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Found 33 papers, 4 papers with code
Using quantum computers in control: interval matrix properties
Quantum computing provides a powerful framework for tackling computational problems that are classically intractable.
SafEDMD: A certified learning architecture tailored to data-driven control of nonlinear dynamical systems
The Koopman operator serves as the theoretical backbone for machine learning of dynamical control systems, where the operator is heuristically approximated by extended dynamic mode decomposition (EDMD).
Koopman-based feedback design with stability guarantees
We present a method to design a state-feedback controller ensuring exponential stability for nonlinear systems using only measurement data.
Training robust and generalizable quantum models
We derive tailored, parameter-dependent Lipschitz bounds for quantum models with trainable encoding, showing that the norm of the data encoding has a crucial impact on the robustness against perturbations in the input data.
A Linear Parameter-Varying Approach to Data Predictive Control
By means of the linear parameter-varying (LPV) Fundamental Lemma, we derive novel data-driven predictive control (DPC) methods for LPV systems.
Quantum computing through the lens of control: A tutorial introduction
In particular, beyond the tutorial introduction, we provide a list of research challenges in the field of quantum computing and discuss their connections to control.
Data-Driven Min-Max MPC for Linear Systems
Designing data-driven controllers in the presence of noise is an important research problem, in particular when guarantees on stability, robustness, and constraint satisfaction are desired.
Control of bilinear systems using gain-scheduling: Stability and performance guarantees
In this paper, we present a state-feedback controller design method for bilinear systems.
Robust data-driven control for nonlinear systems using the Koopman operator
Data-driven analysis and control of dynamical systems have gained a lot of interest in recent years.
Data-driven Dissipativity Analysis of Linear Parameter-Varying Systems
We derive direct data-driven dissipativity analysis methods for Linear Parameter-Varying (LPV) systems using a single sequence of input-scheduling-output data.
Sequential learning and control: Targeted exploration for robust performance
This provides an a priori upper bound on the remaining model uncertainty after exploration, which can further be leveraged in a gain-scheduling controller design that guarantees robust performance.
Linear Data-Driven Economic MPC with Generalized Terminal Constraint
By employing a generalized terminal constraint with artificial equilibrium, the scheme does not require prior knowledge of the optimal equilibrium.
Data-driven Nonlinear Predictive Control for Feedback Linearizable Systems
We present a data-driven nonlinear predictive control approach for the class of discrete-time multi-input multi-output feedback linearizable nonlinear systems.
Stability in data-driven MPC: an inherent robustness perspective
Moreover, we discuss how the presented proof technique allows to show closed-loop stability of a variety of DD-MPC schemes with noisy data, as long as the corresponding model-based MPC is inherently robust.
A novel constraint tightening approach for robust data-driven predictive control
This allows the entire scheme to be set up using only a priori measured data and knowledge of an upper bound on the system order.
Data-driven distributed MPC of dynamically coupled linear systems
In this paper, we present a data-driven distributed model predictive control (MPC) scheme to stabilise the origin of dynamically coupled discrete-time linear systems subject to decoupled input constraints.
Model-Based and Data-Driven Control of Event- and Self-Triggered Discrete-Time LTI Systems
To this end, we begin by presenting a dynamic event-triggering scheme (ETS) based on periodic sampling, and a discrete-time looped-functional approach, through which a model-based stability condition is derived.
Data-driven Control of Dynamic Event-triggered Systems with Delays
Data-based representations for time-invariant linear systems with known or unknown system input matrices are first developed, along with a novel class of dynamic triggering schemes for sampled-data systems with time delays.
Linear systems with neural network nonlinearities: Improved stability analysis via acausal Zames-Falb multipliers
In this paper, we analyze the stability of feedback interconnections of a linear time-invariant system with a neural network nonlinearity in discrete time.
Improved stability conditions for systems under aperiodic sampling: model- and data-based analysis
Recently, model- and data-based stability conditions for such systems were obtained by rewriting them as an interconnection of a linear time-invariant system and a delay operator, and subsequently, performing a robust stability analysis using a known bound on the gain of this operator.
Data-Based System Analysis and Control of Flat Nonlinear Systems
Willems et al. showed that all input-output trajectories of a discrete-time linear time-invariant system can be obtained using linear combinations of time shifts of a single, persistently exciting, input-output trajectory of that system.
Data-Driven Controller Design via Finite-Horizon Dissipativity
Given one open-loop measured trajectory of a single-input single-output discrete-time linear time-invariant system, we present a framework for data-driven controller design for closed-loop finite-horizon dissipativity.
On the design of terminal ingredients for data-driven MPC
We present a model predictive control (MPC) scheme to control linear time-invariant systems using only measured input-output data and no model knowledge.
Optimization and Control Systems and Control Systems and Control
Data-driven analysis and controller design for discrete-time systems under aperiodic sampling
This article is concerned with data-driven analysis of discrete-time systems under aperiodic sampling, and in particular with a data-driven estimation of the maximum sampling interval (MSI).
Data-Driven Control of Nonlinear Systems: Beyond Polynomial Dynamics
While most existing approaches focus on systems with polynomial dynamics, our approach allows to design controllers for unknown systems with rational or general non-polynomial dynamics.
Offset-free setpoint tracking using neural network controllers
In this paper, we present a method to analyze local and global stability in offset-free setpoint tracking using neural network controllers and we provide ellipsoidal inner approximations of the corresponding region of attraction.
Combining Prior Knowledge and Data for Robust Controller Design
We present a framework for systematically combining data of an unknown linear time-invariant system with prior knowledge on the system matrices or on the uncertainty for robust controller design.
Robust and optimal predictive control of the COVID-19 outbreak
Our theoretical findings support various recent studies by showing that 1) adaptive feedback strategies are required to reliably contain the COVID-19 outbreak, 2) well-designed policies can significantly reduce the number of fatalities compared to simpler ones while keeping the amount of social distancing measures on the same level, and 3) imposing stronger social distancing measures early on is more effective and cheaper in the long run than opening up too soon and restoring stricter measures at a later time.
Training robust neural networks using Lipschitz bounds
More specifically, we design an optimization scheme based on the Alternating Direction Method of Multipliers that minimizes not only the training loss of an NN but also its Lipschitz constant resulting in a semidefinite programming based training procedure that promotes robustness.
Robust Dual Control based on Gain Scheduling
We present a novel strategy for robust dual control of linear time-invariant systems based on gain scheduling with performance guarantees.
Robust Constraint Satisfaction in Data-Driven MPC
We propose a purely data-driven model predictive control (MPC) scheme to control unknown linear time-invariant systems with guarantees on stability and constraint satisfaction in the presence of noisy data.
Data-Driven Tracking MPC for Changing Setpoints
We propose a data-driven tracking model predictive control (MPC) scheme to control unknown discrete-time linear time-invariant systems.
Data-Driven Model Predictive Control with Stability and Robustness Guarantees
First, we prove exponential stability of a nominal data-driven MPC scheme with terminal equality constraints in the case of no measurement noise.
