Discrete model predictive control for DC drive using orthonormal basis function

Holkar, K. S.; Waghmare, L. M.

doi:10.1049/ic.2010.0322

Cited by 3 publications

(2 citation statements)

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“…Model Predictive Control, MPC, also known as receding horizon control, uses the range of control methods, making the use of the process model to predict the output and the control signal obtained by minimizing the quadratic cost function [9]. The effectiveness of the controller depends on the quality of the system dynamics captured by the input-output model used for controller design [10].…”

Section: Introductionmentioning

confidence: 99%

Constrained model predictive control for the quadruple-tank process

Zidane

2019

International Advanced Researches and Engineering Journal

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Model Predictive Control (MPC) is an advanced method of controllers, explicitly uses of model to obtain control signal. MPC is popular in industry and academia because it is capable to deals with non-minimum phase, unstable, dead time and multivariable processes, and solves the problem of constraints. MPC with integral action method is used in this study for the quadruple tank system by taking the lower two tanks into account. The objective of this work is to design and study the MPC method for controlling the level of tanks in a quadruple tank process depending on type of constrained problems. However, to solve the problem of constraints is not easy way. The methods based on the quadratic programming function and 'if-else' technique are presented to solve the problem of the process constraints in MPC. A comparative study is performed with the quadratic programming function and 'if-else' technique. The performance of the proposed method is tested for reference tracking and disturbance rejection behavior. Simulation results are presented and discussed to show the performance of the controller.

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Section: Introductionmentioning

confidence: 99%

Constrained model predictive control for the quadruple-tank process

Zidane

2019

International Advanced Researches and Engineering Journal

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“…The algorithm developed enables stable and undisturbed operation of even non-linear and unstable objects. Predictive control methods are based on structural identification of the control plant [19]. They are divided into two groups: algorithms with parametric identification and algorithms with non-parametric identification.…”

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confidence: 99%

Bulletin of the Polish Academy of Sciences: Technical Sciences

Kurnyta-Mazurek,

Szolc,

Henzel

et al. 2021

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This paper deals with research on the magnetic bearing control systems for a high-speed rotating machine. Theoretical and experimental characteristics of the control systems with the model algorithmic control (MAC) algorithm and the proportional-derivative (PD) algorithm are presented. The MAC algorithm is the non-parametric predictive control method that uses an impulse response model. A laboratory model of the rotor-bearing unit under study consists of two active radial magnetic bearings and one active axial (thrust) magnetic bearing. The control system of the rotor position in air gaps consists of the fast prototyping control unit with a signal processor, the input and output modules, power amplifiers, contactless eddy current sensors and the host PC with dedicated software. Rotor displacement and control current signals were registered during investigations using a data acquisition (DAQ) system. In addition, measurements were performed for various rotor speeds, control algorithms and disturbance signals generated by the control system. Finally, the obtained time histories were presented, analyzed and discussed in this paper.

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