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Found 86 papers, 6 papers with code
Safe Time-Varying Optimization based on Gaussian Processes with Spatio-Temporal Kernel
Evaluation on a realistic case study with gas compressors confirms that TVSafeOpt ensures safety when solving time-varying optimization problems with unknown reward and safety functions.
Online Residual Learning from Offline Experts for Pedestrian Tracking
In this paper, we consider the problem of predicting unknown targets from data.
In-situ Controller Autotuning by Bayesian Optimization for Closed-loop Feedback Control of Laser Powder Bed Fusion Process
In summary, BO presents a promising method for automatic in-layer controller tuning in LPBF, enhancing control precision and mitigating overheating in production parts.
Randomized algorithms and PAC bounds for inverse reinforcement learning in continuous spaces
This work studies discrete-time discounted Markov decision processes with continuous state and action spaces and addresses the inverse problem of inferring a cost function from observed optimal behavior.
Adaptive Learning-based Model Predictive Control for Uncertain Interconnected Systems: A Set Membership Identification Approach
Set membership identification is used in the learning phase to learn an uncertainty set that contains the coupling strength using online data.
MPC of Uncertain Nonlinear Systems with Meta-Learning for Fast Adaptation of Neural Predictive Models
The framework consists of two phases: the (offine) meta-training phase learns a aggregated NSSM using data from source systems, and the (online) meta-inference phase quickly adapts this aggregated model to the target system using only a few data points and few online training iterations, based on local loss function gradients.
On the Regret of Recursive Methods for Discrete-Time Adaptive Control with Matched Uncertainty
Continuous-time adaptive controllers for systems with a matched uncertainty often comprise an online parameter estimator and a corresponding parameterized controller to cancel the uncertainty.
Finite Sample Frequency Domain Identification
The error rate is of the order of $\mathcal{O}((d_{\mathrm{u}}+\sqrt{d_{\mathrm{u}}d_{\mathrm{y}}})\sqrt{M/N_{\mathrm{tot}}})$, where $N_{\mathrm{tot}}$ is the total number of samples, $M$ is the number of desired frequencies, and $d_{\mathrm{u}},\, d_{\mathrm{y}}$ are the dimensions of the input and output signals respectively.
Adaptive Economic Model Predictive Control for linear systems with performance guarantees
We present a model predictive control (MPC) formulation to directly optimize economic criteria for linear constrained systems subject to disturbances and uncertain model parameters.
Guided Bayesian Optimization: Data-Efficient Controller Tuning with Digital Twin
This article presents the guided Bayesian optimization algorithm as an efficient data-driven method for iteratively tuning closed-loop controller parameters using an event-triggered digital twin of the system based on available closed-loop data.
Force Controlled Printing for Material Extrusion Additive Manufacturing
In material extrusion additive manufacturing, the extrusion process is commonly controlled in a feed-forward fashion.
Data-Enabled Predictive Iterative Control
This work introduces the Data-Enabled Predictive iteRative Control (DeePRC) algorithm, a direct data-driven approach for iterative LTI systems.
Control Strategies for Recommendation Systems in Social Networks
A closed-loop control model to analyze the impact of recommendation systems on opinion dynamics within social networks is introduced.
Online Identification of Stochastic Continuous-Time Wiener Models Using Sampled Data
It is well known that ignoring the presence of stochastic disturbances in the identification of stochastic Wiener models leads to asymptotically biased estimators.
Closed-loop Performance Optimization of Model Predictive Control with Robustness Guarantees
Model mismatch and process noise are two frequently occurring phenomena that can drastically affect the performance of model predictive control (MPC) in practical applications.
Predictive Linear Online Tracking for Unknown Targets
The learned model is used in the optimal policy under the framework of receding horizon control.
Towards a Systems Theory of Algorithms
Traditionally, numerical algorithms are seen as isolated pieces of code confined to an {\em in silico} existence.
Closed-Loop Finite-Time Analysis of Suboptimal Online Control
Finite-time guarantees allow the control design to distribute a limited computational budget over a time horizon and estimate the on-the-go loss in performance due to sub-optimality.
Data-Driven Robust Reinforcement Learning Control of Uncertain Nonlinear Systems: Towards a Fully-Automated, Insulin-Based Artificial Pancreas
By solving a parameterized optimal tracking control problem subject to the unknown nominal system and a suitable cost function, the resulting optimal tracking control policy can ensure closed-loop stability by achieving a sufficiently small tracking error for the original uncertain nonlinear system.
Layer-to-Layer Melt Pool Control in Laser Powder Bed Fusion
Additive manufacturing processes are flexible and efficient technologies for producing complex geometries.
Urban traffic congestion control: a DeePC change
Urban traffic congestion remains a pressing challenge in our rapidly expanding cities, despite the abundance of available data and the efforts of policymakers.
Multi-Step Optimal Tracking Control of Unknown Nonzero-Sum Games based on Least Squares and Linear Programming: An Application to a Fully-Automated, Dual-Hormone Artificial Pancreas
We consider the problem of optimal tracking control of unknown discrete-time nonlinear nonzero-sum games.
Data-driven optimal control via linear programming: boundedness guarantees
The linear programming (LP) approach is, together with value iteration and policy iteration, one of the three fundamental methods to solve optimal control problems in a dynamic programming setting.
Experimental Validation for Distributed Control of Energy Hubs
As future energy systems become more decentralised due to the integration of renewable energy resources and storage technologies, several autonomous energy management and peer-to-peer trading mechanisms have been recently proposed for the operation of energy hub networks based on optimization and game theory.
Efficient safe learning for controller tuning with experimental validation
The performance of the new method is first validated in a simulated precision motion system, demonstrating improved computational efficiency, and illustrating the role of exploiting numerical solvers to reach the desired precision.
Routing and charging game in ride-hailing service with electric vehicles
This paper studies the routing and charging behaviors of electric vehicles in a competitive ride-hailing market.
Sequential Quadratic Programming-based Iterative Learning Control for Nonlinear Systems
We present simulations to illustrate the performance of the proposed method for linear and nonlinear dynamics models.
Degradation-aware data-enabled predictive control of energy hubs
Mitigating the energy use in buildings, together with satisfaction of comfort requirements are the main objectives of efficient building control systems.
On the Guarantees of Minimizing Regret in Receding Horizon
Towards bridging classical optimal control and online learning, regret minimization has recently been proposed as a control design criterion.
On the Finite-Time Behavior of Suboptimal Linear Model Predictive Control
Inexact methods for model predictive control (MPC), such as real-time iterative schemes or time-distributed optimization, alleviate the computational burden of exact MPC by providing suboptimal solutions.
Recursive Dynamic State Estimation for Power Systems with an Incomplete Nonlinear DAE Model
Power systems are highly complex, large-scale engineering systems subject to many uncertainties, which makes accurate mathematical modeling challenging.
Regret Optimal Control for Uncertain Stochastic Systems
Specifically, we focus on the problem of designing a feedback controller that minimizes the loss relative to a clairvoyant optimal policy that has foreknowledge of both the system dynamics and the exogenous disturbances.
Distributed Multi-Horizon Model Predictive Control for Network of Energy Hubs
Joint operation of such hubs can improve energy efficiency and support the integration of renewable energy resource.
Stochastic MPC for energy hubs using data driven demand forecasting
Historical data is used to build a demand prediction model based on Gaussian processes to generate a forecast of the future electricity and heat demands.
Designing Optimal Personalized Incentive for Traffic Routing using BIG Hype algorithm
We study the problem of optimally routing plug-in electric and conventional fuel vehicles on a city level.
Generalized uncertain Nash games: Reformulation and robust equilibrium seeking -- Extended version
We consider generalized Nash equilibrium problems (GNEPs) with linear coupling constraints affected by both local (i. e., agent-wise) and global (i. e., shared resources) disturbances taking values in polyhedral uncertainty sets.
Online Linear Quadratic Tracking with Regret Guarantees
Online learning algorithms for dynamical systems provide finite time guarantees for control in the presence of sequentially revealed cost functions.
Designing Fairness in Autonomous Peer-to-peer Energy Trading
Several autonomous energy management and peer-to-peer trading mechanisms for future energy markets have been recently proposed based on optimization and game theory.
Optimal service station design for traffic mitigation via genetic algorithm and neural network
We focus on the problem of optimally designing a service station to achieve beneficial effects in terms of total traffic congestion and peak traffic reduction.
Drone-based Volume Estimation in Indoor Environments
Volume estimation in large indoor spaces is an important challenge in robotic inspection of industrial warehouses.
Implications of Regret on Stability of Linear Dynamical Systems
The setting of an agent making decisions under uncertainty and under dynamic constraints is common for the fields of optimal control, reinforcement learning, and recently also for online learning.
Follow the Clairvoyant: an Imitation Learning Approach to Optimal Control
We consider control of dynamical systems through the lens of competitive analysis.
Probabilistic Reachability and Invariance Computation of Stochastic Systems using Linear Programming
We consider the safety evaluation of discrete time, stochastic systems over a finite horizon.
Stability and Robustness of Distributed Suboptimal Model Predictive Control
In distributed model predictive control (MPC), the control input at each sampling time is computed by solving a large-scale optimal control problem (OCP) over a finite horizon using distributed algorithms.
Data-Driven Process Optimization of Fused Filament Fabrication based on In Situ Measurements
The tuning of fused filament fabrication parameters is notoriously challenging.
CARMA: Fair and efficient bottleneck congestion management via non-tradable karma credits
In our CARMA scheme, the bottleneck is divided into a fast lane that is kept in free flow and a slow lane that is subject to congestion.
Moving-Horizon State Estimation for Power Networks and Synchronous Generators
Power network and generators state estimation are usually tackled as separate problems.
Online Computation of Terminal Ingredients in Distributed Model Predictive Control for Reference Tracking
A distributed model predictive control scheme is developed for tracking piecewise constant references where the terminal set is reconfigured online, whereas the terminal controller is computed offline.
Reconfigurable Plug-and-play Distributed Model Predictive Control for Reference Tracking
In the redesign phase, passivity-based control is used to ensure that asymptotic stability of the network is preserved.
Robust and Kernelized Data-Enabled Predictive Control for Nonlinear Systems
This paper presents a robust and kernelized data-enabled predictive control (RoKDeePC) algorithm to perform model-free optimal control for nonlinear systems using only input and output data.
Data-driven Reference Trajectory Optimization for Precision Motion Systems
We propose a data-driven optimization-based pre-compensation method to improve the contour tracking performance of precision motion stages by modifying the reference trajectory and without modifying any built-in low-level controllers.
Controller-Aware Dynamic Network Management for Industry 4.0
In this paper, we consider a cyber-physical manufacturing system (CPMS) scenario containing physical components (robots, sensors, and actuators), operating in a digitally connected, constrained environment to perform industrial tasks.
Advanced Manufacturing Configuration by Sample-efficient Batch Bayesian Optimization
We propose a framework for the configuration and operation of expensive-to-evaluate advanced manufacturing methods, based on Bayesian optimization.
A Novel Control-Oriented Cell Transmission Model Including Service Stations on Highways
In this paper, we propose a novel model that describes how the traffic evolution on a highway stretch is affected by the presence of a service station.
A Stackelberg game for incentive-based demand response in energy markets
In modern buildings renewable energy generators and storage devices are spreading, and consequently the role of the users in the power grid is shifting from passive to active.
Regret Analysis of Online Gradient Descent-based Iterative Learning Control with Model Mismatch
In Iterative Learning Control (ILC), a sequence of feedforward control actions is generated at each iteration on the basis of partial model knowledge and past measurements with the goal of steering the system toward a desired reference trajectory.
Policy Iteration for Multiplicative Noise Output Feedback Control
We propose a policy iteration algorithm for solving the multiplicative noise linear quadratic output feedback design problem.
Data-Driven Optimal Control of Affine Systems: A Linear Programming Perspective
In this letter, we discuss the problem of optimal control for affine systems in the context of data-driven linear programming.
On Robustness in Optimization-Based Constrained Iterative Learning Control
Iterative learning control (ILC) is a control strategy for repetitive tasks wherein information from previous runs is leveraged to improve future performance.
Safe Control with Minimal Regret
As we move towards safety-critical cyber-physical systems that operate in non-stationary and uncertain environments, it becomes crucial to close the gap between classical optimal control algorithms and adaptive learning-based methods.
PAGE-PG: A Simple and Loopless Variance-Reduced Policy Gradient Method with Probabilistic Gradient Estimation
A numerical evaluation confirms the competitive performance of our method on classical control tasks.
Stochastic convex optimization for provably efficient apprenticeship learning
We consider large-scale Markov decision processes (MDPs) with an unknown cost function and employ stochastic convex optimization tools to address the problem of imitation learning, which consists of learning a policy from a finite set of expert demonstrations.
Efficient Performance Bounds for Primal-Dual Reinforcement Learning from Demonstrations
We consider large-scale Markov decision processes with an unknown cost function and address the problem of learning a policy from a finite set of expert demonstrations.
Learning-Based Repetitive Precision Motion Control with Mismatch Compensation
In this work, we develop an iterative approach for repetitive precision motion control problems where the objective is to follow a reference geometry with minimal tracking error.
Batch Model Predictive Control for Selective Laser Melting
Selective laser melting is a promising additive manufacturing technology enabling the fabrication of highly customizable products.
In-layer Thermal Control of a Multi-layer Selective Laser Melting Process
Selective Laser Melting (SLM) is an additive manufacturing technology that builds three dimensional parts by melting layers of metal powder together with a laser that traces out a desired geometry.
Physics-informed linear regression is competitive with two Machine Learning methods in residential building MPC
However, we also see that the physics-informed ARMAX models have a lower computational burden, and a superior sample efficiency compared to the Machine Learning based models.
A distributed framework for linear adaptive MPC
Adaptive model predictive control (MPC) robustly ensures safety while reducing uncertainty during operation.
Passivity-based Decentralized Control for Discrete-time Large-scale Systems
The controller is synthesized by locally solving a semidefinite program offline for each subsystem in a decentralized fashion.
Robust Data-Enabled Predictive Control: Tractable Formulations and Performance Guarantees
The proposed framework enables us to obtain model-free optimal control for LTI systems based on noisy input/output data.
Performance-based Trajectory Optimization for Path Following Control Using Bayesian Optimization
Accurate positioning and fast traversal times determine the productivity in machining applications.
Plasma Spray Process Parameters Configuration using Sample-efficient Batch Bayesian Optimization
Recent work has shown constrained Bayesian optimization to be a powerful technique for the optimization of industrial processes.
Safe and Efficient Model-free Adaptive Control via Bayesian Optimization
Thus, our approach builds on GoOSE, an algorithm for safe and sample-efficient Bayesian optimization.
Quadratic Regularization of Data-Enabled Predictive Control: Theory and Application to Power Converter Experiments
Moreover, when the input/output constraints are inactive, the quadratic regularization leads to a closed-form solution of the DeePC algorithm and thus enables fast calculations.
Input Convex Neural Networks for Building MPC
We assess the consequences of the additional constraints for the model accuracy and test the models in a real-life MPC experiment in an apartment in Switzerland.
Learning from Simulation, Racing in Reality
We present a reinforcement learning-based solution to autonomously race on a miniature race car platform.
Safety-Aware Cascade Controller Tuning Using Constrained Bayesian Optimization
This paper presents an automated, model-free, data-driven method for the safe tuning of PID cascade controller gains based on Bayesian optimization.
Robust MPC with data-driven demand forecasting for frequency regulation with heat pumps
By combining a control scheme based on Robust Model Predictive Control, with affine policies, and heating demand forecasting based on Artificial Neural Networks with online correction methods, we offer frequency regulation reserves and maintain user comfort with a system comprising a heat pump and buffer storage.
Distributed Model Predictive Control with Reconfigurable Terminal Ingredients for Reference Tracking
The proposed scheme is tested in simulation where the proposed MPC problem is solved using distributed optimization.
Learning solutions to hybrid control problems using Benders cuts
Hybrid control problems are complicated by the need to make a suitable sequence of discrete decisions related to future modes of operation of the system.
GPU Acceleration of ADMM for Large-Scale Quadratic Programming
The alternating direction method of multipliers (ADMM) is a powerful operator splitting technique for solving structured convex optimization problems.
Optimization and Control
Decentralized Data-Enabled Predictive Control for Power System Oscillation Damping
Further, we discuss how to relieve the computational burden of the Min-Max DeePC by reducing the dimension of prediction uncertainty and how to leverage disturbance feedback to reduce the conservativeness of robustification.
AMZ Driverless: The Full Autonomous Racing System
This paper presents the algorithms and system architecture of an autonomous racecar.
Robotics
Inference of the three-dimensional chromatin structure and its temporal behavior
In this work, we explore the idea of manifold learning for the 3D chromatin structure inference and present a novel method, REcurrent Autoencoders for CHromatin 3D structure prediction (REACH-3D).
Generalized maximum entropy estimation
We consider the problem of estimating a probability distribution that maximizes the entropy while satisfying a finite number of moment constraints, possibly corrupted by noise.
An Integrated and Scalable Platform for Proactive Event-Driven Traffic Management
Traffic on freeways can be managed by means of ramp meters from Road Traffic Control rooms.
