with complex dynamics, where models derived from first principles are hard to infer.
  arXiv  Detail & Related papers  (2024-06-04T17:15:25Z)
• Parameter-Adaptive Approximate MPC: Tuning Neural-Network Controllers without Retraining [50.00291020618743]
  This work introduces a novel, parameter-adaptive AMPC architecture capable of online tuning without recomputing large datasets and retraining. We showcase the effectiveness of parameter-adaptive AMPC by controlling the swing-ups of two different real cartpole systems with a severely resource-constrained microcontroller (MCU) Taken together, these contributions represent a marked step toward the practical application of AMPC in real-world systems.
  arXiv  Detail & Related papers  (2024-04-08T20:02:19Z)
• Data-driven Nonlinear Model Reduction using Koopman Theory: Integrated Control Form and NMPC Case Study [56.283944756315066]
  We propose generic model structures combining delay-coordinate encoding of measurements and full-state decoding to integrate reduced Koopman modeling and state estimation. A case study demonstrates that our approach provides accurate control models and enables real-time capable nonlinear model predictive control of a high-purity cryogenic distillation column.
  arXiv  Detail & Related papers  (2024-01-09T11:54:54Z)
• Multirotor Ensemble Model Predictive Control I: Simulation Experiments [0.0]
  An ensemble-represented Gaussian process performs the backward calculations to determine optimal gains for the initial time. We construct the EMPC for terminal control and regulation problems and apply it to the control of a simulated, identical-twin study.
  arXiv  Detail & Related papers  (2023-05-22T01:32:17Z)
• Learning Stochastic Parametric Differentiable Predictive Control Policies [2.042924346801313]
  We present a scalable alternative called parametric differentiable predictive control (SP-DPC) for unsupervised learning of neural control policies. SP-DPC is formulated as a deterministic approximation to the parametric constrained optimal control problem. We provide theoretical probabilistic guarantees for policies learned via the SP-DPC method on closed-loop constraints and chance satisfaction.
  arXiv  Detail & Related papers  (2022-03-02T22:46:32Z)
• Sparsity in Partially Controllable Linear Systems [56.142264865866636]
  We study partially controllable linear dynamical systems specified by an underlying sparsity pattern. Our results characterize those state variables which are irrelevant for optimal control.
  arXiv  Detail & Related papers  (2021-10-12T16:41:47Z)
• Approximate Robust NMPC using Reinforcement Learning [0.0]
  We present a Reinforcement Learning-based Robust Model Predictive Control (RL-RNMPC) for controlling nonlinear systems in the presence of disturbances and uncertainties.
  arXiv  Detail & Related papers  (2021-04-06T18:34:58Z)
• Anticipating the Long-Term Effect of Online Learning in Control [75.6527644813815]
  AntLer is a design algorithm for learning-based control laws that anticipates learning. We show that AntLer approximates an optimal solution arbitrarily accurately with probability one.
  arXiv  Detail & Related papers  (2020-07-24T07:00:14Z)
• Learning Constrained Adaptive Differentiable Predictive Control Policies With Guarantees [1.1086440815804224]
  We present differentiable predictive control (DPC), a method for learning constrained neural control policies for linear systems. We employ automatic differentiation to obtain direct policy gradients by backpropagating the model predictive control (MPC) loss function and constraints penalties through a differentiable closed-loop system dynamics model.
  arXiv  Detail & Related papers  (2020-04-23T14:24:44Z)
• Adaptive Control and Regret Minimization in Linear Quadratic Gaussian (LQG) Setting [91.43582419264763]
  We propose LqgOpt, a novel reinforcement learning algorithm based on the principle of optimism in the face of uncertainty. LqgOpt efficiently explores the system dynamics, estimates the model parameters up to their confidence interval, and deploys the controller of the most optimistic model.
  arXiv  Detail & Related papers  (2020-03-12T19:56:38Z)
• Information Theoretic Model Predictive Q-Learning [64.74041985237105]
  We present a novel theoretical connection between information theoretic MPC and entropy regularized RL. We develop a Q-learning algorithm that can leverage biased models.
  arXiv  Detail & Related papers  (2019-12-31T00:29:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.