Comperative Performance Analysis of PMSM Drive Using MPSO and ACO Techniques

Yadav, Deepti; Verma, Arunima

doi:10.11591/ijpeds.v9.i4.pp1510-1522

Cited by 14 publications

(16 citation statements)

References 18 publications

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“…From the comparison shown in Figure 12, it can be seen that the terminal voltage response with varied reference voltage has small overshoot and short settling time compared to the terminal voltage response with a fixed reference voltage. The transient response specifications (overshoot percentage, the settling time and the rise time) are calculated using the relationship provided by the reference [25]. The comparative results obtained are summarized in Table 5.…”

Section: Imrovement Of the Overshootmentioning

confidence: 99%

Output voltage regulation of synchronous generator using PSO algorithm-based PI controller

Habbi¹,

Gabour²,

Boudissa³

et al. 2021

IJPEDS

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In this paper a regulation of the terminal voltage of synchronous generator (SG) has been developed. Here, the nonlinear model of the SG is used directly without requirement for a linearized mathematical model of the generator. A proportional integral PI-controller is used to adjust the duty cycle of the DC chopper of step-down type for controlling the field voltage and consequently the output voltage of the generator. Furthermore, Particle swarm optimization (PSO) algorithm is employed as an optimization technique for tuning the optimal parameters of the PI controller (Kp and Ki). This is achieved by the minimization of the quadratic output error between the reference voltage and the output voltage calculated from the adopted model at the same time. In order to test the performance of the PSO-PI controller, results are compared with the genetic algorithm (GA). Moreover, to reduce the overshoot resulting in the response of the terminal voltage, a varied reference voltage is adopted. Results obtained show the superiority of the varied reference voltage to decrease the overshoot versus the fixed reference voltage.

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Section: Imrovement Of the Overshootmentioning

confidence: 99%

Output voltage regulation of synchronous generator using PSO algorithm-based PI controller

Habbi¹,

Gabour²,

Boudissa³

et al. 2021

IJPEDS

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“…In the Nineteen Sixties, Caputo presented a popular form to calculate the differ-integral. A fractional derivative of order "λ" with variation in "t" domain and with initial condition t = 0 is become as [8]- [10]:…”

Section: The Differ-integral and Fractional Order Systemsmentioning

confidence: 99%

Fractional-order PID controller for permanent magnet DC motor based on PSO algorithm

Hasan

Rashad

2019

IJPEDS

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<span>This paper proposes the fractional-order proportional integral derivative (FOPID) controller, as a speed controller for permanent magnet direct current (PMDC) motor, instead of the traditional integer-order PID controller. The FOPID controller is the general form of the integer-order PID controllers, which found wide applications in all engineering fields. In this work a hybrid M-file and SIMULINK program is developed to simulate the overall system, the FOPID controller has five associated parameters. The optimum values of those parameters are found out by using particle swarm optimization technique. Simulation results show excellent command speeds tracking and superior dynamic response in conjunction with that of the integer-order PID controller. The proposed controller shows a high ability to overcome any external disturbance the system may be exposed; also, it performs a high degree of robustness to control the system in motoring and regenerative operating modes.</span>

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“…Fractional order PID controller tuned by bat algorithm for robot trajectory control (Mohammad A. Faraj) 75 possible due to developments of an evolutionary optimization algorithms. Several bio inspired optimization techniques has been tested and implemented as powerful tool for solving an optimization problem for different applications such as : genetic algorithm GA [17], particle swarm optimization PSO [18,19], ant colony optimization (ACO) [20], differential evolution (DE), artificial bee colony [21], Cuckoo search algorithms (CSA) [22,23], bat algorithm [24]. Many of these algorithms has been focused on tuning FOPID controller parameters for robotic manipulators [17,18,22,23,25].…”

Section: Introductionmentioning

confidence: 99%

Fractional order PID controller tuned by bat algorithm for robot trajectory control

Faraj

Abbood

2021

IJEECS

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<span>This paper deals with implementing the tuning process of the gains of fractional order proportional-integral-derivative (FOPID) controller designed for trajectory tracking control for two-link robotic manipulators by using a Bat algorithm. Two objective functions with weight values assigned has been utilized for achieving the minimization operation of errors in joint positions and torque outputs values of robotic manipulators. To show the effectiveness of using a Bat algorithm in tuning FOPID parameters, a comparison has been made with particle swarm optimization algorithm (PSO). The validity of the proposed controllers has been examined in case of presence of disturbance and friction. The results of simulations have clearly explained the efficiency of FOPID controller tuned by Bat algorithm as compared with FOPID controller tuned by PSO algorithm. </span>

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Comperative Performance Analysis of PMSM Drive Using MPSO and ACO Techniques

Cited by 14 publications

References 18 publications

Output voltage regulation of synchronous generator using PSO algorithm-based PI controller

Output voltage regulation of synchronous generator using PSO algorithm-based PI controller

Fractional-order PID controller for permanent magnet DC motor based on PSO algorithm

Fractional order PID controller tuned by bat algorithm for robot trajectory control

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