Genetic algorithm applied to state feedback control design

Oliveira, Phelipe Sena; Barros, Luciano Sales; Silveira, Luiz Gonzaga de Queiroz

doi:10.1109/tdc-la.2010.5762925

Cited by 11 publications

(7 citation statements)

References 2 publications

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“…The procedure used with GA controller method has three stages, reproduction, crossover and mutation. This procedure can be described briefly with following sequence steps [ 5].…”

Section: Genetic Algorithm Methodsmentioning

confidence: 99%

Genetic Algorithm (GA) Based Optimal Feedback Control Weighting Matrices Computation -ENG

Abdulla¹,

Ahmed²,

Attya³

2013

(AREJ)

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Linear Quadratic Regulator (LQR) is one of the most interesting control techniques adopted as a control strategy in state feedback. These types of techniques achieve good results but suffer from the problem of trial and error involved in the computation of weight matrices. The trial and error technique leads to hard tuning of the LQR controller parameters. This of course will lead to difficulty in reaching the optimal system performance. The paper attempts to solve the above difficulty via the selection of the LQR weight matrices using Genetic Algorithm GA. This proposed solution will avoid the trail and error involved in the state feedback technique. The proposed solution has been adopted in the design of position controller of a robot arm and the results of computer simulation have shown that the proposed solution fulfill specifications, for minimum overshoot , settling and rising times.

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“…The procedure used with GA controller method has three stages, reproduction, crossover and mutation. This procedure can be described briefly with following sequence steps [ 5].…”

Section: Genetic Algorithm Methodsmentioning

confidence: 99%

Genetic Algorithm (GA) Based Optimal Feedback Control Weighting Matrices Computation -ENG

Abdulla¹,

Ahmed²,

Attya³

2013

(AREJ)

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“…This algorithm is different from other algorithms by the working principle where it deals with the coding of the parameters rather than the parameters themselves. Also, in certain cases, the binary coding has been suggested [13].…”

Section: Genetic Algorithmmentioning

confidence: 99%

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

Ahmed¹

2020

IJECE

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For multiple input-multiple output (MIMO) systems, the most common control strategy is the linear quadratic regulator (LQR) which relies on state vector feedback. Despite this strategy gives very good result, it still has trial and error procedure to select the values of its weight matrices which plays a important role in reaching to the desiered system performance. In order to overcome this problem, the Genetic algorithm is used. The design of genetic algorithm based linear quadratic regulator (GA-LQR) utilized Integral time absolute error (ITAE) as a cost function for optimization. The propsed procedure is implemented on a linear model of gas turbine to control the generator spool’s speed and the output power.

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“…SaDE algorithm is best compared to DESAP and FADE. Several optimization techniques like Particle Swarm Optimization (PSO) [15], Genetic Algorithm (GA) [16], Differential Evolution (DE) [17], bacteria foraging [18] are proposed in the literature to tune the parameters of PI controller to Wind based DFIG system. The performance of the system can be enhanced by adopting SaDE.…”

Section: Introductionmentioning

confidence: 99%

Optimal Tuning of PI Controllers for DFIG-Based Wind Energy System using Self-adaptive Differential Evolution Algorithm

Alluri¹,

Tummala²,

Pv³

et al. 2019

IJEEI

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The rapid infiltration of wind energy systems in the power system is causing huge power fluctuations due to varying wind speeds and fault occurrences. Here objective is to tune the Proportional Integral (PI) controller parameters. Formulation of objective function with Integral Square Error (ISE) concept is to minimize the errors of active power, terminal voltage and DC bus voltage. To tune these control parameters a Self-adaptive Differential Evolution (SaDE) approach is adopted. These optimized parameters are applied to a 9 MW wind generation system using Doubly-Fed Induction Generator's (DFIG) and compared this system with the actual system in MATLAB/Simulink. The results obtained here are found to be superior in terms of settling time, peak overshoot and undershoots, voltage profile. 352the major challenge in wind energy based DFIG system is selection of optimum PI control parameters that improves system performance and stability. * * DFIG Pitch Controller RSC Controller GSC Controller Vg ig VDC_ref Vs_ref VDC ir Vr wr tsh tm Drive Train RSC GSC wref L AC Bus b Vs Vw P s P r Wr < Ws Wr > WsStep-up Transformer P s + P r Hemanth KR Alluri received B.

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Genetic algorithm applied to state feedback control design

Cited by 11 publications

References 2 publications

Genetic Algorithm (GA) Based Optimal Feedback Control Weighting Matrices Computation -ENG

Genetic Algorithm (GA) Based Optimal Feedback Control Weighting Matrices Computation -ENG

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

Optimal Tuning of PI Controllers for DFIG-Based Wind Energy System using Self-adaptive Differential Evolution Algorithm

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