Comparison of the electronic realization of the fractional-order system and its model

Dorčák, Ľ.; Terpák, Ján; Petráš, Ivo; Valsa, Juraj; Gonzalez, Emmanuel A.

doi:10.1109/carpathiancc.2012.6228627

Cited by 15 publications

(8 citation statements)

References 6 publications

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“…Another approach was presented based on a frequency domain in [23,24]. Moreover, a state-space design method based on feedback pole placement was presented in [25], and another method based on loop shaping using linear matrix inequalities (LMI) was provided in [26]. In general terms, to achieve an efficient design of FO-based PID controllers, parameter optimization represents a significant challenge since the optimal value determines the robustness of the DAB system.…”

Section: Related Workmentioning

confidence: 99%

A High-Performance Fractional Order Controller Based on Chaotic Manta-Ray Foraging and Artificial Ecosystem-Based Optimization Algorithms Applied to Dual Active Bridge Converter

Ruiz,

Pichardo,

Aly

et al. 2024

Fractal Fract

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Over the last decade, dual active bridge (DAB) converters have become critical components in high-frequency power conversion systems. Recently, intensive efforts have been directed at optimizing DAB converter design and control. In particular, several strategies have been proposed to improve the performance of DAB control systems. For example, fractional-order (FO) control methods have proven potential in several applications since they offer improved controllability, flexibility, and robustness. However, the FO controller design process is critical for industrializing their use. Conventional FO control design methods use frequency domain-based design schemes, which result in complex and impractical designs. In addition, several nonlinear equations need to be solved to determine the optimum parameters. Currently, metaheuristic algorithms are used to design FO controllers due to their effectiveness in improving system performance and their ability to simultaneously tune possible design parameters. Moreover, metaheuristic algorithms do not require precise and detailed knowledge of the controlled system model. In this paper, a hybrid algorithm based on the chaotic artificial ecosystem-based optimization (AEO) and manta-ray foraging optimization (MRFO) algorithms is proposed with the aim of combining the best features of each. Unlike the conventional MRFO method, the newly proposed hybrid AEO-CMRFO algorithm enables the use of chaotic maps and weighting factors. Moreover, the AEO and CMRFO hybridization process enables better convergence performance and the avoidance of local optima. Therefore, superior FO controller performance was achieved compared to traditional control design methods and other studied metaheuristic algorithms. An exhaustive study is provided, and the proposed control method was compared with traditional control methods to verify its advantages and superiority.

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Section: Related Workmentioning

confidence: 99%

A High-Performance Fractional Order Controller Based on Chaotic Manta-Ray Foraging and Artificial Ecosystem-Based Optimization Algorithms Applied to Dual Active Bridge Converter

Ruiz,

Pichardo,

Aly

et al. 2024

Fractal Fract

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show abstract

“…With this controller, more flexibility can be achieved in achieving control goals. Different design methods have been introduced for this type of controller, including: pole placement, 16 frequency domain strategy, 17 state space design, 18 and two‐layer with combined strategy 19 which is based on the controller design method (correct traditional order) and then, the efficiency of the designed control system is improved by adding a suitable fraction controller.…”

Section: Modelingmentioning

confidence: 99%

Compensation analysis of the active and reactive power of electric vehicles using optimized FOPID‐GA controller

Moghadam

Balochian

2023

Energy Storage

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Recently, the interest in using electric and environmentally friendly vehicles has risen increasingly. The most important challenge is charging these electric vehicles (EVs) at the right time based on the amount of charging demand, which if not paid attention to, can affect the network power profile. In this article, the effect of charging EVs in fast charging stations on the voltage profile and power factor in the IEEE Bus‐33 standard network is investigated. Therefore, a mathematical model is used to model the problem that the parameters of EV arrival time to charging stations, EV charging time, number of charging times, EV status of charging (SOC), distance traveled by the EV are considered as model variables. In this article, four charging stations are considered for each fast‐charging station. The power rate that each station in the fast‐charging station receives from the network is considered 200 kW. So, if all stations are charged simultaneously, the charging station will receive 800 kW of electricity from the grid. There are also 300 EVs in the network, each charging time in fast charging stations is less than 10 minutes. To control the charge and power of cars, the FOPID controller has been used to determine the optimal coefficients through the application of a genetic algorithm (GA). The simulation results showed that in the uncontrolled reactive power mode, the voltage range in the busbars to which the charging stations are connected is less than the standard value (0.95 p.u) in the interval, but using FOPID‐GA controller, we will be able to increase the voltage range and improve the power factor of the distribution system about 10.26% by controlling the reactive power of the vehicles.

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“…This identification method (chapter 3.3) was successfully used also for determining the parameters of the electronic realization of the FO system [51].…”

Section: Verification Of the Methodsmentioning

confidence: 99%

Identification of Fractional-Order Dynamical Systems Based on Nonlinear Function Optimization

'ak¹,

Gonzalez²,

'ak³

et al. 2013

Int. J. of Pure and Appl. Math.

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In general, real objects are fractional-order systems and also dynamical processes taking place in them are fractional-order processes, although in some types of systems the order is very close to an integer order. So we consider dynamical system whose mathematical description is a differential equation in which the orders of derivatives can be real numbers. With regard to this, in the task of identification, it is necessary to consider also the fractional order of the dynamical system. In this paper we give suitable numerical solutions of differential equations of this type and subsequently an experimental method of identification in the time domain is given. We will concentrate mainly on the identification of parameters, including the orders of derivatives, for a chosen structure of the dynamical model of the system. Under mentioned assump- tions, we would obtain a system of nonlinear equations to identify the system. More suitable than to solve the system of nonlinear equations is to formulate the identification task as an optimization problem for nonlinear function minimization. As a criterion we have considered the sum of squares of the vertical deviations of experimental and theoretical data and the sum of squares of the corresponding orthogonal distances. The verification was performed on systems with known parameters and also on a laboratory object.

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Comparison of the electronic realization of the fractional-order system and its model

Cited by 15 publications

References 6 publications

A High-Performance Fractional Order Controller Based on Chaotic Manta-Ray Foraging and Artificial Ecosystem-Based Optimization Algorithms Applied to Dual Active Bridge Converter

A High-Performance Fractional Order Controller Based on Chaotic Manta-Ray Foraging and Artificial Ecosystem-Based Optimization Algorithms Applied to Dual Active Bridge Converter

Compensation analysis of the active and reactive power of electric vehicles using optimized FOPID‐GA controller

Identification of Fractional-Order Dynamical Systems Based on Nonlinear Function Optimization

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