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Sufficient Conditions for Global Boundedness of Solutions for Two Coupled Synchronverters

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Sufficient Conditions for Global Boundedness of Solutions fo...

IEEE Conference on Decision and control

作者： Angel Mercado-Uribe Jesús Mendoza-Ávila Denis Efimov Johannes Schiffer Control Systems and Network Technology Group Brandenburgische Technische Universität Cottbus-Senftenberg Inria Univ. Lille CNRS UMR 9189 – CRIStAL Lille France Control Systems and Network Technology Group Brandenburgische Technische Universität Cottbus-Senftenberg and the Fraunhofer IEG Fraunhofer Research Institution on Energy Infrastructures and Geothermal Systems IEG

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper analyzes two synchronverters connected in parallel to a common capacitive-resistive load through resistive-inductive power lines. This system is conceptualized as a microgrid with two renewable energy sources controlled using the synchronverter algorithm. It is modeled as an interconnection of three port-Hamiltonian systems, and the dq-coordinates model is derived by averaging the frequencies. Applying the recent Leonov function theory, sufficient conditions to guarantee the global boundedness of the whole system’s trajectories are provided. This is necessary to reach the global synchronization of microgrids. Additionally, a numerical example illustrates the potential resonance behavior of the microgrid.

关键词： Sufficient conditions Renewable energy sources Resonant frequency Microgrids Resonance Numerical models Trajectory Synchronization Load modeling

Stochastic MPC for Finite Gaussian Mixture Disturbances with Guarantees

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arXiv 2024年

作者： Engelaar, Maico H.W. Swaanen, M.P.P. Lazar, M. Haesaert, S. Department of Electrical Engineering Control Systems Group Eindhoven University of Technology Netherlands

This paper presents a stochastic model predictive control (SMPC) algorithm for linear systems subject to additive Gaussian mixture disturbances, with the goal of satisfying chance constraints. To synthesize a control strategy, the stochastic control problem is reformulated into an MPC problem. The reformulation begins by decoupling the mixture distribution and decomposing the system dynamics. Using stochastic simulation relations, we then redefine the stochastic control problem onto the resultant abstract system. Next, constraint tightening forms an MPC problem subject to finite disturbances. A branching control is introduced to solve the MPC problem. Finally, a controller refinement procedure determines a valid control strategy. Our contribution is an extension of the SMPC literature to accommodate Gaussian mixture disturbances while retaining recursive feasibility and closed-loop guarantees. We illustrate the retention of guarantees with a case study of vehicle control on an ill-maintained road. © 2024, CC BY-NC-ND.

关键词： Predictive control systems

Constrained Synchronization of Multi-Agent systems Using Distributed MPC and Invariant Families of Terminal Sets

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Constrained Synchronization of Multi-Agent Systems Using Dis...

European control Conference (ECC)

作者： Shiqi Duan Victor Daniel Reyes Dreke Mircea Lazar Department of Electrical Engineering Control Systems Group Eindhoven University of Technology The Netherlands

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

In this paper, we present a synchronizing DMPC scheme that employs two ingredients: (i) a cost function that penalizes the deviation of the MPC control input from an unconstrained synchronization control law based on algebraic graph theory and (ii) an invariant family of constraints admissible terminal sets for MASs in closed-loop with an unconstrained synchronization control law. We prove that the developed DMPC scheme with a shrinking prediction horizon guarantees finite-time controllability to a family of invariant terminal sets and recursive feasibility. Compared to existing LMI methods for computing a family of constraints admissible invariant sets, we reduce conservatism by exploiting specific graph properties common to MASs. The developed DMPC algorithm for achieving constrained synchronization is tested in different benchmark examples, including balancing capacitor voltages for modular multilevel converters and harmonic oscillators, yielding faster synchronization.

关键词： Multilevel converters Laplace equations Prediction algorithms Harmonic analysis Graph theory Synchronization Communication networks

Planning and tracking for safe autonomous overtaking and abort overtaking through a model predictive controller

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Planning and tracking for safe autonomous overtaking and abo...

IEEE Symposium on Intelligent Vehicle

作者： Ishu Jaiswal Ekansh Saraf Arpita Sinha Systems and Control Engineering Group Indian Institute of Technology Bombay Mumbai India

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

This paper addresses trajectory planning for overtaking maneuvers in a dynamic, two-way, two-lane environment. The proposed novel five-stage planning algorithm comprises initial lane change, acceleration, final lane change, abort, and final lane change abort. This algorithm allows overtaking to be aborted in oncoming lane danger, reverting to the original lane. Employing a point mass model-based model predictive control (MPC) framework, separate longitudinal and lateral planning is conducted in each phase, combined to generate the overall trajectory. A practical nonlinear bicycle model is employed for trajectory tracking to address kinematic infeasibility. Furthermore, the tracking controller utilizes adaptive MPC. Numerical simulations validate the performance of the proposed algorithm.

关键词： Adaptation models Trajectory planning Heuristic algorithms Kinematics Bicycles Predictive models Prediction algorithms

Learning Subsystem Dynamics in Nonlinear systems via Port-Hamiltonian Neural networks

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arXiv 2024年

作者： van Otterdijk, G.J.E. Moradi, S. Weiland, S. Tóth, R. Jaensson, N.O. Schoukens, M. Control Systems Group Eindhoven University of Technology Eindhoven Netherlands Eindhoven Artificial Intelligence Systems Institute Eindhoven Netherlands Processing and Performance of Materials Group Eindhoven University of Technology Eindhoven Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

Port-Hamiltonian neural networks (pHNNs) are emerging as a powerful modeling tool that integrates physical laws with deep learning techniques. While most research has focused on modeling the entire dynamics of interconnected systems, the potential for identifying and modeling individual subsystems while operating as part of a larger system has been overlooked. This study addresses this gap by introducing a novel method for using pHNNs to identify such subsystems based solely on input-output measurements. By utilizing the inherent compositional property of the port-Hamiltonian systems, we developed an algorithm that learns the dynamics of individual subsystems, without requiring direct access to their internal states. On top of that, by choosing an output error (OE) model structure, we have been able to handle measurement noise effectively. The effectiveness of the proposed approach is demonstrated through tests on interconnected systems, including multi-physics scenarios, demonstrating its potential for identifying subsystem dynamics and facilitating their integration into new interconnected models. © 2024, CC BY-NC-SA.

关键词： Hamiltonians

On Space-Filling Input Design for Nonlinear Dynamic Model Learning: A Gaussian Process Approach

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arXiv 2025年

作者： Liu, Yuhan Kiss, Máté Tóth, Roland Schoukens, Maarten Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven5600 MB Netherlands Systems and Control Laboratory HUN-REN Institute for Computer Science and Control Budapest1111 Hungary

While optimal input design for linear systems has been well-established, no systematic approach exists for nonlinear systems, where robustness to extrapolation/interpolation errors is prioritized over minimizing estimated parameter variance. To address this issue, we develop a novel identification-oriented space-filling input design strategy for efficient covering of a well-defined region of interest. By placing a Gaussian Process (GP) prior on the joint input-state space, the proposed strategy leverages the GP posterior variance to construct a space-filling promoting input design cost function from the Bayesian point of view. This accordingly enables maximization of the coverage in the region of interest, thereby facilitating the generation of informative datasets. Furthermore, we theoretically prove that minimization of the cost function indicates the space-filling behavior. The effectiveness of the proposed strategy is demonstrated on both an academic example and a mass-spring-damper system, highlighting its potential practical impact on efficient exploration in identification scenarios for nonlinear dynamical systems. © 2025, CC BY-NC-SA.

关键词： Cost functions

Physics-Informed Learning Using Hamiltonian Neural networks with Output Error Noise Models

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IFAC-PapersOnLine 2023年第2期56卷 5152-5157页

作者： Sarvin Moradi Nick Jaensson Roland Tóth Maarten Schoukens Control System group Eindhoven University of Technology Eindhoven the Netherlands Eindhoven Artificial Intelligence Systems Institute Eindhoven the Netherlands Processing and Performance of Materials Group Eindhoven University of Technology Eindhoven the Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

In order to make data-driven models of physical systems interpretable and reliable, it is essential to include prior physical knowledge in the modeling framework. Hamiltonian Neural networks (HNNs) implement Hamiltonian theory in deep learning and form a comprehensive framework for modeling autonomous energy-conservative systems. Despite being suitable to estimate a wide range of physical system behavior from data, classical HNNs are restricted to systems without inputs and require noiseless state measurements and information on the derivative of the state to be available. To address these challenges, this paper introduces an Output Error Hamiltonian Neural network (OE-HNN) modeling approach to address the modeling of physical systems with inputs and noisy state measurements. Furthermore, it does not require the state derivatives to be known. Instead, the OE-HNN utilizes an ODE-solver embedded in the training process, which enables the OE-HNN to learn the dynamics from noisy state measurements. In addition, extending HNNs based on the generalized Hamiltonian theory enables to include external inputs into the framework which are important for engineering applications. We demonstrate via simulation examples that the proposed OE-HNNs results in superior modeling performance compared to classical HNNs.

关键词： Hamiltonian Neural networks Nonlinear system identification Machine learning

Online feedforward parameter learning with robustness to set-point variations

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IFAC-PapersOnLine 2023年第2期56卷 1919-1925页

作者： Thijs van Keulen Tom Oomen Maurice Heemels Eindhoven University of Technology Department of Mechanical Engineering Control Systems Technology group ASML Veldhoven the Netherlands

High-tech motion system development is driven by increasingly accurate and fast positioning requirements. Feedforward compensation together with high bandwidth feedback control are essential to achieve these ever tightening performance demands. In particular, online adaptation of the feedforward parameters, to correct for small position dependencies and slow variations, is crucial to approach zero error tracking. The aim of this paper is a framework that provides robust recursive learning of feedforward parameters for any bounded reference trajectory. The convergence of the parameter learning strategy exploits the difference in time-scale between the parameter variation rate and the bandwidth of the servo controlled system. This enables to describe a servo-error-based objective function for varying trajectories as a static sector bounded nonlinearity. Subsequently, the circle criterion is employed to derive stability guarantees on the learning with explicit robustness to reference trajectory variation. A numerical case study demonstrates that a significant performance improvement can be achieved.

关键词： Iterative and repetitive learning control Extremum seeking and model free adaptive control Continuous time system estimation

Finite Dimensional Koopman Form of Polynomial Nonlinear systems

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arXiv 2023年

作者： Iacob, Lucian Cristian Schoukens, Maarten Tóth, Roland Control Systems Group Eindhoven University of Technology Eindhoven Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

The Koopman framework is a popular approach to transform a finite dimensional nonlinear system into an infinite dimensional, but linear model through a lifting process, using so-called observable functions. While there is an extensive theory on infinite dimensional representations in the operator sense, there are few constructive results on how to select the observables to realize them. When it comes to the possibility of finite Koopman representations, which are highly important form a practical point of view, there is no constructive theory. Hence, in practice, often a data-based method and ad-hoc choice of the observable functions is used. When truncating to a finite number of basis, there is also no clear indication of the introduced approximation error. In this paper, we propose a systematic method to compute the finite dimensional Koopman embedding of a specific class of polynomial nonlinear systems in continuous-time such that, the embedding, without approximation, can fully represent the dynamics of the nonlinear system. © 2023, CC BY-NC-SA.

关键词： Nonlinear systems