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Found 36 papers, 13 papers with code
Using Flexibility Envelopes for the Demand-Side Hierarchical Optimization of District Heating Networks
The demand-side control of district heating networks is notoriously challenging due to the large number of connected users and the high number of states to be considered.
Principled Preferential Bayesian Optimization
We study the problem of preferential Bayesian optimization (BO), where we aim to optimize a black-box function with only preference feedback over a pair of candidate solutions.
SIMBa: System Identification Methods leveraging Backpropagation
This manuscript details the SIMBa toolbox (System Identification Methods leveraging Backpropagation), which uses well-established Machine Learning tools for discrete-time linear multi-step-ahead state-space System Identification (SI).
Risk-aware Scheduling and Dispatch of Flexibility Events in Buildings
Residential and commercial buildings, equipped with systems such as heat pumps, hot water tanks, or stationary energy storage, have a large potential to offer their consumption flexibility as grid services.
Stable Linear Subspace Identification: A Machine Learning Approach
Machine Learning (ML) and linear System Identification (SI) have been historically developed independently.
Multi-Agent Bayesian Optimization with Coupled Black-Box and Affine Constraints
Additionally, the algorithm guarantees an $\mathcal{O}(N\sqrt{T})$ bound on the cumulative violation for the known affine constraints, where $N$ is the number of agents.
Efficient Recursive Data-enabled Predictive Control (Extended Version)
This is exemplified through a comparison to Subspace Predictive Control, where the algorithm achieves asymptotically consistent prediction for stochastic linear time-invariant systems.
A Generalized Stopping Criterion for Real-Time MPC with Guaranteed Stability
Most of the real-time implementations of the stabilizing optimal control actions suffer from the necessity to provide high computational effort.
Advancing Distributed AC Optimal Power Flow for Integrated Transmission-Distribution Systems
This paper introduces a distributed operational solution for coordinating integrated transmission-distribution (ITD) systems regarding data privacy.
Adaptive Data-Driven Prediction in a Building Control Hierarchy: A Case Study of Demand Response in Switzerland
By providing various services, such as Demand Response (DR), buildings can play a crucial role in the energy market due to their significant energy consumption.
Bayesian Optimization of Expensive Nested Grey-Box Functions
We consider the problem of optimizing a grey-box objective function, i. e., nested function composed of both black-box and white-box functions.
A comparison of methods to eliminate regularization weight tuning from data-enabled predictive control
Data-enabled predictive control (DeePC) is a recently established form of Model Predictive Control (MPC), based on behavioral systems theory.
Primal-Dual Contextual Bayesian Optimization for Control System Online Optimization with Time-Average Constraints
This paper studies the problem of online performance optimization of constrained closed-loop control systems, where both the objective and the constraints are unknown black-box functions affected by exogenous time-varying contextual disturbances.
Physically Consistent Multiple-Step Data-Driven Predictions Using Physics-based Filters
(Extended Version) Data-driven control can facilitate the rapid development of controllers, offering an alternative to conventional approaches.
Computationally Efficient Reinforcement Learning: Targeted Exploration leveraging Simple Rules
Model-free Reinforcement Learning (RL) generally suffers from poor sample complexity, mostly due to the need to exhaustively explore the state-action space to find well-performing policies.
CONFIG: Constrained Efficient Global Optimization for Closed-Loop Control System Optimization with Unmodeled Constraints
In this paper, the CONFIG algorithm, a simple and provably efficient constrained global optimization algorithm, is applied to optimize the closed-loop control performance of an unknown system with unmodeled constraints.
Physically Consistent Neural ODEs for Learning Multi-Physics Systems
Since IPHS models are consistent with the first and second principles of thermodynamics by design, so are the proposed Physically Consistent NODEs (PC-NODEs).
Distributed data-driven predictive control for cooperatively smoothing mixed traffic flow
This algorithm achieves both computation and communication efficiency, as well as trajectory data privacy, through parallel calculation.
Uncertainty-aware Flexibility Envelope Prediction in Buildings with Controller-agnostic Battery Models
Buildings are a promising source of flexibility for the application of demand response.
Lower Bounds on the Worst-Case Complexity of Efficient Global Optimization
In this paper, we study the worst-case complexity of the efficient global optimization problem and, in contrast to existing kernel-specific results, we derive a unified lower bound for the complexity of efficient global optimization in terms of the metric entropy of a ball in its corresponding reproducing kernel Hilbert space~(RKHS).
Stability Verification of Neural Network Controllers using Mixed-Integer Programming
We provide sufficient conditions for the closed-loop stability of the candidate policy in terms of the worst-case approximation error with respect to the baseline policy, and we show that these conditions can be checked by solving a Mixed-Integer Quadratic Program (MIQP).
A Proximal-Point Lagrangian Based Parallelizable Nonconvex Solver for Bilinear Model Predictive Control
The proposed algorithm is validated on a simulation of an HVAC system control.
Scheduling Delays and Curtailment for Household Appliances with Deterministic Load Profiles using MPC
Smart home appliances can time-shift and curtail their power demand to assist demand side management or allow operation with limited power, as in an off-grid application.
Lessons Learned from Data-Driven Building Control Experiments: Contrasting Gaussian Process-based MPC, Bilevel DeePC, and Deep Reinforcement Learning
This manuscript offers the perspective of experimentalists on a number of modern data-driven techniques: model predictive control relying on Gaussian processes, adaptive data-driven control based on behavioral theory, and deep reinforcement learning.
Over-the-Air Federated Learning via Second-Order Optimization
To address this issue, in this paper, we instead propose a novel over-the-air second-order federated optimization algorithm to simultaneously reduce the communication rounds and enable low-latency global model aggregation.
Near-optimal Deep Reinforcement Learning Policies from Data for Zone Temperature Control
Replacing poorly performing existing controllers with smarter solutions will decrease the energy intensity of the building sector.
Data-driven input reconstruction and experimental validation
This paper addresses a data-driven input reconstruction problem based on Willems' Fundamental Lemma in which unknown input estimators (UIEs) are constructed directly from historical I/O data.
Communication-Efficient Stochastic Zeroth-Order Optimization for Federated Learning
Under non-convex settings, we derive the convergence performance of the FedZO algorithm on non-independent and identically distributed data and characterize the impact of the numbers of local iterates and participating edge devices on the convergence.
Experimental Data-Driven Model Predictive Control of a Hospital HVAC System During Regular Use
Herein we report a multi-zone, heating, ventilation and air-conditioning (HVAC) control case study of an industrial plant responsible for cooling a hospital surgery center.
Physically Consistent Neural Networks for building thermal modeling: theory and analysis
To counter this known generalization issue, physics-informed NNs have recently been introduced, where researchers introduce prior knowledge in the structure of NNs to ground them in known underlying physical laws and avoid classical NN generalization issues.
Robust Resource-Aware Self-triggered Model Predictive Control
The wide adoption of wireless devices in the Internet of Things requires controllers that are able to operate with limited resources, such as battery life.
Robust Uncertainty Bounds in Reproducing Kernel Hilbert Spaces: A Convex Optimization Approach
The problem of establishing out-of-sample bounds for the values of an unkonwn ground-truth function is considered.
From System Level Synthesis to Robust Closed-loop Data-enabled Predictive Control
Inspired by this observation, a robust closed-loop data-enabled predictive control scheme is proposed for stochastic LTI systems.
Robust Learning Model Predictive Control for Periodically Correlated Building Control
Accounting for more than 40% of global energy consumption, residential and commercial buildings will be key players in any future green energy systems.
Deterministic error bounds for kernel-based learning techniques under bounded noise
We consider the problem of reconstructing a function from a finite set of noise-corrupted samples.
Reference tracking stochastic model predictive control over unreliable channels and bounded control actions
A stochastic model predictive control framework over unreliable Bernoulli communication channels, in the presence of unbounded process noise and under bounded control inputs, is presented for tracking a reference signal.
Optimization and Control
