Optimal tuning linear quadratic regulator for gas turbine by genetic algorithm using integral time absolute error

Ahmed, Jamal M.

doi:10.11591/ijece.v10i2.pp1367-1375

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…Surge protection line-is the protection line prior of reaching SLL by operational point. The "SPL" coordinates are defined as (9). Where, 𝐾 𝑆𝑃𝐿 is the SPL coefficient, by default setpoint is 1.0404 and could be adjusted by user through the dialog box.…”

Section: Anti-surge Controller Developmentmentioning

confidence: 99%

Centrifugal compressor anti-surge control system modelling

Batayev

Suleimenov

Batayeva³

2022

IJECE

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<span>From the middle of XX century, natural gas is an important mineral, widely used in the energy sector. Transportation of natural gas is carried out via gas pipeline networks and compression stations. One of the key features which need to be implemented for any centrifugal gas compressor is a surge protection. This article describes the method and develops software application intended for simulation and study of surge protection system of a centrifugal compressor used in modern gas compression stations. Within the article research method, modelling environment’s block diagram, proposed algorithms and results are described. For surge cases control and prediction, Anti-surge control block implemented which based on practical experience and centrifugal compressor theory. To avoid complicated energy balancing differential equations the volumetric flow calculation algorithm proposed which is used in combination with Redlich-Kwong equation of state. Developed software’s adequacy test performed through modeling of one-stage gas compression scheme at rated speed with comparison of parameters with reference commercial software and verification of the anti-surge control system.</span>

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Section: Anti-surge Controller Developmentmentioning

confidence: 99%

Centrifugal compressor anti-surge control system modelling

Batayev

Suleimenov

Batayeva³

2022

IJECE

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“…Weight selection is often done by trial and error or using the desired response based on the analysis method [24] [25]. Recently, many studies have proposed using the genetic algorithm (GA) method to obtain the LQR weighting matrices, which results in desired control performances more quickly and easily [26] [27] [28].…”

Section: Introductionmentioning

confidence: 99%

Swarm control of an unmanned quadrotor model with LQR weighting matrix optimization using genetic algorithm

Joelianto

Christian

Samsi

2020

J. Mechatron. Electr. Power Veh. Technol.

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Unmanned aerial vehicle (UAV) quadrotors have developed rapidly and continue to advance together with the development of new supporting technologies. However, the use of one quadrotor has many obstacles and compromises the ability of a UAV to complete complex missions that require the cooperation of more than one quadrotor. In nature, one interesting phenomenon is the behaviour of several organisms to always move in flocks (swarm), which allows them to find food more quickly and sustain life compared with when they move independently. In this paper, the swarm behaviour is applied to drive a system consisting of six UAV quadrotors as agents for flocking while tracking a swarm trajectory. The swarm control system is expected to minimize the objective function of the energy used and tracking errors. The considered swarm control system consists of two levels. The first higher level is a proportional – derivative type controller that produces the swarm trajectory to be followed by UAV quadrotor agents in swarming. In the second lower level, a linear quadratic regulator (LQR) is used by each UAV quadrotor agent to follow a tracking path well with the minimal objective function. A genetic algorithm is applied to find the optimal LQR weighting matrices as it is able to solve complex optimization problems. Simulation results indicate that the quadrotors' tracking performance improved by 36.00 %, whereas their swarming performance improved by 17.17 %.

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