Study of tensor product model alternatives

Kuczmann, Miklós

doi:10.1002/asjc.2446

Cited by 13 publications

(3 citation statements)

References 36 publications

(39 reference statements)

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“…The main purpose of TP-based model transformation is to map a given Linear Parameter Varying (LPV) or quasi-Linear Parameter Varying (qLPV) state-space model onto a TP model made of Linear Time Invariant (LTI) systems using the Higher Order Singular Value Decomposition. The TP-based model transformation technique was successfully applied recently to tower crane system modeling [92], pendulum cart system modeling [93], [94], black box system modeling [95], induction machine modeling [96], and white noise process modeling [97]. This transformation technique was also used in the TP-based controller design; TP controllers were designed for a big number of processes including more recent ones such as Lotka-Volterra fractional order model [98] and aeroelastic systems [99], but they could be applied to other systems as, for instance, networked control systems [100].…”

Section: Resultsmentioning

confidence: 99%

Performance Improvement of Low-Cost Iterative Learning-Based Fuzzy Control Systems for Tower Crane Systems

Precup¹,

Roman²,

Hedrea³

et al. 2022

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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This paper is dedicated to the memory of Prof. Ioan Dzitac, one of the fathers of this journal and its founding Editor-in-Chief till 2021. The paper addresses the performance improvement of three Single Input-Single Output (SISO) fuzzy control systems that control separately the positions of interest of tower crane systems, namely the cart position, the arm angular position and the payload position. Three separate low-cost SISO fuzzy controllers are employed in terms of first order discrete-time intelligent Proportional-Integral (PI) controllers with Takagi-Sugeno-Kang Proportional-Derivative (PD) fuzzy terms. Iterative Learning Control (ILC) system structures with PD learning functions are involved in the current iteration SISO ILC structures. Optimization problems are defined in order to tune the parameters of the learning functions. The objective functions are defined as the sums of squared control errors, and they are solved in the iteration domain using the recent metaheuristic Slime Mould Algorithm (SMA). The experimental results prove the performance improvement of the SISO control systems after ten iterations of SMA.

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Section: Resultsmentioning

confidence: 99%

Performance Improvement of Low-Cost Iterative Learning-Based Fuzzy Control Systems for Tower Crane Systems

Precup¹,

Roman²,

Hedrea³

et al. 2022

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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“…The TP model transformation and its extension, LMI and HOSVD based methods are a popular research field today, there are many publications about the investigation; [4][5][6][7][8][9][10][11][12][13]. Articles on practical applications of the TP model transformation: [14][15][16][17][18][19][20][21][22][23][24]. Additional papers used for current article: [25,26].…”

Section: Introductionmentioning

confidence: 99%

LMI feasibility analysis of 2DOF NATA model

Wéber,

Kuczmann

2024

Pollack

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Present paper shows the different types of tensor product model based linear matrix inequality controller design and feasibility analysis of two degrees of freedom aeroelastic wing section model. The tensor product models are based on reducing or removing the nonlinear behavior of the system and weighting functions. The linear matrix inequality based method results globally asymptotically stable system. The goal of the paper is to examine that selecting and varying the transformation space influences the feasibility of the linear matrix inequality based controller. The paper gives a comparison between the different tensor product models in terms of controller performance. The linear matrix inequality gives feasible solution for the controller design if the transformation space is selected adequately.

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“…Therefore, high performance and easy to implement control methods can be achieved by combining FOC and TP transformation, similarly to [40][41][42]. However, the idea of [43] shows that a TP model has a huge number of alternatives. These variants can be readily derived by the TP model transformation that can variate the number of fuzzy rules, the number of antecedent and consequent sets, and, further, the shape of the antecedent fuzzy sets.…”

Section: Introductionmentioning

confidence: 99%

Tensor Product Alternatives for Nonlinear Field-Oriented Control of Induction Machines

Kuczmann,

Horváth

2024

Electronics

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The paper presents a nonlinear field-oriented control technique based on the tensor product representation of the nonlinear induction machine model and the solvability of linear matrix inequalities. The nonlinear model has 32 quasi linear parameter-varying equivalent variants, and it is shown that only half of the models result in feasible controller. Two control goals are realized: torque control and speed control. The controller is a nonlinear state feedback controller completed by integral action. A new block diagram is investigated for speed control. The controller gains are designed by the solution of linear matrix inequalities to solve the Lyapunov inequality to obtain a stable and fast response and constraints on the control signal. The presented methods are verified and compared by simulations.

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Study of tensor product model alternatives

Cited by 13 publications

References 36 publications

Performance Improvement of Low-Cost Iterative Learning-Based Fuzzy Control Systems for Tower Crane Systems

Performance Improvement of Low-Cost Iterative Learning-Based Fuzzy Control Systems for Tower Crane Systems

LMI feasibility analysis of 2DOF NATA model

Tensor Product Alternatives for Nonlinear Field-Oriented Control of Induction Machines

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