Ensuring reach ability and stability in the synthesis of robust discrete model predictive control in conditions of incomplete information
( Pp. 29-33)

Methods of synthesis of control of multiscale processes with predictive models for linear discrete time systems are considered. A description is given of a control scheme in which the current control action is obtained by solving at each instant of the sample the optimal control problem with a finite horizon without feedback and using the current state of the object as an initial state. An optimization problem is described that gives an optimal control sequence when the control obtained for the first step of the subsequent sequence is applied to the object. The analysis of the reachability and stability problems of synthesized controls with a predictive model under conditions of disturbances and uncertainties is given. As well as the problems of providing preset indicators of the quality of management and comparing indicators in the management of MPC in open and closed systems. The urgent issues requiring research in the framework of the considered management system are identified. The proposed solutions are extremely relevant to the problems of modeling and control of technological processes of growing nanoscale structures.

Nguyen K.T., Zhilenkov A.A., Dang B.K., (2020), ENSURING REACH ABILITY AND STABILITY IN THE SYNTHESIS OF ROBUST DISCRETE MODEL PREDICTIVE CONTROL IN CONDITIONS OF INCOMPLETE INFORMATION. Computational Nanotechnology, 2 => 29-33.

Keerthi S., Gilbert E. Optimal infinite-horizon feedback laws for a general class of constrained discrete-time systems: stability and moving-horizon approximations. Journal of Optimization Theory and Applications. 1988. No. 57 (2). Pp. 265-293.
Nevisti V., Primbs J.A. Finite receding horizon linear quadratic control: A unifying theory for stability and performance analysis. Paper presented at Technical Report CIT-CDS 97-001. California Institute of Technology. Pasadena, CA, 1997.
Bolognani S., Bolognani S., Peretti L., Zigliotto M. Design and Implementation of Model Predictive Control for Electrical Motor Drives. IEEE Transactions on Industrial Electronics. June 2009. Vol. 56. No. 6. Pp. 925-1936. DOI: 10.1109/TIE.2008.2007547
Zhilenkov A., Chernyi S. Models and algorithms of the positioning and trajectory stabilisation system with elements of structural analysis for robotic applications. International Journal of Embedded Systems. 2019. No. 11 (6). P. 806. DOI: 10.1504/ijes.2019.104005
Zhilenkov A., Chernyi S., Sokolov S., Nyrkov A. Intelligent autonomous navigation system for UAV in randomly changing environmental conditions. Journal of Intelligent Fuzzy Systems. 2020. Vol. 38, No. 5, Pp. 6619-6625. Available: 10.3233/jifs-179741
Grimm G., Messina M.J., Tuna S.E., Teel A.R. Nominally robust model predictive control with state constraints. IEEE Transactions on Automatic Control. Oct. 2007. Vol. 52. No. 10. Pp. 1856-1870. DOI: 10.1109/TAC.2007.906187
Qi W., Liu J., Chen X., Christofides P.D. Supervisory Predictive Control of Standalone Wind / Solar Energy Generation Systems. IEEE Transactions on Control Systems Technology. Jan. 2011. Vol. 19. No. 1. Pp. 199-207. DOI: 10.1109/TCST.2010.2041930
Sadowska A., Schutter B.De., van Overloop P. Delivery-Oriented Hierarchical Predictive Control of an Irrigation Canal: Event-Driven Versus Time-Driven Approaches. IEEE Transactions on Control Systems Technology. Sept. 2015. Vol. 23. No. 5. Pp. 1701-1716. DOI: 10.1109/TCST.2014.2381600
Veksler A., Johansen T.A., Borrelli F., Realfsen B. Dynamic positioning with model predictive control. IEEE Transactions on Control Systems Technology. July 2016. Vol. 24. No. 4. Pp. 1340-1353. DOI: 10.1109/TCST.2015.2497280
Liu Jiangang, Huang Zhiwu, Peng Jun et al. An explicit predictive current-sharing control for parallel charging systems with nonlinear dynamics. American Control Conference (ACC). 2016. Pp. 6821-6826.
Ionescu C.M., Keyser R.D., Torrico B.C. et al. Robust predictive control strategy applied for propofol dosing using BIS as a controlled variable during anesthesia. IEEE Transactions on Biomedical Engineering. Sept. 2008. Vol. 55. No. 9. Pp. 2161-2170. DOI: 10.1109/TBME.2008.923142
Zbede Y.B., Gadoue S.M., Atkinson D.J. Model predictive MRAS estimator for sensorless induction motor drives. IEEE Transactions on Industrial Electronics. June 2016. Vol. 63. No. 6. Pp. 3511-3521. DOI: 10.1109/TIE.2016.2521721
Shadmand M.B., Balog R.S., Abu-Rub H. Model predictive control of PV sources in a smart DC distribution system: Maximum power point tracking and droop control. IEEE Transactions on Energy Conversion, Dec. 2014. Vol. 29, No. 4, Pp. 913-921. DOI: 10.1109/TEC.2014.2362934
Kouki Rihab, Salhi Hichem, Bouani Faouzi. Application of model predictive control for a thermal process using STM32 microcontroller. Control Automation and Diagnosis (ICCAD) International Conference. 2017. Pp. 146-151.
Omar M.S., El Deib Amgad, El Shafei A.L. et al. Comparative study between PI and fuzzy-logic controllers for three-phase grid-connected photovoltaic systems. Power Systems Conference (MEPCON) Eighteenth International Middle East. 2016. Pp. 380-386.

reachability, stability, control, predictive model, disturbances, incompleteness of information.