Optimal Pid Tuning With Genetic Algorithms for Non Linear Process Models

Herrero, J. M.; Blasco, X.; Martínez, Miguel Á.; Salcedo, J. V.

doi:10.3182/20020721-6-es-1901.00658

Cited by 41 publications

(23 citation statements)

References 4 publications

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“…These chromosomes are the encoded representations of all the parameters of the solution. Each chromosomes is compared to other chromosomes in the population and awarded fitness rating that indicates how successful this chromosomes to the latter [7][8][9][10][11][12][13]. There are three main stages of a genetic algorithm, these are known as reproduction, crossover and mutation [8].…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

Voltage Stability for a Photovoltaic System Connected to Grid by Using Genetic Algorithm Technique

Mostafa¹,

Bahgaat²,

sayed³

et al. 2017

IJGDC

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Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

Voltage Stability for a Photovoltaic System Connected to Grid by Using Genetic Algorithm Technique

Mostafa¹,

Bahgaat²,

sayed³

et al. 2017

IJGDC

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“…Pereira and Pinto provide a tuning and system identification procedure [15]. Herrero et al provide a similar implementation using GAs [16].…”

Section: A Pid Tuning Surveymentioning

confidence: 99%

Online Evolutionary Swarm Algorithm for Self-Tuning Unmanned Flight Control Laws

Ghiglino

Forshaw

Lappas

2015

Journal of Guidance, Control, and Dynamics

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Nomenclature a p , b p , c p = fitness function weightings B = body frame B cm = center of mass of the vehicle in B D = D gain for proportional integral derivative e = error, generic Fe = fitness function I = inertial frame I B Bcm B = inertial tensor of B cm with respect to B, kg · m 2 , expressed in B I, k i = I gain for proportional integral derivative P, k p = P gain for proportional integral derivative q e = quaternion error from B to I r = position, generic, m u = input to the plant v = velocity, generic, m∕s v x , v y , v z = velocity of B cm with respect to I, m∕s, expressed in B x, y, z = position of B cm from I o , m, expressed in I x c , y c , z c= position setpoints, m δ c , δ r , δ p , δ y = thrust, roll, pitch, yaw commands ζ = damping ratio ϕ, θ, ψ = Euler angles from B to I, deg ψ c = yaw setpoint, deg ω = linear control bandwidth I ω B B = angular rate from B to I, rad∕s, expressed in B

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“…The use of this technique for automatic tuning of PID controllers is recent and some works can be cited [3][4][5][6][7][8]. These works focus on tuning of single PI control loops in a monovariable framework.…”

Section: Introductionmentioning

confidence: 99%

PID control of MIMO process based on rank niching genetic algorithm

et al. 2007

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Non-linear multiple-input multiple-output (MIMO) processes which are common in industrial plants are characterized by significant interactions and nonlinearities among their variables. Thus, tuning several controllers in complex industrial plants is a challenge for process engineers and operators. An approach for adjusting the parameters of n proportional-integral-derivative (PID) controllers based on multiobjective optimization and genetic algorithms (GA) is presented in this paper. A modified genetic algorithm with elitist model and niching method is developed to guarantee a set of solutions (set of PID parameters) with different tradeoffs regarding the multiple requirements of the control performance. Experiments considering a fluid catalytic cracking (FCC) unit, under PI and dynamic matrix control (DMC) are carried out in order to evaluate the proposed method. The results show that the proposed approach is an alternative to classical techniques as ZieglerNichols rules and others.

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Optimal Pid Tuning With Genetic Algorithms for Non Linear Process Models

Cited by 41 publications

References 4 publications

Voltage Stability for a Photovoltaic System Connected to Grid by Using Genetic Algorithm Technique

Voltage Stability for a Photovoltaic System Connected to Grid by Using Genetic Algorithm Technique

Online Evolutionary Swarm Algorithm for Self-Tuning Unmanned Flight Control Laws

PID control of MIMO process based on rank niching genetic algorithm

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