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Found 34 papers, 16 papers with code
A PAC-Bayesian Framework for Optimal Control with Stability Guarantees
Based on these bounds, we propose a new method for designing optimal controllers, offering a principled way to incorporate prior knowledge into the synthesis process, which aids in improving the control policy and mitigating overfitting.
Neural Distributed Controllers with Port-Hamiltonian Structures
Controlling large-scale cyber-physical systems necessitates optimal distributed policies, relying solely on local real-time data and limited communication with neighboring agents.
Neural Exponential Stabilization of Control-affine Nonlinear Systems
Third, this parametrization and the inequality condition enable the design of contractivity-enforcing regularizers, which can be incorporated while designing the NN controller for exponential stabilization of the underlying nonlinear systems.
Moving horizon partition-based state estimation of large-scale systems -- Revised version
This report presents three Moving Horizon Estimation (MHE) methods for discrete-time partitioned linear systems, i. e. systems decomposed into coupled subsystems with non-overlapping states.
Power Grid Parameter Estimation Without Phase Measurements: Theory and Empirical Validation
The primary result of the paper is that, while the impedance of a line or a network can be estimated without synchronized phase angle measurements in a consistent way, the admittance cannot.
Unconstrained learning of networked nonlinear systems via free parametrization of stable interconnected operators
Further, we can embed prior knowledge about the interconnection topology and stability properties of the system directly into the large-scale distributed operator we design.
A Coverage Control-based Idle Vehicle Rebalancing Approach for Autonomous Mobility-on-Demand Systems
As an emerging mode of urban transportation, Autonomous Mobility-on-Demand (AMoD) systems show the potential in improving mobility in cities through timely and door-to-door services.
Stable Linear Subspace Identification: A Machine Learning Approach
Machine Learning (ML) and linear System Identification (SI) have been historically developed independently.
Closing the Gap to Quadratic Invariance: a Regret Minimization Approach to Optimal Distributed Control
In this work, we focus on the design of optimal controllers that must comply with an information structure.
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.
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.
Regularization for distributionally robust state estimation and prediction
We provide a direct method using samples of the noise to create a moving horizon observer for linear time-varying and nonlinear systems, which is optimal under the empirical noise distribution.
Unconstrained Parametrization of Dissipative and Contracting Neural Ordinary Differential Equations
We validate the properties of NodeRENs, including the possibility of handling irregularly sampled data, in a case study in nonlinear system identification.
Universal Approximation Property of Hamiltonian Deep Neural Networks
This paper investigates the universal approximation capabilities of Hamiltonian Deep Neural Networks (HDNNs) that arise from the discretization of Hamiltonian Neural Ordinary Differential Equations.
Minimal regret state estimation of time-varying systems
Kalman and H-infinity filters, the most popular paradigms for linear state estimation, are designed for very specific specific noise and disturbance patterns, which may not appear in practice.
Follow the Clairvoyant: an Imitation Learning Approach to Optimal Control
We consider control of dynamical systems through the lens of competitive analysis.
Maximum likelihood estimation of distribution grid topology and parameters from smart meter data
Not measuring the voltage phase only adds 30\% of error to the admittance matrix estimate in realistic conditions.
Robust online joint state/input/parameter estimation of linear systems
This paper presents a method for jointly estimating the state, input, and parameters of linear systems in an online fashion.
Neural System Level Synthesis: Learning over All Stabilizing Policies for Nonlinear Systems
We address the problem of designing stabilizing control policies for nonlinear systems in discrete-time, while minimizing an arbitrary cost function.
Robust Classification using Contractive Hamiltonian Neural ODEs
Since in NODEs the input data corresponds to the initial condition of dynamical systems, we show contractivity can mitigate the effect of input perturbations.
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.
Distributed neural network control with dependability guarantees: a compositional port-Hamiltonian approach
A main challenge of NN controllers is that they are not dependable during and after training, that is, the closed-loop system may be unstable, and the training may fail due to vanishing and exploding gradients.
Neural Energy Casimir Control for Port-Hamiltonian Systems
The energy Casimir method is an effective controller design approach to stabilize port-Hamiltonian systems at a desired equilibrium.
Non Vanishing Gradients for Arbitrarily Deep Neural Networks: a Hamiltonian System Approach
Deep Neural Networks (DNNs) training can be difficult due to vanishing or exploding gradients during weight optimization through backpropagation.
Bayesian Error-in-Variables Models for the Identification of Power Networks
The increasing integration of intermittent renewable generation, especially at the distribution level, necessitates advanced planning and optimisation methodologies contingent on the knowledge of thegrid, specifically the admittance matrix capturing the topology and line parameters of an electricnetwork.
Hamiltonian Deep Neural Networks Guaranteeing Non-vanishing Gradients by Design
Deep Neural Networks (DNNs) training can be difficult due to vanishing and exploding gradients during weight optimization through backpropagation.
Near-Optimal Design of Safe Output Feedback Controllers from Noisy Data
As we transition towards the deployment of data-driven controllers for black-box cyberphysical systems, complying with hard safety constraints becomes a primary concern.
Data-driven Unknown-input Observers and State Estimation
Unknown-input observers (UIOs) allow for estimation of the states of an LTI system without knowledge of all inputs.
A Unified Passivity-Based Framework for Control of Modular Islanded AC Microgrids
Voltage and frequency control in an islanded AC microgrid (ImGs) amount to stabilizing an a priori unknown ImG equilibrium induced by loads and changes in topology.
Finite-sample-based Spectral Radius Estimation and Stabilizability Test for Networked Control Systems
In the analysis and control of discrete-time linear time-invariant systems, the spectral radius of the system state matrix plays an essential role.
Optimization and Control Systems and Control Systems and Control
A Behavioral Input-Output Parametrization of Control Policies with Suboptimality Guarantees
Recent work in data-driven control has revived behavioral theory to perform a variety of complex control tasks, by directly plugging libraries of past input-output trajectories into optimal control problems.
On Consensusability of Linear Interconnected Multi-Agent Systems and Simultaneous Stabilization
Consensusability of multi-agent systems (MASs) certifies the existence of a distributed controller capable of driving the states of each subsystem to a consensus value.
Identification of AC Networks via Online Learning
The increasing penetration of intermittent distributed energy resources in power networks calls for novel planning and control methodologies which hinge on detailed knowledge of the grid.
Hierarchical Control in Islanded DC Microgrids with Flexible Structures
Hierarchical architectures stacking primary, secondary, and tertiary layers are widely employed for the operation and control of islanded DC microgrids (DCmGs), composed of Distribution Generation Units (DGUs), loads, and power lines.
