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Effectiveness of Dynamic Matrix Control algorithm with Laguerre functions

Tatjewski Piotr

Archives of Control Sciences

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2021

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Vol. 31, No. 4

795--814

EN

The paper is concerned with the presentation and analysis of the Dynamic Matrix Control (DMC) model predictive control algorithm with the representation of the process input trajectories by parametrised sums of Laguerre functions. First the formulation of the DMCL (DMC with Laguerre functions) algorithm is presented. The algorithm differs from the standard DMC one in the formulation of the decision variables of the optimization problem - coefficients of approximations by the Laguerre functions instead of control input values are these variables. Then the DMCL algorithm is applied to two multivariable benchmark problems to investigate properties of the algorithm and to provide a concise comparison with the standard DMC one. The problems with difficult dynamics are selected, which usually leads to longer prediction and control horizons. Benefits from using Laguerre functions were shown, especially evident for smaller sampling intervals.

2

Nonlinear model predictive control for processes with complex dynamics: A parameterisation approach using Laguerre functions

Ławryńczuk Maciej

International Journal of Applied Mathematics and Computer Science

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2020

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Vol. 30, no. 1

35--46

EN

Classical model predictive control (MPC) algorithms need very long horizons when the controlled process has complex dynamics. In particular, the control horizon, which determines the number of decision variables optimised on-line at each sampling instant, is crucial since it significantly affects computational complexity. This work discusses a nonlinear MPC algorithm with on-line trajectory linearisation, which makes it possible to formulate a quadratic optimisation problem, as well as parameterisation using Laguerre functions, which reduces the number of decision variables. Simulation results of classical (not parameterised) MPC algorithms and some strategies with parameterisation are thoroughly compared. It is shown that for a benchmark system the MPC algorithm with on-line linearisation and parameterisation gives very good quality of control, comparable with that possible in classical MPC with long horizons and nonlinear optimisation.

3

Optymalizacja parametryczna regulatora niecałkowitego rzędu typu PD^alfa

Zagórowska M.

Pomiary Automatyka Robotyka

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2015

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R. 19, nr 4

5--14

PL

W artykule przeanalizowano zachowanie układu całkowitego rzędu ze sprzężeniem zwrotnym niecałkowitego rzędu. Przedstawiono nową metodę doboru optymalnych parametrów dla regulatorów typu PDα we wspomnianych układach z nieskończonym horyzontem. Zaprezentowano wykorzystaną metodę aproksymacji układów niecałkowitego rzędu z wykorzystaniem funkcji Laguerre’a oraz sformułowano problem w postaci zagadnienia minmax. Pokazano również przykładowy przebieg optymalizacji ze względu na parametry związane z typem aproksymacji.

EN

In this paper we analysed the behaviour of an integer order system with non-integer control function. We presented a new method for tuning the non-integer order controllers PDα for use in systems with infinite horizon. An approximation method for non-integer order systems was presented (using Laguerre functions) along with the definition of the issue in form of minmax problem. Finally some examples were analysed with respect to parameters specific for this approximation.