Design and performance comparison of variable parameter nonlinear PID controller and genetic algorithm based PID controller

Korkmaz, Mehmet; Aydoğdu, Ömer; Doğan, Hüseyin

doi:10.1109/inista.2012.6246935

Cited by 74 publications

(49 citation statements)

References 7 publications

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“…On the other hand, the set of tunings will end up with different parameters. So, the approach of speed controller tuning is plenty of motivation to develop an algorithm [15]. The tuning based on Ziegler-Nichols method consist of the following steps:…”

Section: Simulation and Hardware Setupmentioning

confidence: 99%

Comparison Performances of Indirect Field Oriented Control for Three-Phase Induction Motor Drives

Aziri¹,

Patakor²,

Sulaiman³

et al. 2017

IJPEDS

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This paper presents the comparative performances of Indirect Field Oriented Control (IFOC) for the three-phase induction motor. Recently, the interest of widely used the induction motor at industries because of reliability, ruggedness and almost free in maintenance. Thus, the IFOC scheme is employed to control the speed of induction motor. Therefore, P and PI controllers based on IFOC approach are analyzed at differences speed commands with no load condition. On the other hand, the PI controller is tuned based on Ziegler-Nichols method by using PSIM software which is user-friendly for simulations, design and analysis of motor drive, control loop and the power converter in power electronics studies. Subsequently, the simulated of P controller results are compared with the simulated of PI controller results at difference speed commands with no load condition. Finally, the simulated results of speed controllers are compared with the experimental results in order to explore the performances of speed responses by using IFOC scheme for three-phase induction motor drives.

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Section: Simulation and Hardware Setupmentioning

confidence: 99%

Comparison Performances of Indirect Field Oriented Control for Three-Phase Induction Motor Drives

Aziri¹,

Patakor²,

Sulaiman³

et al. 2017

IJPEDS

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“…1 c and 2 c is the weighting factor belonging to (1,2). Using the above equation, a certain velocity, the global optimal position and the individual optimal position can be calculated with above evolutions.…”

Section: B Particle Swarm Optimization (Pso)mentioning

confidence: 99%

“…The acceleration constants 1 c and 2 c are set 1.2 and 1.2. The particle initial location range is (0,300), corresponding to three-dimensional parameters of PID.…”

Section: The Simulation Experimentsmentioning

confidence: 99%

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A New Method of Speed Control for Induction Motor Based On Improved Particle Swarm Optimization

Yi¹,

Sui²,

Yu³

2016

JCTECS

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Abstract-Optimization techniques are becoming more popular for the improvement in control of induction motor. Many intelligent algorithms have been used to improve performance of induction motor so for including particle swarm optimization. However, the improved performance may be limited on account of inertia coefficient in particle swarm optimization, which lead to the unbalance between the searching step and searching precision. In this paper, a variable-step nonlinear dynamic inertia weight of particle swarm optimization speed controller is proposed to improve the performance of an induction motor. The experiment results show that the proposed method has excellent performance.

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“…They can be categorized into two types. One is the nonlinear PID controller introduced by Korkmaz et al [9] and Isayed [10], whose gains are gradually changed based on error and/or error rate. The other is the nonlinear PID controller introduced by Seraji [11] and Zaidner et al [12], where a nonlinear gain in cascade with a linear PID controller produces the scaled error.…”

Section: Introductionmentioning

confidence: 99%

Temperature Control of a Regasification System for LNG fuelled Marine Engines

Jin¹,

So²

2018

Eighth International Conference on Advances in Mechanical, Aeronautical and Production Techniques - MAPT 2018

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Abstract-This paper presents a nonlinear PID (NPID) controller which controls the glycol temperature of a regasification system for LNG-fuelled engines. The NPID controller has a parallel structure of linear PD action and nonlinear I action. A nonlinear function is employed to scale the error as input of the integral and implemented as a TakagiSugeno (T-S) fuzzy model. The controller parameters are optimally tuned by assuming that it is operated in both warming-up and running modes, and the parameters are switched according to the operation mode. Furthermore, the stability problem of the overall system is verified based on the circle criterion. A set of simulation works is carried out to validate the efficiency of the proposed controller.

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Design and performance comparison of variable parameter nonlinear PID controller and genetic algorithm based PID controller

Cited by 74 publications

References 7 publications

Comparison Performances of Indirect Field Oriented Control for Three-Phase Induction Motor Drives

Comparison Performances of Indirect Field Oriented Control for Three-Phase Induction Motor Drives

A New Method of Speed Control for Induction Motor Based On Improved Particle Swarm Optimization

Temperature Control of a Regasification System for LNG fuelled Marine Engines

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