A comparison on genetic algorithm based integer order and fractional order PID control of magnetic bearing system

Altintas, Gokhan; Aydın, Yücel

doi:10.1109/icmech.2017.7921074

Cited by 9 publications

(2 citation statements)

References 11 publications

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“…Several controllers consist of extended concepts from the traditional PID controller have been proposed. These include fractional-order PID [6,7], higher-order PID [8], integerorder PID [9], etc. However, in general, the traditional PID controller is still broadly used because of its low-cost implementation, feasibility, and robustness.…”

Section: Introductionmentioning

confidence: 99%

Analytic Time Domain Specifications PID Controller Design for a Class of 2^nd Order Linear Systems: A Genetic Algorithm Method

Lee

Peng

2021

IEEE Access

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In this paper, an analytical approach of a Proportional-Integral-Derivative (PID) controller design for a servo motor position control with precise desired performance demands is presented. When designing linear controllers, the resulting closed-loop system is often considered as an approximation of a standard 2 nd order system to simplify the gain design process. However, in reality, such an approximation could exist model discrepancies, which lead to performance mismatches between the actual systems behavior and the desired one inevitably. In order to solve this issue, this paper presents an analytical PID controller solution, which is able to achieve desired time domain specification precisely. In addition, a disturbance is added to the system and the associated PID controller will be designed to perform the disturbance rejection. Given a linear 2 nd order linear system together with a PID controller, the related analytical solutions are derived first. Next, the associated fitness function and constraints are then constructed under given prescribed time domain specifications. Lastly, the PID control gains are determined using Genetic Algorithm. To attest the proposed solution, many numerical simulations are performed. A set of control gains is also found using the standard 2 nd order system formulas in order to prove the accuracy of the presented method. Moreover, a comparison study with respect to MATLAB PID Tuner is considered to illustrate the advantage of the proposed PID design scheme. The main contributions of this study include the precision of satisfying given control performance demands with the exact analytical solutions, avoiding the existed model discrepancies between the standard 2 nd order system and the exact closed-loop model. Simulations firmly demonstrate that the proposed method can fulfill users' different control demands as well as get rid of the frequently used trial-and-error strategy.INDEX TERMS PID analytical solution, Genetic Algorithm, PID tuning, servo motor control, time domain specifications.

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Section: Introductionmentioning

confidence: 99%

Analytic Time Domain Specifications PID Controller Design for a Class of 2^nd Order Linear Systems: A Genetic Algorithm Method

Lee

Peng

2021

IEEE Access

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“…Therefore many advanced control tuning methods have been developed in recent years to solve the difficulties arises in FOPID controller design using fractional calculus (Oustaloup, 1991;Samko et al, 1993;Aghababa, 2016;Hamamci, 2007). As evolutionary optimisation algorithms-based design does not depends on the rigorous mathematical model of the plants, recently, many research works have been carried out by using different evolutionary optimisation algorithms (Aghababa, 2016;Hamamci, 2007;Ramezanian et al, 2013;Biswas et al, 2009;Lee et al, 2010;Verma et al, 2017;Ateş and Yeroglu, 2016;Cao et al, 2005;Zamani et al, 2017;Oprzędkiewicz and Dziedzic, 2017;Raju et al, 2016;Haji and Monje, 2017;Altintas and Aydin, 2017;Shata et al, 2016;Chaib et al, 2017;Bouarroudj, 2015). In this regard, to design FOPID controller, either genetic algorithm (Zhang and Li, 2011), fuzzy logic (Moafi et al, 2016), particle swarm optimisation (PSO) (Zamani et al, 2009) or hybrid optimisation (Łapa, 2017) have been used.…”

Section: Introductionmentioning

confidence: 99%

Design of fractional order PID controller for heat flow system using hybrid particle swarm optimisation and gravitational search algorithm

2019

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This paper uses the hybrid particle swarm optimisation (PSO) and gravitational search algorithm (GSA) for the design of fractional order proportional-integrator-derivative (FOPID) controller for a heat flow system. The social behaviour of PSO is combined with the motion technique of GSA. The objective of the algorithm is to obtain the optimal controller parameters for the heat flow system. To obtain the optimal computation, different performance indices such as integral absolute error (IAE), integral squared error (ISE), integral time absolute error (ITAE), and integral time squared error (ITSE) are considered for the optimisation. The performance of the hybrid PSO-GSA is compared with the IMC-PID, fractional order filter-PID, and PSO-FOPID. The proposed method performs comparatively better than the other published methods. Simulink/MATLAB environment is used for simulation purpose.

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Optimization of Fractional and Integer Order PID Parameters using Big Bang Big Crunch and Genetic Algorithms for a MAGLEV System

Altintas

Aydın

2017

IFAC-PapersOnLine

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A comparison on genetic algorithm based integer order and fractional order PID control of magnetic bearing system

Cited by 9 publications

References 11 publications

Analytic Time Domain Specifications PID Controller Design for a Class of 2^nd Order Linear Systems: A Genetic Algorithm Method

Analytic Time Domain Specifications PID Controller Design for a Class of 2^nd Order Linear Systems: A Genetic Algorithm Method

Design of fractional order PID controller for heat flow system using hybrid particle swarm optimisation and gravitational search algorithm

Optimization of Fractional and Integer Order PID Parameters using Big Bang Big Crunch and Genetic Algorithms for a MAGLEV System

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A comparison on genetic algorithm based integer order and fractional order PID control of magnetic bearing system

Cited by 9 publications

References 11 publications

Analytic Time Domain Specifications PID Controller Design for a Class of 2nd Order Linear Systems: A Genetic Algorithm Method

Analytic Time Domain Specifications PID Controller Design for a Class of 2nd Order Linear Systems: A Genetic Algorithm Method

Design of fractional order PID controller for heat flow system using hybrid particle swarm optimisation and gravitational search algorithm

Optimization of Fractional and Integer Order PID Parameters using Big Bang Big Crunch and Genetic Algorithms for a MAGLEV System

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Analytic Time Domain Specifications PID Controller Design for a Class of 2^nd Order Linear Systems: A Genetic Algorithm Method

Analytic Time Domain Specifications PID Controller Design for a Class of 2^nd Order Linear Systems: A Genetic Algorithm Method