Application of the MPC to the Position Control of the Two-Mass Drive System

Serkies, Piotr; Szabat, K.

doi:10.1109/tie.2012.2208435

Cited by 75 publications

(40 citation statements)

References 31 publications

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“…Such construction significantly complicates control of the motor speed, because additional oscillations of state variables may appear [10]. In control structures, often applied in a drive with such construction of a mechanical part, information about the state variables vector is used to provide signals for feedback loops in classical or advanced control structures [13]. But this approach can be problematic in real applications, because problems with implementation of sensors can appear, and additionally, cost of a whole construction is higher.…”

Section: Neural Speed Control Structure For Drive System With Elamentioning

confidence: 99%

“…implementation of absorbers) [10], -implementation of modified (decreased) speed controller coefficients in the control structure [10], -classical control structures with PI/PID controllers and additional feedbacks [10], or with state controller [11],  application of advanced control structures, like: slidingmode [12], predictive [13] and artificial intelligence based methods (fuzzy logic and neural networks) [14].…”

Section: Introductionmentioning

confidence: 99%

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An on-line trained neural controller with a fuzzy learning rate of the Levenberg–Marquardt algorithm for speed control of an electrical drive with an elastic joint

Kaminski

Orłowska-Kowalska

2015

Applied Soft Computing

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Section: Neural Speed Control Structure For Drive System With Elamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An on-line trained neural controller with a fuzzy learning rate of the Levenberg–Marquardt algorithm for speed control of an electrical drive with an elastic joint

Kaminski

Orłowska-Kowalska

2015

Applied Soft Computing

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“…In recent decades, due to the broad applications for mobile robots, the core issues in motion control of mobile robots, including point stabilization, path planning, trajectory tracking, and real-time avoidance, have attracted considerable attention from both academic scholars and practitioners [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Trajectory Tracking of an Omni-Directional Wheeled Mobile Robot Using a Model Predictive Control Strategy

et al. 2018

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This paper addresses trajectory tracking of an omni-directional mobile robot (OMR) with three mecanum wheels and a fully symmetrical configuration. The omni-directional wheeled robot outperforms the non-holonomic wheeled robot due to its ability to rotate and translate independently and simultaneously. A kinematics model of the OMR is established and a model predictive control (MPC) algorithm with control and system constraints is designed to achieve point stabilization and trajectory tracking. Simulation results validate the accuracy of the established kinematics model and the effectiveness of the proposed MPC controller.

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Section: Introductionmentioning

confidence: 99%

“…One of the methods allowing to introduce the restrictions of the values of state variables is predictive control. This method allows to introduce limitations as early as the controller design stage [6,15].The goal of this work is a comparison of various methods of restricting the amplitude of selected state variables of a drive with an elastic coupling and their influences on the dynamic properties of a drive. The comparison will encompass three control systems: the first one will use a classical PI controller with two additional feedbacks from selected state variables.…”

mentioning

confidence: 99%

Comparison of the control methods of electrical drives with an elastic coupling allowing to limit the torsional torque amplitude

Serkies¹

2017

EiN

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Article citation info: (*) Tekst artykułu w polskiej wersji językowej dostępny w elektronicznym wydaniu kwartalnika na stronie www.ein.org.pl Piotr SerkieSComparison of the Control methods of eleCtriCal drives with an elastiC Coupling allowing to limit the torsional torque amplitude porównanie metod sterowania napędem elektryCznym z połąCzeniem sprężystym pozwalająCe na ograniCzenie amplitudy momentu skrętnego* IntroductionModern power transmission systems are expected to show higher dynamics while preserving high precision and reliability. Such requirements results in an increasingly higher use of the properties and strength of construction materials. Therefore, in a growing number of drives one can observe finite stiffness of couplings between a drive and machinery. This problem was initially considered in the heavy industry in various types of machines, such as: rolling mills [13], conveyor belts [10] Various methods are used in the control of vehicle speed or the location of drives with an elastic coupling, starting with simple ones based on a PI controller tuned with account for coupling elasticity [22], to the introduction of additional feedbacks from one to a few state variables to such a controller, a detailed overview of such solutions can be found in [17]. Some other control systems use adaptive control. Such methods as fuzzy sliding mode control and neural control [3,7], as well as vortex control [14] can be distinguished here, this group of methods ensures good oscillation attenuation and it is additionally characterised by high resistance in the case of wrongly determined drive parameters. Another approach to adaptive is presented in [8,18], here a superior system was used to retune controller setting, it continuously reproduces the unknown and variable parameter. Yet another, not so common, is the sliding mode control [9].In the case drives with an elastic coupling, the major challenge faced by the control system is the attenuation of the oscillation of electro-mechanical state variables. However, another issue which appears in the discussed group of drives is the limitation of the torsional torque amplitude in such a way that the lives of mechanical couplings can be extended, which improves the reliability of the whole drive system. However, the above described methods do not allow to introduce the restriction of the values of state variables (torsional torque) in a simple way. The only restricted values is the set electromagnetic torque (control signal). In such systems the only way to limit the amplitude of internal state variables is weakening the dynamics of the whole system. Another approach which allows to introduce the value restrictions of a selected state variable is using the cascade control structure in which a separate, subordinate control loop is formed. Then it is possible to restrict such state variable [16]. One of the methods allowing to introduce the restrictions of the values of state variables is predictive control. This method allows to introduce limitations as early as the ...

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Application of the MPC to the Position Control of the Two-Mass Drive System

Cited by 75 publications

References 31 publications

An on-line trained neural controller with a fuzzy learning rate of the Levenberg–Marquardt algorithm for speed control of an electrical drive with an elastic joint

An on-line trained neural controller with a fuzzy learning rate of the Levenberg–Marquardt algorithm for speed control of an electrical drive with an elastic joint

Trajectory Tracking of an Omni-Directional Wheeled Mobile Robot Using a Model Predictive Control Strategy

Comparison of the control methods of electrical drives with an elastic coupling allowing to limit the torsional torque amplitude

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