Performance optimization of PID controllers using fuzzy logic

Sam, Sneha Mariam; Angel, T. S. Shiny

doi:10.1109/icstm.2017.8089200

Cited by 14 publications

(3 citation statements)

References 5 publications

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“…Ziegler-Nichols method, 10 the Cohen-Coon method, 11 the integrated-model-control method, 12 and the gain-phase margin method, 13 2. The new PI tuning approaches that are based on smart techniques like genetic algorithms, 14 fuzzy logic, 15 and neural networks. 16 The traditional PI tuning approaches are less efficient to guarantee the stability and performance of the system to be controlled.…”

Section: The Traditional Pi Tuning Approaches Such Asmentioning

confidence: 99%

Energy optimization of an electric car using losses minimization and intelligent predictive torque control

Kraiem

Shaaban

2020

Journal of Algorithms & Computational Technology

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Autonomy is considered an important criterion that characterizes the performance of electric vehicles. It is represented by the distance that could be traveled by a fully electric vehicle which mainly depends on several parameters such as the vehicle model, type of battery, type of motor, etc. In this context, to improve the autonomy of electric vehicles, this paper represents an optimization study for the electric motor based on two contributions. The first devise an energy optimization algorithm to reduce the motor losses by calculation of the stator flux reference according to the electromagnetic torque and the rotation speed. The second is concerned with controller parameters adjustment using the Particle Swarm Optimization (PSO) technique to improve the efficacy and robustness of the drive. The performance of this strategy is evaluated in terms of torque, flux ripples, and transient response to step variations of the torque control. A comparative study of the designed PI controllers based on PSO with four other control algorithms and tuning methods is established in order to prove the efficiency of PI_PSO. The analysis, modeling, and simulation results are presented to verify the validity of the proposed overall optimization study.

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Section: The Traditional Pi Tuning Approaches Such Asmentioning

confidence: 99%

Energy optimization of an electric car using losses minimization and intelligent predictive torque control

Kraiem

Shaaban

2020

Journal of Algorithms & Computational Technology

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“…The response of a control system can be defined in terms of the error, the degree to which the system exceeds a set point, and the amount of oscillation around any set value. However, a PID controller, relying solely on the measurable system variable rather than the underlying process information, is widely applicable and has a long history of successful use in a in many applications [2][3][4][5][6]. A PID controller has three gain coefficients to meet some system performance criteria.…”

Section: Introductionmentioning

confidence: 99%

PID Kontrolörün Kazanç Katsayılarının Optimizasyonu için Farklı Yöntemlerin Karşılaştırılması

YILMAZ

2023

Kırklareli Üniversitesi Mühendislik Ve Fen Bilimleri Dergisi

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Proportional-Integral-Derivative (PID) controller is widely used in technical applications due to its robustness and ease of application. The gain values of a PID controller have a strong impact on performance criteria such as settling time, rise time, and overshoot. Systems that possess at least one of these criteria are considered strong control systems. Adjusting the parameters to obtain the best step response of closed loop control systems is a complex operation. While long known methods such as the Ziegler-Nichols (ZN) method were initially used to compute parameter values, today, metaheuristic algorithms are employed. This article focuses on the tuning of gain parameters of a PID controller using metaheuristic algorithms for the control of a system with a third-order transfer function. The proposed algorithms are Fuzzy Logic (FL), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). The comparison results concluded that GA is the best algorithm for optimization.

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“…Hence, the researchers have focused on more sophisticated problems for WECS control, such as backstepping based nonlinear control [9], fuzzy logic control [10], sliding mode control [11] etc. The major drawback of the reported fuzzy inference system is that it is completely based on the knowledge and experience of the designer [12]. Intelligent control algorithms such as neural network (NN) [13], neuro-fuzzy control (NFC) [14], adaptive network-based fuzzy inference system (ANFIS) [15], genetic algorithm [16], particle swarm optimization [17], artificial bee colony algorithm [18], grey wolf optimization [19] have been gaining popularity over the last decade.…”

Section: Introductionmentioning

confidence: 99%

AFIS based Advanced Power Control Scheme for Grid-Connected DFIG-Driven WECS

Amin¹,

Uddin²

2022

GJRE

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In wind energy conversion system, extraction of optimum power and efficient operation are two major challenges. In grid connected mode, a doubly fed induction generator (DFIG) control unit is designed to operate at optimum speed to deliver maximum output power in the grid while the voltage, frequency and harmonic regulations need to be fulfilled. Vector control associated with proportional-integral (PI) controllers has been widely applied in wind farms for reliable power regulation of DFIG. As DFIG based wind energy conversion system (WECS) experiences strong nonlinearity and uncertainties originated from the aerodynamics of the wind turbine and magnetic saturation of the generator, different adaptive and nonlinear control schemes have been proposed to resolve the problems associated with fixed-gain PI controllers.

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Performance optimization of PID controllers using fuzzy logic

Cited by 14 publications

References 5 publications

Energy optimization of an electric car using losses minimization and intelligent predictive torque control

Energy optimization of an electric car using losses minimization and intelligent predictive torque control

PID Kontrolörün Kazanç Katsayılarının Optimizasyonu için Farklı Yöntemlerin Karşılaştırılması

AFIS based Advanced Power Control Scheme for Grid-Connected DFIG-Driven WECS

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