Search Results for author: Mircea Lazar

Found 15 papers, 0 papers with code

Multivariable control of modular multilevel converters with convergence and safety guarantees

no code implementations27 Mar 2024 Victor Daniel Reyes Dreke, Ygor Pereira Marca, Maurice Roes, Mircea Lazar

Even though this control problem involves multiple control objectives, conventional current control schemes are comprised of independently designed decoupled controllers, e. g., proportional-integral (PI) or proportional-resonant (PR).

Risk-Aware MPC for Stochastic Systems with Runtime Temporal Logics

no code implementations5 Feb 2024 Maico Hendrikus Wilhelmus Engelaar, Zengjie Zhang, Mircea Lazar, Sofie Haesaert

In this paper, we propose a novel, provably correct control scheme for linear systems with unbounded stochastic disturbances that dynamically evaluates the feasibility of runtime signal temporal logic specifications and automatically reschedules the control inputs.

Motion Planning

Basis functions nonlinear data-enabled predictive control: Consistent and computationally efficient formulations

no code implementations9 Nov 2023 Mircea Lazar

This paper considers the extension of data-enabled predictive control (DeePC) to nonlinear systems via general basis functions.

Computational Efficiency

Physics-guided neural networks for inversion-based feedforward control applied to hybrid stepper motors

no code implementations22 Jun 2023 Daiwei Fan, Max Bolderman, Sjirk Koekebakker, Hans Butler, Mircea Lazar

Rotary motors, such as hybrid stepper motors (HSMs), are widely used in industries varying from printing applications to robotics.

Abstracting Linear Stochastic Systems via Knowledge Filtering

no code implementations12 Apr 2023 Maico Hendrikus Wilhelmus Engelaar, Licio Romao, Yulong Gao, Mircea Lazar, Alessandro Abate, Sofie Haesaert

In this paper, we propose a new model reduction technique for linear stochastic systems that builds upon knowledge filtering and utilizes optimal Kalman filtering techniques.

Data-driven feedforward control design for nonlinear systems: A control-oriented system identification approach

no code implementations20 Mar 2023 Max Bolderman, Mircea Lazar, Hans Butler

To achieve this, we present a model of the system in a lifted space of trajectories, based on which we derive an upperbound on the reference tracking performance.

Physics-guided neural networks for feedforward control with input-to-state stability guarantees

no code implementations20 Jan 2023 Max Bolderman, Hans Butler, Sjirk Koekebakker, Eelco van Horssen, Ramidin Kamidi, Theresa Spaan-Burke, Nard Strijbosch, Mircea Lazar

The increasing demand on precision and throughput within high-precision mechatronics industries requires a new generation of feedforward controllers with higher accuracy than existing, physics-based feedforward controllers.

Friction

Consensus of hierarchical multi-agent systems with a time-varying set of active agents

no code implementations1 Dec 2022 Victor Daniel Reyes Dreke, Mircea Lazar

This type of multi-agent system is relevant in applications such as modular multilevel converters and water pumping systems.

On the Steady-State Behavior of Finite-Control-Set MPC with an Application to High-Precision Power Amplifiers

no code implementations31 May 2022 Duo Xu, Sander Damsma, Mircea Lazar

To improve the steady-state behavior of FCS-MPC, in this paper we design a cost function that penalizes the tracking error with respect to a state and input steady-state limit cycle.

Model Predictive Control

Physics-guided neural networks for feedforward control: From consistent identification to feedforward controller design

no code implementations1 Apr 2022 Max Bolderman, Mircea Lazar, Hans Butler

However, direct identification of the inverse dynamics is sensitive to noise that is present in the training data, and thereby results in biased parameter estimates which limit the achievable tracking performance.

Long hauling eco-driving: heavy-duty trucks operational modes control with integrated road slope preview

no code implementations23 Mar 2022 Gustavo R. Gonçalves da Silva, Mircea Lazar

In this paper, a complete eco-driving strategy for heavy-duty trucks (HDT) based on a finite number of driving modes with corresponding gear shifting is developed to cope with different route events and with road slope data.

On feedforward control using physics-guided neural networks: Training cost regularization and optimized initialization

no code implementations28 Jan 2022 Max Bolderman, Mircea Lazar, Hans Butler

Performance of model-based feedforward controllers is typically limited by the accuracy of the inverse system dynamics model.

Guaranteed $\mathcal{H}_\infty$ performance analysis and controller synthesis for interconnected linear systems from noisy input-state data

no code implementations26 Mar 2021 Tom R. V. Steentjes, Mircea Lazar, Paul M. J. Van den Hof

The increase in available data and complexity of dynamical systems has sparked the research on data-based system performance analysis and controller design.

Physics-Guided Neural Networks for Inversion-based Feedforward Control applied to Linear Motors

no code implementations10 Mar 2021 Max Bolderman, Mircea Lazar, Hans Butler

Ever-increasing throughput specifications in semiconductor manufacturing require operating high-precision mechatronics, such as linear motors, at higher accelerations.

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