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Marcin Cychowski

Researcher at Cork Institute of Technology

Publications -  35
Citations -  831

Marcin Cychowski is an academic researcher from Cork Institute of Technology. The author has contributed to research in topics: Model predictive control & Torque. The author has an hindex of 12, co-authored 35 publications receiving 747 citations. Previous affiliations of Marcin Cychowski include Carrier Corporation.

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Technical communique: A synthesis approach for output feedback robust constrained model predictive control

TL;DR: This paper addresses the synthesis approach for output feedback robust model predictive control for systems with polytopic description, bounded state disturbance and measurement noise by presenting a rigorous method to guarantee satisfaction of input/state constraints.
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Constrained Model Predictive Control of the Drive System With Mechanical Elasticity

TL;DR: The standard MPC controller is replaced by its explicit form, which achieves the same level of performance as the conventional MPC, but requires only a fraction of the real-time computational machinery, thus leading to fast and reliable implementation.
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Kalman Filter-Based Distributed Predictive Control of Large-Scale Multi-Rate Systems: Application to Power Networks

TL;DR: The main task of the proposed distributed KF is to compensate for the information loss due to the multi-rate nature of the systems by providing optimal estimation of the missing information.
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Technical communique: Improving off-line approach to robust MPC based-on nominal performance cost

TL;DR: This paper gives two alternative off-line synthesis approaches to robust model predictive control (RMPC) for systems with polytopic description by incorporating the knowledge of control laws associated with all smaller ellipsoids so as to further improve feasibility and optimality.
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Efficient real-time model predictive control of the drive system with elastic transmission

TL;DR: In this paper, an explicit version of model predictive control (MPC) is proposed for industrial drive systems with elastic coupling, which is computationally more suited for systems with demanding sampling rates.