Optimal fractional order adaptive controllers for AVR applications

Ortiz-Quisbert, Marco E.; Duarte‐Mermoud, Manuel A.; Milla, Freddy; Castro‐Linares, R.; Lefranc, Gastón

doi:10.1007/s00202-016-0502-2

Cited by 46 publications

(44 citation statements)

References 22 publications

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“…Proof of Theorem 5: Consider the state-space equations given in (21). The state variable z(t) is changed to new variable μ(t) using the following relation:…”

Section: Appendixmentioning

confidence: 99%

“…This means that c s T b s = 0. Considering this fact and incorporating relations (21), (30), (32) and 33gives…”

Section: Appendixmentioning

confidence: 99%

“…In [20], a fractional‐order Lyapunov‐based adaptation mechanism has been proposed for velocity control of a permanent magnet synchronous motor. Fractional‐order MRAC (FOMRAC) and fractional‐order PID controllers have been applied to an automatic voltage regulator [21]. Moreover, the capability of the FOMRAC for BIS regulation during anaesthesia has been studied in [22].…”

Section: Introductionmentioning

confidence: 99%

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Control of depth of anaesthesia using fractional‐order adaptive high‐gain controller

Alavi

2019

IET Systems Biology

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This study presents a fractional-order adaptive high-gain controller for control of depth of anaesthesia. To determine the depth of anaesthesia, the bispectral index (BIS) is utilised. To attain the desired BIS, the propofol infusion rate (as the control signal) should be appropriately adjusted. The effect of the propofol on the human body is modelled with the pharmacokinetic-pharmacodynamic (PK/PD) model. Physical properties of the patient such as gender, age, height and a like determine the parameters of the PK/PD model. This necessitates us to employ an appropriate adaptive controller. To attain this goal, a fractional-order adaptive high-gain controller is constructed to solve the tracking problem for minimum phase systems with relative degree two (such as the PK/PD model). This leads to a time-varying gain adjusting according to a fractional-order adaptation mechanism. Simulation results performed on various patients (considering the external disturbance and the measurement noise) show the effectiveness of the proposed method.

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“…Proof of Theorem 5: Consider the state-space equations given in (21). The state variable z(t) is changed to new variable μ(t) using the following relation:…”

Section: Appendixmentioning

confidence: 99%

“…This means that c s T b s = 0. Considering this fact and incorporating relations (21), (30), (32) and 33gives…”

Section: Appendixmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Control of depth of anaesthesia using fractional‐order adaptive high‐gain controller

Alavi

2019

IET Systems Biology

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“…PSO and GA optimization algorithms are used to calculate the optimal coefficients of the proposed control systems. The results of the proposed controllers in terms of advantages and disadvantages are compared with the case study based on the literature results [17]. Modabbernia et al present an H ∞ controller as a new approach for the AVR system to demonstrate a solid operating performance.…”

Section: Introductionmentioning

confidence: 99%

Optimal Control of AVR System With Tree Seed Algorithm-Based PID Controller

Köse

2020

IEEE Access

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In this study, an optimal Tree-Seed Algorithm (TSA) algorithm-based Proportional-Integral-Derivative (PID) controller is proposed for automatic voltage regulator (AVR) system terminal tracking problem. PID controller gains K p, K i, and K d are optimized with the proposed TSA algorithm based on different objective functions. The TSA-based optimal PID controller's performance is compared with numerous PID controllers, which were developed using different meta-hermetic optimization algorithms in the literature. Several analysis methods including root locus, bode analysis, robustness, and disturbance rejection are studied and compared with reported works in the literature. It is shown that there is still a research gap to improve the tracking performance of the AVR system due to its importance in electrical systems. According to the obtained comparison results, it has been revealed that the proposed TSA-based PID controller improves tracking properties under load change thus it can be effectively used for synchronous generator automatic voltage regulator terminal voltage stability.

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“…The optimal design of the FOPID controller implies determining the parameters to satisfy defined optimization criteria (or fitness/objective function). In the available literature, the most used method for optimal tuning of the FOPID controller is based on metaheuristic algorithms [4][5][6][7][8][9][10][11][12]. Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are applied in [4,5,8,11] to determine the optimal values of the FOPID parameters.…”

Section: Introductionmentioning

confidence: 99%

Fractional Order PID Controller Design for an AVR System Using Chaotic Yellow Saddle Goatfish Algorithm

2020

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This paper presents a novel method for optimal tunning of a Fractional Order Proportional-Integral-Derivative (FOPID) controller for an Automatic Voltage Regulator (AVR) system. The presented method is based on the Yellow Saddle Goatfish Algorithm (YSGA), which is improved with Chaotic Logistic Maps. Additionally, a novel objective function for the optimization of the FOPID parameters is proposed. The performance of the obtained FOPID controller is verified by comparison with various FOPID controllers tuned by other metaheuristic algorithms. A comparative analysis is performed in terms of step response, frequency response, root locus, robustness test, and disturbance rejection ability. Results of the simulations undoubtedly show that the FOPID controller tuned with the proposed Chaotic Yellow Saddle Goatfish Algorithm (C-YSGA) outperforms FOPID controllers tuned by other algorithms, in all of the previously mentioned performance tests.

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Optimal fractional order adaptive controllers for AVR applications

Cited by 46 publications

References 22 publications

Control of depth of anaesthesia using fractional‐order adaptive high‐gain controller

Control of depth of anaesthesia using fractional‐order adaptive high‐gain controller

Optimal Control of AVR System With Tree Seed Algorithm-Based PID Controller

Fractional Order PID Controller Design for an AVR System Using Chaotic Yellow Saddle Goatfish Algorithm

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