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Robust model predictive control of constrained supply networks via invariant ellipsoids technique
(2013) Lyubchyk, Leonid; Dorofieiev, Yuri; Nikulchenko, Artem
The problem of robust control strategy synthesis for distributed supply network under demand uncertainty, time delays and state and control constraints is considered. An invariant ellipsoids approach is used for robust control problem solving, since the uncertain demands are regarded as an external disturbance. On the base Model Predictive Control approach, the designed control law implements in the form of linear feedback signal based on mismatch between the current state and safety stock level and provides external disturbances effect suppression with simultaneous robust stabilization of closed-loop system. Via invariant ellipsoids technique the considered problem was presented in the terms of Linear Matrix Inequalities and a solution of corresponding semi-definite optimization problem was also obtained. As an example, the three-tier supply network with ve nodes robust control problem is considered.
Preference Function Reconstruction for Multiple Criteria Decision Making Based on Machine Learning Approach
(Springer International Publishing, 2014) Lyubchyk, Leonid; Grinberg, Galina
The problem of expert preference function reconstruction in decision making process of multicriterion comparative assessment of set of object is considered. The problem is reduced to integral indicator identification using available data of object’s performance indexes measurements as well as expert estimation of integral indicators values for each object and feature weights. Based on machine learning approach and expert estimations concordance technique, the solution of preference function recovering problem is obtained in the form of optimal nonlinear object feature convolution.
Nonlinear dynamic system kernel based reconstruction from time series data
(ТВіМС, 2015) Lyubchyk, Leonid; Kolbasin, Vladislav; Grinberg, Galina
A unified approach to reccurent kernel identification algorithms design is proposed. In order to fix the auxiliary vector dimension, the reduced order model kernel method is proposed and proper reccurent identification algorithms are designed.
Inverse Dynamic Models in Chaotic Systems Identification and Control Problems
(2018) Lyubchyk, Leonid; Grinberg, Galina
Inverse dynamic models approach for chaotic system synchronization in the presence of uncertain parameters is considered. The problem is identifying and compensating unknown state-dependent parametric disturbance describing an unmodelled dynamics that generates chaotic motion. Based on the method of inverse model control, disturbance observers and compensators are synthesized. A control law is proposed that ensures the stabilization of chaotic system movement along master reference trajectory. The results of computational simulation of controlled Rösller attractor synchronization are also presented.
Ranking Model Real-Time Adaptation via Preference Learning Based on Dynamic Clustering
(ННК "IПСА" НТУУ "КПI iм. Iгоря Сiкорського", 2017) Lyubchyk, Leonid; Galuza, Oleksy; Grinberg, Galina
The proposed preference learning on clusters method allows to fully realizing the advantages of the kernel-based approach. While the dimension of the model is determined by a pre-selected number of clusters and its complexity do not grow with increasing number of observations. Thus real-time preference function identification algorithm based on training data stream includes successive estimates of cluster parameter as well as average cluster ranks updating and recurrent kernel-based nonparametric estimation of preference model.
Nonlinear expert preference function concordance identification for multiple criteria decision making
(ТВіМС, 2014) Lyubchyk, Leonid; Grinberg, Galina
The proposal generalization of expert estimates concordance idea for the case of nonlinear preferance function guaranties on optimal concordance of mesuarement and expert data, whereas machine learning approach ensure the possibility of more accurate approximation expert preference function with complex structure.
Selective invariant multivariable control system design based on inverse model approach
(Consulting Company Consilium Sp. z o.o. Warsaw, Poland, 2015) Lyubchyk, Leonid; Kostyuk, Olga
The problem of disturbance rejection in multivariable control systems is considered. It has been shown that different two-degree-of-freedom control structures used for immeasurable disturbance estimation and compensation may be treated as a particular case of a general Inverse Model Control approach. The decomposition of the problem into the separate disturbance estimation and compensation is suggested. Moreover the connection between inverse model design problems and unknown input observer theory has been established in order to give a practical way to inverse model parameterization and design. The properties of closed-loop selective invariant control system with model-based controllers have been also investigated with the aim of attainable accuracy estimation.

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