PID Controller Design Based on Global Optimization Technique with Additional Constraints

Ožana, Štěpán; Docekal, Tomas

doi:10.1515/jee-2016-0023

Cited by 21 publications

(8 citation statements)

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“…The Nelder-Mead method iteratively generates a sequence of interested vertex points which converge to an optimal vertex point of objective function f(x) [10]. At each iteration, the vertices xi are ordered according to the objective function values (6) where x1 is the best vertex and xn+1 is the worst vertex. The algorithm uses four possible operations: reflection, expansion, contraction and shrink, each being associated with a scalar parameter: α (reflection), β (expansion), γ (contraction), and δ (shrink).The values of α, β, γ and δ are lying in the range of >0, >1 and 0 to 1 in both γ and δ respectively.…”

Section: Nelder-mead "Simplex" Direct Search Methodsmentioning

confidence: 99%

Parameter Estimation of DC Motor using Adaptive Transfer Function Based on Nelder-Mead Optimisation

Nayak¹,

Sahu²,

Choudhury³

2018

IJEECS

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<p>This paper explains an adaptive method for estimation of unknown parameters of transfer function model of any system for finding the parameters. The transfer function of the model with unknown model parameters is considered as the adaptive model whose values are adapted with the experimental data. The minimization of error between the experimental data and the output of the adaptive model have been realised by choosing objective function based on different error criterions. Nelder-Mead optimisation Method is used for adaption algorithm. To prove the method robustness and for students learning, the simple system of separately excited dc motor is considered in this paper. The experimental data of speed response and corresponding current response are taken and transfer function parameters of dc motors are adapted based on Nelder-Mead optimisation to match with the experimental data. The effectiveness of estimated parameters with different objective functions are compared and validated with machine specification parameters.</p>

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Section: Nelder-mead "Simplex" Direct Search Methodsmentioning

confidence: 99%

Parameter Estimation of DC Motor using Adaptive Transfer Function Based on Nelder-Mead Optimisation

Nayak¹,

Sahu²,

Choudhury³

2018

IJEECS

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“…It is based on DC motor open-loop control on the speed and position signal by rotary encoder and resolver feedback to the driver, to form a closed loop negative feedback PID control, closed loop current loop, three position loop, speed and drive the internal loop regulation of the motor has a strong follow up to the set value, and then the system can achieve dynamic balance [3][4][5].…”

Section: Object Characteristics Analysismentioning

confidence: 99%

Design of Flying Shear Device and Algorithm Based on Motion Control System

Liu¹,

Li²,

Wang³

et al. 2018

Topics in Chemical &Amp; Material Engineering

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The flying shear device and algorithm design using the SIEMENS S7-300 series PLC SINAMIC SI20 motion controller and the motion control system based on the realization of the servo motor with high precision and high stability control are no traction test data under the condition of Kun and disc cutter by inertia compensation and the control algorithm of PID system, analysis of the causes of the control process and material tension linear velocity error analysis. The system analysis and algorithm design can be used for reference in industrial flying shear and motion control design.

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“…The Proportional-Integral-Derivative (PID) algorithm is one of the most used algorithms in the industry to control processes. It has a simple implementation and there is a vast number of easy-to-use and intuitive tuning methods for such a controller depending on the controlled process with required control quality [20,38,48].…”

Section: Introductionmentioning

confidence: 99%

PLC based fractional-order PID temperature control in pipeline: design procedure and experimental evaluation

2020

Self Cite

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Fractional-order control system design can be used for systems with non-local dynamics involving long-term memory effects. However, implementation of a fractional-order controller in industrial systems is complicated, because of the excessive demand for computational power. The following paper presents the step-by-step design procedure, parameter tuning, and experimental evaluation of the fractional order proportional-integral-derivative (FOPID) controller. The control algorithm is based on the Continued Fraction Expansion approximation of the fractional-order operators. It is implemented on a standard industrial Programmable Logic Controller. The FOPID control system is verified and evaluated in terms of efficiency and robustness using a new laboratory benchmark of a temperature control in the pipeline. The proposed solution shows increased efficiency in terms of robustness compared to the standard PID closed-loop control.

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PID Controller Design Based on Global Optimization Technique with Additional Constraints

Cited by 21 publications

References 0 publications

Parameter Estimation of DC Motor using Adaptive Transfer Function Based on Nelder-Mead Optimisation

Parameter Estimation of DC Motor using Adaptive Transfer Function Based on Nelder-Mead Optimisation

Design of Flying Shear Device and Algorithm Based on Motion Control System

PLC based fractional-order PID temperature control in pipeline: design procedure and experimental evaluation

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