Genetic algorithm and particle swarm optimization tuned fuzzy PID controller on direct torque control of dual star induction motor

Boukhalfa, Ghoulemallah; Belkacem, Sebti; Chikhi, Abdesselem; Benaggoune, Saïd

doi:10.1007/s11771-019-4142-3

Cited by 55 publications

(27 citation statements)

References 34 publications

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“…Processes 2019, 7, x FOR PEER REVIEW 3 of 21 (5) For an integrated consideration of control performance and control energy cost, the multiobjective fitness function is adopted in PSO.…”

Section: Methodsmentioning

confidence: 99%

“…Processes 2019, 7, x FOR PEER REVIEW 9 of 21 5. When i reaches n, one round of optimization on multiple controller parameters for the singleoutput thermal process is done.…”

Section: Verificationmentioning

confidence: 99%

“…Processes 2019, 7, 784 3 of 21 (4) Due to efficient optimization ability on multi-dimension real-number problems, the canonical PSO algorithm is adopted for PID parameter optimizing in the approach. (5) For an integrated consideration of control performance and control energy cost, the multi-objective fitness function is adopted in PSO.…”

Section: Introductionmentioning

confidence: 99%

“…GA is adopted for tuning PID parameters in an application of electrical furnace temperature control and the Integral of Absolute index is used for assessment [4]. In [5], the PID-PSO, Fuzzy-PSO, and GA-PSO are taken respectively for improving the speed control of dual star induction motor and the Fuzzy-PSO behaves the best. Genetic Programming is studied for the PID parameter tuning in [6].…”

mentioning

confidence: 99%

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A PSO-Based Recurrent Closed-Loop Optimization Method for Multiple Controller Single-Output Thermal Engineering Systems

Liu

Pan

2019

Processes

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For solving the problems of closed-loop optimization on controller parameters of multiple-controller single-output thermal engineering system, this paper proposes a recurrent optimization method that is based on the particle swarm computing and closed-loop simulation (PSO-RCO). It consists of a set of closed-loop identification, simulation, and optimization functions that are organized in a recurrent working flow. The working flow makes one controller tuned at a time whilst others keep their values. It ends after several rounds of overall optimizations. Such a recurrently alternative tuning can greatly speed up the convergence of controller parameters to reasonable values. Verifications on practical data from a superheated steam temperature control system show that the optimized control system performance is greatly improved by reasonable controller parameters and practicable control action. With the advantage of not interfering system operation and the potential supporting on big data identification method, the PSO-RCO is a promising method for control system optimization.

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“…Processes 2019, 7, x FOR PEER REVIEW 3 of 21 (5) For an integrated consideration of control performance and control energy cost, the multiobjective fitness function is adopted in PSO.…”

Section: Methodsmentioning

confidence: 99%

“…Processes 2019, 7, x FOR PEER REVIEW 9 of 21 5. When i reaches n, one round of optimization on multiple controller parameters for the singleoutput thermal process is done.…”

Section: Verificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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A PSO-Based Recurrent Closed-Loop Optimization Method for Multiple Controller Single-Output Thermal Engineering Systems

Liu

Pan

2019

Processes

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“…The hybrid techniques can work in series, parallel, or with impeded calibration. [18][19][20][21][22][23][24][25][26][27] The particle distribution models can be classified into three major categories: one complex (OC-PSO), shuffled complex evolution (SCE-PSO), and random shuffled complex evolution (SCER-PSO). 28,29 The SCE-PSO modifications find a local optimum point instead of the global optimum point, while SCER-PSO divides the particles into sub-swarms according to a random replacement scheme.…”

Section: Introductionmentioning

confidence: 99%

Garra Rufa‐inspired optimization technique

Jaber¹,

Abdulbari

Shalash³

et al. 2020

Int J Intell Syst

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Natural selection has inspired researchers to develop and apply several intelligent optimization techniques in the past few decades. Generally, in artificial intelligence optimization, the particles follow a local or global best particle until finding an acceptable solution. In well‐ developed optimization techniques, such as swarm optimization (PSO) and the firefly algorithm (FA), getting around the initial optimal value of the group and randomly checking the effect of the surrounding points may lead to a better solution than the initial optimal value. The present work was inspired by the fascinating movement of Garra Rufa fish between two immersed legs during a regular “fish massage session.” A new optimization approach is proposed and modeled based on the movements of Garra Rufa fish, in which the particles are separated into groups, and the best optimal value leads each group for the group. Also, some of these particles are allowed to change groups depending on the fitness of the leaders of the groups. The suggested strategy is then compared with PSO and FA using multiple test optimization functions, such as the Ackley, Hartmann, Michalewicz, Shubert, Easom, Bohachevsky, and Rastrigin functions. Also, a multiobjective real issue in power system is tested using the proposed methods where the objectives were cumulative voltage deviation and power losses of three weight sets during the selection allocation of distribution generators. The results show that the proposed method provides good data and greater convergence to the optimal point compared with the classical methods for most of the functions tested.

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One universal method of complex system reliability, maintainability, supportability, testability quotas design and trade‐off based on improved flower pollination algorithm

Zhu¹,

Han²,

Liu³

et al. 2020

Quality & Reliability Eng

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For modern complex system, quality has become a serious and difficult problem that restricts the generation of combat effectiveness. Withal, based on domestic and foreign research and analysis, this paper puts forward a trade‐off method for complex system reliability, maintainability, supportability, testability (RMST) design, which is suitable for product design stage. First, aiming at the uncertain problem of complex system constrained optimization, the good‐point set population initialization, Deb feasibility comparison method and ε constraint method are integrated into the flower pollination algorithm, to construct the algorithm for RMST quotas design and trade‐off. Second, taking the typical series‐parallel repairable system as an example, one general model for complex system RMST quotas design and trade‐off based on effectiveness is constructed. Finally, take an unmanned nuclear radiation detection system as a typical case, to verify its feasibility, advantages, and disadvantages.

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Genetic algorithm and particle swarm optimization tuned fuzzy PID controller on direct torque control of dual star induction motor

Cited by 55 publications

References 34 publications

A PSO-Based Recurrent Closed-Loop Optimization Method for Multiple Controller Single-Output Thermal Engineering Systems

A PSO-Based Recurrent Closed-Loop Optimization Method for Multiple Controller Single-Output Thermal Engineering Systems

Garra Rufa‐inspired optimization technique

One universal method of complex system reliability, maintainability, supportability, testability quotas design and trade‐off based on improved flower pollination algorithm

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