34 citations · 106 across the 32 of their papers we have counts for
33 papers
Physics-guided neural networks for inversion-based feedforward control applied to hybrid stepper motors
Daiwei Fan, Max Bolderman, Sjirk Koekebakker +2
Rotary motors, such as hybrid stepper motors (HSMs), are widely used in industries varying from printing applications to robotics. The increasing need for productivity and efficien…
Stochastic Model Predictive Control with Dynamic Chance Constraints
Maico Hendrikus Wilhelmus Engelaar, Sofie Haesaert, Mircea Lazar
This work introduces a stochastic model predictive control scheme for dynamic chance constraints. We consider linear discrete-time systems affected by unbounded additive stochastic…
Generalized Data-driven Predictive Control
M. Lazar, P. C. N. Verheijen
Data-driven predictive control (DPC) is becoming an attractive alternative to model predictive control as it requires less system knowledge for implementation and reliable data is…
Abstracting Linear Stochastic Systems via Knowledge Filtering
Maico Hendrikus Wilhelmus Engelaar, Licio Romao, Yulong Gao +3
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. This…
Data-driven feedforward control design for nonlinear systems: A control-oriented system identification approach
Max Bolderman, Mircea Lazar, Hans Butler
Feedforward controllers typically rely on accurately identified inverse models of the system dynamics to achieve high reference tracking performance. However, the impact of the (in…
Physics-guided neural networks for feedforward control with input-to-state stability guarantees
Max Bolderman, Hans Butler, Sjirk Koekebakker +5
The increasing demand on precision and throughput within high-precision mechatronics industries requires a new generation of feedforward controllers with higher accuracy than exist…
Consensus of hierarchical multi-agent systems with a time-varying set of active agents
Victor Daniel Reyes Dreke, Mircea Lazar
Time-varying hierarchical multi-agent systems are common in many applications. A well-known solution to control these systems is to use state feedback controllers that depend on th…
On a Canonical Distributed Controller in the Behavioral Framework
Tom R. V. Steentjes, Mircea Lazar, Paul M. J. Van den Hof
Control in a classical transfer function or state-space setting typically views a controller as a signal processor: sensor outputs are mapped to actuator inputs. In behavioral syst…
On the Steady-State Behavior of Finite-Control-Set MPC with an Application to High-Precision Power Amplifiers
Duo Xu, Sander Damsma, Mircea Lazar
Motivated by increasing precision requirements for switched power amplifiers, this paper addresses the problem of model predictive control (MPC) design for discrete-time linear sys…
Physics-guided neural networks for feedforward control: From consistent identification to feedforward controller design
Max Bolderman, Mircea Lazar, Hans Butler
Model-based feedforward control improves tracking performance of motion systems, provided that the model describing the inverse dynamics is of sufficient accuracy. Model sets, such…
Long hauling eco-driving: heavy-duty trucks operational modes control with integrated road slope preview
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 diff…
Informativity conditions for data-driven control based on input-state data and polyhedral cross-covariance noise bounds
Tom R. V. Steentjes, Mircea Lazar, Paul M. J. Van den Hof
Modeling and control of dynamical systems rely on measured data, which contains information about the system. Finite data measurements typically lead to a set of system models that…
On feedforward control using physics-guided neural networks: Training cost regularization and optimized initialization
Max Bolderman, Mircea Lazar, Hans Butler
Performance of model-based feedforward controllers is typically limited by the accuracy of the inverse system dynamics model. Physics-guided neural networks (PGNN), where a known p…
On data-driven control: informativity of noisy input-output data with cross-covariance bounds
Tom R. V. Steentjes, Mircea Lazar, Paul M. J. Van den Hof
In this paper we develop new data informativity based controller synthesis methods that extend existing frameworks in two relevant directions: a more general noise characterization…
Data-driven predictive control with estimated prediction matrices and integral action
P. C. N. Verheijen, G. R. Gonçalves da Silva, M. Lazar
This paper presents a data-driven approach to the design of predictive controllers. The prediction matrices utilized in standard model predictive control (MPC) algorithms are typic…
Design and assessment of an eco--driving PMP algorithm for optimal deceleration and gear shifting in trucks
B. Wingelaar, G. R. Gonçalves da Silva, M. Lazar +2
In this paper, an eco--driving Pontryagin maximum principle (PMP) algorithm is designed for optimal deceleration and gear shifting in trucks based on switching among a finite set o…
Guaranteed performance analysis and controller synthesis for interconnected linear systems from noisy input-state data
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. Recent approaches can g…
Stabilization of discrete-time nonlinear systems based on control dissipation functions
Mircea Lazar
This paper develops a dissipativity-based framework for synthesis of stabilizing controllers for discrete-time nonlinear systems subject to state/input constraints. Firstly, we rev…
Physics-Guided Neural Networks for Inversion-based Feedforward Control applied to Linear Motors
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. In turn this…
Data-driven distributed control: Virtual reference feedback tuning in dynamic networks
Tom R. V. Steentjes, Mircea Lazar, Paul M. J. Van den Hof
In this paper, the problem of synthesizing a distributed controller from data is considered, with the objective to optimize a model-reference control criterion. We establish an exp…
An Efficient MPC Algorithm For Switched Systems with Minimum Dwell Time Constraints
Yutao Chen, Mircea Lazar
This paper presents an efficient suboptimal model predictive control (MPC) algorithm for nonlinear switched systems subject to minimum dwell time constraints (MTC). While MTC are r…
Scalable distributed and decentralized controller synthesis for interconnected linear discrete-time systems
Tom R. V. Steentjes, Mircea Lazar, Paul M. J. Van den Hof
The current limitation in the synthesis of distributed controllers for linear interconnected systems is scalability due to non-convex or unstructured synthesis cond…
Heterogeneously parameterized tube model predictive control for LPV systems
Jurre Hanema, Mircea Lazar, Roland Tóth
This paper presents a heterogeneously parameterized tube-based model predictive control (MPC) design applicable to linear parameter-varying (LPV) systems. In a heterogeneous tube,…
A recursive estimation approach to distributed identification of large-scale multi-input-single-output FIR systems
Tom R. V. Steentjes, Mircea Lazar, Paul M. J. Van den Hof
The problem of identifying single modules in multiple-input-single-output (MISO) systems is considered. A novel approach to distributed identification of MISO finite impulse respon…