An Optimal Robust State Feedback Controller for the AVR System-Based Harris Hawks Optimization Algorithm

Mary, Ali Hussien; Miry, Abbas H.; Miry, Mohammed Hussein

doi:10.1080/15325008.2021.1908456

Cited by 8 publications

(3 citation statements)

References 17 publications

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“…Further enhancements to the 2DOF state-feedback PI controller employed a dynamic weighted state-feedback method [20], offering flexibility under varying system conditions. For the AVR, a distinct robust state-feedback controller was proposed, considering bounded system uncertainties and external disturbances in its design [39]. To tackle AVR system uncertainties, a non-fragile PID controller, optimized with a genetic algorithm, and a model predictive controller using Angle of Arrival (AOA) optimization were also introduced in [40,41], respectively.…”

Section: Introductionmentioning

confidence: 99%

Augmenting the Stability of Automatic Voltage Regulators through Sophisticated Fractional-Order Controllers

Mohamed,

Aly,

Alhosaini

et al. 2024

Fractal Fract

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The transition from traditional to renewable energy sources is a critical issue in current energy-generation systems, which aims to address climate change and the increased demand for energy. This shift, however, imposes additional burdens on control systems to maintain power system stability and quality within predefined limits. Addressing these challenges, this paper proposes an innovative Modified Hybrid Fractional-Order (MHFO) automatic voltage regulator (AVR) equipped with a fractional-order tilt integral and proportional derivative with a filter plus a second-order derivative with a filter FOTI-PDND2N2 controller. This advanced controller combines the benefits of a (FOTI) controller, known for enhancing dynamic performance and steady-state response, with a (PDND2N2) controller to improve system robustness and adaptability. The proposed MHFO controller stands out with its nine tunable parameters, providing more extensive control options than the conventional three-parameter PID controller and the five-parameter FOPID controller. Furthermore, a recent optimization approach using a growth optimizer (GO) has been formulated and applied to optimally adjust the MHFO controller’s parameters simultaneously. The performance of the proposed AVR based on the MHFO-GO controller is scrutinized by contrasting it with various established and developed optimization algorithms. The comparative study shows that the AVR based on the MHFO-GO controller surpasses other AVR controllers from the stability, robustness, and dynamic response speed points of view.

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Section: Introductionmentioning

confidence: 99%

Augmenting the Stability of Automatic Voltage Regulators through Sophisticated Fractional-Order Controllers

Mohamed,

Aly,

Alhosaini

et al. 2024

Fractal Fract

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“…several metaheuristic methods used to tune the parameters controlling on the AVR system are methods grasshopper optimization algorithm [7]- [9]. Whale optimization algorithm [10]- [12], sine-cosine-algorithm [13]- [15], Harris Hawks optimization [16]- [18] and black widow algorithm [19], [20] .…”

Section: Introductionmentioning

confidence: 99%

Cooperation search algorithm for tuning automatic voltage regulator system

Aribowo

Suprianto

Kartini

2022

IJPEDS

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<p>The cooperation search algorithm (CSA) duplicates teamwork in every part of the company. This paper presents an approach to setting automatic voltage regulator (AVR) with proportional-integral-derivative (PID) control based on CSA. To get the performance of the proposed method, this paper uses the maximum overshoot, rise time, settling time, and error. This paper uses the whale optimization algorithm (WOA), grasshopper optimization algorithm (GOA), particle swarm optimization (PSO), and sine-cosine algorithm (SCA) methods as a comparison in measuring the performance of the CSA method which is used as the optimization of PID parameters on the AVR. From the simulation, the application of the CSA method to get the optimal PID parameter value has an average result that can reduce the maximum overshoot by 10.97% compared to the GOA, PSO and SCA methods.</p>

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“…In recent years, researchers have used many intelligent methods for tuning PID parameters, such as the Flower pollination algorithm, Teaching learning based optimization [29], Artificial Bee Colony Algorithm [30] [31], Grey Wolf Optimization [32], Firefly Algorithm [33], Differential Evolution [34], Genetic Algorithm [35], Sine Cosine Algorithm [36] [37], Water Wave Optimization [38]. Researchers began to study the intelligent behavior of animals to be applied in solving optimization problems such as Whale Optimizer Algorithm [39], Fish Migration Optimization Algorithm [40], Grey Wolf Optimizer [41], Artificial Bee Colony Algorithm [42], Bat Algorithm [43], Harris Hawk Optimization [44] [45]. Several optimization methods based on conventional methods and intelligent methods have been widely used to optimize PID parameters on DC motors [46][47] [48][49] [50].…”

mentioning

confidence: 99%

Particle Swarm Optimization (PSO) Tuning of PID Control on DC Motor

Rahayu

Ma’arif

Çakan

2022

IJRCS

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The use of DC motors is now common because of its advantages and has become an important necessity in helping human activities. Generally, motor control is designed with PID control. The main problem that is often discussed in PID is parameter tuning, namely determining the value of the Kp, Ki, and Kd parameters in order to obtain optimal system performance. In this study, one method for tuning PID parameters on a DC motor will be used, namely the Particle Swarm Optimization (PSO) method. Parameter optimization using the PSO method has stable results compared to other methods. The results of tuning the PID controller parameters using the PSO method on the MATLAB Simulink obtained optimal results where the value of Kp = 8.9099, K = 2.1469, and Kd = 0.31952 with the value of rise time of 0.0740, settling time of 0.1361 and overshoot of 0. Then the results of hardware testing by entering the PID value in the Arduino IDE software produce a stable motor speed response where Kp = 1.4551, Ki= 1.3079, and Kd = 0.80271 with a rise time value of 4.3296, settling time of 7.3333 and overshoot of 1.

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An Optimal Robust State Feedback Controller for the AVR System-Based Harris Hawks Optimization Algorithm

Cited by 8 publications

References 17 publications

Augmenting the Stability of Automatic Voltage Regulators through Sophisticated Fractional-Order Controllers

Augmenting the Stability of Automatic Voltage Regulators through Sophisticated Fractional-Order Controllers

Cooperation search algorithm for tuning automatic voltage regulator system

Particle Swarm Optimization (PSO) Tuning of PID Control on DC Motor

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