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Found 72 papers, 6 papers with code
Bootstrapping Guarantees: Stability and Performance Analysis for Dynamic Encrypted Control
By recognizing the bootstrapping errors occurring in the controller's state as an uncertainty in the robust control framework, we can provide stability and performance guarantees for the whole encrypted control system.
Collision Avoidance Safety Filter for an Autonomous E-Scooter using Ultrasonic Sensors
In this paper, we propose a collision avoidance safety filter for autonomous electric scooters to enable safe operation of such vehicles in pedestrian areas.
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).
Novel Quadratic Constraints for Extending LipSDP beyond Slope-Restricted Activations
However, a direct application of LipSDP to the resultant residual ReLU networks is conservative and even fails in recovering the well-known fact that the MaxMin activation is 1-Lipschitz.
Towards non-stochastic targeted exploration
We present a novel targeted exploration strategy for linear time-invariant systems without stochastic assumptions on the noise, i. e., without requiring independence or zero mean, allowing for deterministic model misspecifications.
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.
Decrypting Nonlinearity: Koopman Interpretation and Analysis of Cryptosystems
Our results on the required lifting dimension are in line with the intractability of brute-force attacks.
Performance implications of different $p$-norms in level-triggered sampling
We therefore demonstrate that the performance degradation found in the distributed setting originates from the triggering rule and not from the considered cooperative control goal.
Analysis and design of model predictive control frameworks for dynamic operation -- An overview
This article provides an overview of model predictive control (MPC) frameworks for dynamic operation of nonlinear constrained systems.
On $\ell_2$-performance of weakly-hard real-time control systems
This paper considers control systems with failures in the feedback channel, that occasionally lead to loss of the control input signal.
Stochastic Model Predictive Control using Initial State and Variance Interpolation
We present a Stochastic Model Predictive Control (SMPC) framework for linear systems subject to Gaussian disturbances.
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.
Distributed Model Predictive Control for Periodic Cooperation of Multi-Agent Systems
We show that under suitable assumptions, the agents can incrementally move their artificial output trajectories towards the cooperative goal, and, hence, their closed-loop output trajectories asymptotically achieve it.
Self-triggered output feedback control for nonlinear networked control systems based on hybrid Lyapunov functions
Most approaches for self-triggered control (STC) of nonlinear networked control systems (NCS) require measurements of the full system state to determine transmission times.
Time- versus Event-Triggered Consensus of a Single-Integrator Multi-Agent System
Inspired by existing performance analyses for the single-loop case, we provide a first fundamental performance comparison of time- and event-triggered control in a distributed multi-agent consensus setting.
Lipschitz-bounded 1D convolutional neural networks using the Cayley transform and the controllability Gramian
We establish a layer-wise parameterization for 1D convolutional neural networks (CNNs) with built-in end-to-end robustness guarantees.
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.
Convolutional Neural Networks as 2-D systems
This paper introduces a novel representation of convolutional Neural Networks (CNNs) in terms of 2-D dynamical systems.
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.
Lipschitz constant estimation for 1D convolutional neural networks
In this work, we propose a dissipativity-based method for Lipschitz constant estimation of 1D convolutional neural networks (CNNs).
Koopman interpretation and analysis of a public-key cryptosystem: Diffie-Hellman key exchange
In this paper, we introduce a new perspective on cryptosystems by interpreting the Diffie-Hellman key exchange as a nonlinear dynamical system.
Robust peak-to-peak gain analysis using integral quadratic constraints
This work provides a framework to compute an upper bound on the robust peak-to-peak gain of discrete-time uncertain linear systems using integral quadratic constraints (IQCs).
Shared Network Effects in Time- versus Event-Triggered Consensus of a Single-Integrator Multi-Agent System
While this motivation is commonly used also for multi-agent systems, a theoretical analysis of the impact of network effects on the performance of event- and time-triggered control for such distributed systems is currently missing.
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.
Distributed MPC for Self-Organized Cooperation of Multi-Agent Systems
We present a sequential distributed model predictive control (MPC) scheme for cooperative control of multi-agent systems with dynamically decoupled heterogeneous nonlinear agents subject to individual constraints.
Data-Driven Control of Distributed Event-Triggered Network Systems
The present paper deals with data-driven event-triggered control of a class of unknown discrete-time interconnected systems (a. k. a.
Analysis of Time- versus Event-Triggered Consensus for a Single-Integrator Multi-Agent System
Contrary to the non-cooperative setting, we prove that event-triggered control performs worse than time-triggered control beyond a certain number of agents in this setup.
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.
Linearly discounted economic MPC without terminal conditions for periodic optimal operation
Under standard dissipativity and controllability assumptions, we can prove that the resulting linearly discounted economic MPC without terminal conditions achieves optimal asymptotic average performance up to an error that vanishes with growing prediction horizons.
Bounding the difference between model predictive control and neural networks
There is a growing debate on whether the future of feedback control systems will be dominated by data-driven or model-driven approaches.
Self-triggered MPC robust to bounded packet loss via a min-max approach: extended version
Networked Control Systems typically come with a limited communication bandwidth and thus require special care when designing the underlying control and triggering law.
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.
Stochastic Model Predictive Control using Initial State Optimization
We propose a stochastic MPC scheme using an optimization over the initial state for the predicted trajectory.
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.
Dynamic self-triggered control for nonlinear systems with delays
Self-triggered control (STC) is a resource efficient approach to determine sampling instants for Networked Control Systems (NCS).
Neural network training under semidefinite constraints
This paper is concerned with the training of neural networks (NNs) under semidefinite constraints, which allows for NN training with robustness and stability guarantees.
Robust dynamic self-triggered control for nonlinear systems using hybrid Lyapunov functions
In the framework, a dynamic variable is used in addition to current state information to determine the next sampling instant, rendering the STC mechanism dynamic.
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.
Dynamic self-triggered control for nonlinear systems based on hybrid Lyapunov functions
In this paper, a dynamic STC mechanism for nonlinear systems is proposed.
Data-driven system analysis of nonlinear systems using polynomial approximation
To tackle these drawbacks, we establish a polynomial representation of nonlinear functions based on a polynomial sector by Taylor's theorem and a set-membership for Taylor polynomials.
Uncertainties and output feedback in rollout event-triggered control
Rollout ETC addresses this issue by using a triggering and control law that is implicitly defined by the solution to an optimal control problem (OCP), instead of an explicit one as in classical ETC.
Rollout event-triggered control: reconciling event- and time-triggered control
Event-triggered control (ETC) and time-triggered control (TTC), the classical concepts to determine the transmission instants for networked control systems, each come with drawbacks: It is difficult to tune ETC such that a certain bandwidth is respected, whereas TTC cannot adapt the sampling interval to the current state of the control system.
Data-Driven Reachability Analysis from Noisy Data
We consider the problem of computing reachable sets directly from noisy data without a given system model.
Robust output feedback model predictive control using online estimation bounds
Robust constraint satisfaction is guaranteed by suitably incorporating these online validated bounds on the estimation error in a homothetic tube based MPC formulation.
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.
Multi-party computation enables secure polynomial control based solely on secret-sharing
As homomorphic encryptions are much more computationally demanding than secret sharing, they make up for a tremendous amount of the overall computational demand of this scheme.
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-driven inference on optimal input-output properties of polynomial systems with focus on nonlinearity measures
In the context of dynamical systems, nonlinearity measures quantify the strength of nonlinearity by means of the distance of their input-output behaviour to a set of linear input-output mappings.
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.
Transient Performance of Tube-based Robust Economic Model Predictive Control
In this paper, we provide non-averaged and transient performance guarantees for recently developed, tube-based robust economic model predictive control (MPC) schemes.
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
Stability and performance in MPC using a finite-tail cost
In this paper, we provide a stability and performance analysis of model predictive control (MPC) schemes based on finite-tail costs.
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).
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.
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.
Data-Driven Reachability Analysis Using Matrix Zonotopes
In this paper, we propose a data-driven reachability analysis approach for unknown system dynamics.
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.
A Simple Approach to Increase the Maximum Allowable Transmission Interval
When designing Networked Control Systems (NCS), the maximum allowable transmission interval (MATI) is an important quantity, as it provides the admissible time between two transmission instants.
Constrained nonlinear output regulation using model predictive control -- extended version
The paper also contains novel results for MPC without terminal constraints with positive semidefinite input/output stage costs that are of independent interest.
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.
Safe and Fast Tracking on a Robot Manipulator: Robust MPC and Neural Network Control
Fast feedback control and safety guarantees are essential in modern robotics.
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.
Model-Free Practical Cooperative Control for Diffusively Coupled Systems
In this paper, we develop a data-based controller design framework for diffusively coupled systems with guaranteed convergence to an $\epsilon$-neighborhood of the desired formation.
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.
Learning an Approximate Model Predictive Controller with Guarantees
A supervised learning framework is proposed to approximate a model predictive controller (MPC) with reduced computational complexity and guarantees on stability and constraint satisfaction.
