Tuning of a PID controller Using a Multi-objective Optimization Technique Applied to A Neutralization Plant

Popov, A.; Farag, A.; Werner, H.

doi:10.1109/cdc.2005.1583312

Cited by 25 publications

(17 citation statements)

References 8 publications

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“…[17], Popov et al [21] and Zielinski et al [24]. Robust controller optimization can be found in Gassmann et al [5], Knittel et al [16] and Kuhm et al [14].…”

Section: Mono-objective Controllers Optimizationmentioning

confidence: 99%

Multidisciplinary design optimization of an accumulator for a large elasticity range of flexible webs

Knittel¹,

Renaud²,

Kuhm³

2010

Int. J. Simul. Multidisci. Des. Optim.

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Abstract-This paper concerns the optimization of an accumulator used in an industrial elastic web processing plant (paper, fabric, polymer, metal …). A nonlinear model of an industrial accumulator is first detailed which enables to deduce a linear model. These models are derived from the physical laws describing web tension and velocity dynamics in each web span. The effects of time-varying rheological and mechanical parameters, such as web Young modulus, web length and rolls inertia, on accumulator dynamics and performances are analyzed. The second part presents several optimizations of industrial PI controllers using evolutionary algorithm for a realistic non-linear model, in conjunction with the controllers interpolation strategy. Finally, simulations made in the Matlab/Simulink software environment show performances improvements compared to hand tuned controllers.

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“…[17], Popov et al [21] and Zielinski et al [24]. Robust controller optimization can be found in Gassmann et al [5], Knittel et al [16] and Kuhm et al [14].…”

Section: Mono-objective Controllers Optimizationmentioning

confidence: 99%

Multidisciplinary design optimization of an accumulator for a large elasticity range of flexible webs

Knittel¹,

Renaud²,

Kuhm³

2010

Int. J. Simul. Multidisci. Des. Optim.

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“…MOO leads to a set of optimal solutions, i.e., Pareto optimal solutions or non-dominated solutions (Colette and Siarry, 2002). In this context, many works (e.g., Bemporada and Munoz de la Penab, 2009;Muldera et al, 2009;Popov et al, 2005;Yang and Pedersen, 2006;Behroozsarand and Shaffei, 2010) were focused on the synthesis of controllers based on multiobjective optimization, which has received growing interest.…”

Section: F Ben Aicha Et Almentioning

confidence: 99%

A multivariable multiobjective predictive controller

Aicha¹,

Bouani²,

Ksouri³

2013

International Journal of Applied Mathematics and Computer Science

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Predictive control of MIMO processes is a challenging problem which requires the specification of a large number of tuning parameters (the prediction horizon, the control horizon and the cost weighting factor). In this context, the present paper compares two strategies to design a supervisor of the Multivariable Generalized Predictive Controller (MGPC), based on multiobjective optimization. Thus, the purpose of this work is the automatic adjustment of the MGPC synthesis by simultaneously minimizing a set of closed loop performances (the overshoot and the settling time for each output of the MIMO system). First, we adopt the Weighted Sum Method (WSM), which is an aggregative method combined with a Genetic Algorithm (GA) used to minimize a single criterion generated by the WSM. Second, we use the NonDominated Sorting Genetic Algorithm II (NSGA-II) as a Pareto method and we compare the results of both the methods. The performance of the two strategies in the adjustment of multivariable predictive control is illustrated by a simulation example. The simulation results confirm that a multiobjective, Pareto-based GA search yields a better performance than a single objective GA.

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“…(35)(36)(37) show a time domain representation of the errors. An optimized value of the PID controller gains can obtain good system behaviour capable of minimizing the performance criteria in the time domain.…”

Section: Objective Function Formulationmentioning

confidence: 99%

“…The objective function which is the PID performance criteria is formulated as a function of these errors (Integral Absolute Error-IAE, Integral of Time Square Error-ITSE and Integral of Square Error-ISE) as follow [36]:…”

Section: Objective Function Formulationmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Design of PID Controller for Deep Space Antenna Positioning Using Weighted Cultural Artificial Fish Swarm Algorithm

Salawudeen¹,

Mu’azu²,

Sha’aban³

et al. 2017

J Electr Electron Syst

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Tuning of a PID controller Using a Multi-objective Optimization Technique Applied to A Neutralization Plant

Cited by 25 publications

References 8 publications

Multidisciplinary design optimization of an accumulator for a large elasticity range of flexible webs

Multidisciplinary design optimization of an accumulator for a large elasticity range of flexible webs

A multivariable multiobjective predictive controller

Optimal Design of PID Controller for Deep Space Antenna Positioning Using Weighted Cultural Artificial Fish Swarm Algorithm

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