Wyniki wyszukiwania - BazTech

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Sterowanie bazujące na sprzężeniu od wektora zmiennych stanu z ograniczeniami serwonapędem z silnikiem PMSM

Tarczewski T., Skiwski M., Grzesiak L M., Zieliński M.

Przegląd Elektrotechniczny

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2018

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R. 94, nr 3

99--105

PL

W artykule przedstawiono syntezę regulatora bazującego na sprzężeniu od wektora zmiennych stanu z ograniczeniami sterującego serwonapędem. Do wyznaczenia współczynników wzmocnień regulatora zastosowano algorytm optymalizacyjny sztucznej kolonii pszczół. Przedstawiono metodę wprowadzenia ograniczeń do regulatora bazującą na dyskretnych równaniach predykcyjnych. Opracowany algorytm regulacji zaimplementowano w prototypowym serwonapędzie z tranzystorami mocy SiC MOSFET i przeprowadzono testy na stanowisku laboratoryjnym.

EN

In this paper, synthesis of constrained state feedback controller for servo-drive is presented. Gain coefficients of the controller were obtained by using an artificial bee colony based optimization algorithm. Constraints handling method that is based on discrete predictive equations has been proposed. Designed control algorithm has been implemented in a prototype servo-drive with SiC MOSFET power transistors and experimentally verified.

2

High-performance PMSM servo-drive with constrained state feedback position controller

Tarczewski T., Skiwski M., Niewiara L. J., Grzesiak L. M.

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2018

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Vol. 66, nr 1

49--58

EN

This paper describes high-performance permanent magnet synchronous motor (PMSM) servo-drive with constrained state feedback (SFC) position controller. Superior behavior of the control system has been achieved by applying SFC with constraints handling method based on a posteriori model predictive approach (MPAC). The concept utilizes predictive equations obtained from discrete-time model of the PMSM to compute control signals which generate admissible values of the future state variables. The novelty of the proposed solution lies in the limitation of several state-space variables in servo-drive control system. Since MPAC has firstly been applied to limit more than one state-space variable of the plant, necessary conditions for introducing constraints into multivariable control system with SFC are depicted. Due to the low complexity of proposed algorithm, a low cost microprocessor, STM32F4, is employed to execute the state feedback position control with model predictive approach to constraints handling. Experimental results show that the proposed control method provides superior performance of PMSM servodrive with modern SiC based voltage source inverter (VSI).

3

Supervisory predictive control and on-line set-point optimization

Tatjewski P.

International Journal of Applied Mathematics and Computer Science

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2010

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Vol. 20, no 3

483-495

EN

The subject of this paper is to discuss selected effective known and novel structures for advanced process control and optimization. The role and techniques of model-based predictive control (MPC) in a supervisory (advanced) control layer are first shortly discussed. The emphasis is put on algorithm efficiency for nonlinear processes and on treating uncertainty in process models, with two solutions presented: the structure of nonlinear prediction and successive linearizations for nonlinear control, and a novel algorithm based on fast model selection to cope with process uncertainty. Issues of cooperation between MPC algorithms and on-line steady-state set-point optimization are next discussed, including integrated approaches. Finally, a recently developed two-purpose supervisory predictive set-point optimizer is discussed, designed to perform simultaneously two goals: economic optimization and constraints handling for the underlying unconstrained direct controllers.

4

Effective dual-mode fuzzy DMC algorithms with on-line quadratic optimization and guaranteed stability

Marusak P. M., Tatjewski P.

International Journal of Applied Mathematics and Computer Science

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2009

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Vol. 19, no 1

127-141

EN

Dual-mode fuzzy dynamic matrix control (fuzzy DMC-FDMC) algorithms with guaranteed nominal stability for constrained nonlinear plants are presented. The algorithms join the advantages of fuzzy Takagi-Sugeno modeling and the predictive dual-mode approach in a computationally efficient version. Thus, they can bring an improvement in control quality compared with predictive controllers based on linear models and, at the same time, control performance similar to that obtained using more demanding algorithms with nonlinear optimization. Numerical effectiveness is obtained by using a successive linearization approach resulting in a quadratic programming problem solved on-line at each sampling instant. It is a computationally robust and fast optimization problem, which is important for on-line applications. Stability is achieved by appropriate introduction of dual-mode type stabilization mechanisms, which are simple and easy to implement. The effectiveness of the proposed approach is tested on a control system of a nonlinear plant-a distillation column with basic feedback controllers.

5

Cooperation of model predictive control with steady-state economic optimisation

Ławryńczuk M., Marusak P. M., Tatjewski P.

Control and Cybernetics

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2008

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Vol. 37, no 1

133-158

EN

The problem of cooperation of Model Predictive Control (MPC) algorithms with steady-state economic optimisation is investigated in this paper. It is particularly important when the dynamics of disturbances is comparable with the dynamics of the process, since in such a case the classical hierarchical multilayer structure is likely to be not efficient and give the economic yield smaller than expected. This is because the economic nonlinear optimisation problem cannot be then solved on-line to update the optimal operating point as frequently as needed. On the other hand, simple target set-point optimisation based on linear models can be also insufficiently accurate. This paper introduces approximate formulations of the target set-point optimisation problem which tightly cooperates with the MPC and is solved as frequently as the MPC controller executes. Linear, linear-quadratic and piecewise-linear formulations are discussed, tuning guidelines are also given.

6

Struktury i algorytmy współdziałania regulacji predykcyjnej i bieżącej optymalizacji ekonomicznej

Ławryńczuk M., Marusak P., Tatjewski P.

Pomiary Automatyka Kontrola

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2007

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R. 53, nr 10

55-61

PL

Celem pracy jest omówienie zagadnienia współpracy algorytmów regulacji predykcyjnej z nieliniową optymalizacją ekonomiczną. Problem ten jest szczególnie istotny wówczas, gdy dynamika zmian zakłóceń jest porównywalna z dynamiką procesu, ponieważ zastosowanie klasycznej warstwowej (hierarchicznej) struktury sterowania z rzadko powtarzaną optymalizacją ekonomiczną może nie być efektywne. Omawiane są dwie klasy struktur. W pierwszym przypadku stosuje się pomocniczą optymalizację ekonomiczną, której zadaniem jest aktualizacja punktu pracy poprzedzająca każdą interwencję algorytmu regulacji predykcyjnej. W dodatkowym liniowym lub kwadratowym zadaniu optymalizacji ekonomicznej stosuje się aktualizowaną na bieżąco liniową, liniowo-kwadratową lub odcinkowo-liniową aproksymację modelu. W drugim przypadku zadanie optymalizacji ekonomicznej i algorytm regulacji predykcyjnej są zintegrowane w pojedynczym problemie optymalizacji. Aby ograniczyć nakład obliczeń stosuje się aktualizowaną na bieżąco liniową lub liniowo-kwadratową aproksymację modelu, dzięki czemu otrzymuje się zadanie optymalizacji ekonomicznej w postaci problemu programowania kwadratowego.

EN

The paper is concerned with co-operation of model predictive control (MPC) algorithms with nonlinear economic optimisation. The problem is particularly important when dynamics of disturbances is comparable with dynamics of the process itself, since in such cases application of the classical multilayer (hierarchical) structure with infrequent economic optimisation may be not efficient. Two classes of control structures are investigated. In the first class an additional simplified optimisation is used which recalculates the operating point as frequently as the MPC controller executes. In the supplementary linear or quadratic programming optimisation problem approximate linear, linear-quadratic (updated on-line) or piecewise-linear models of the process are used. In the second class the economic optimisation and MPC manipulated variables computational load, approximate linear or linear-quadratic (updated on-line) models are used, then the resulting optimisation problem is of quadratic programming type.