9 citations · 11 across the 10 of their papers we have counts for
12 papers
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 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…