Self-tuning of fuzzy neural PID parameter based on chaotic ant colony optimization

Zhao, Jimin; Fu, Zhenzhu

doi:10.1063/1.5090762

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…CSA optimization technique could be applied to many controllers to find the best gain values to minimize the error, have optimum system response, and maximize the output power. Tunning of the PI controller [27], PID controller [28,29], and FOPID controller [30] has been introduced in many applications. In this work, CSA would be applied to tune the gains of the PI and FOPID controllers for the proposed DFIG wind energy conversion system.…”

Section: Optimization Techniquementioning

confidence: 99%

Cuckoo search algorithm based for tunning both PI and FOPID controllers for the DFIG-Wind energy conversion system

Al‐Gabalawy

Hosny

Hussien

2020

IJECE

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Wind Energy has received great attention in this century. It influences the new power systems, adding new challenges to the power system expansion problem. Nowadays, double feed induction generator (DFIG) wind turbines are used majorly in wind farms, due to their advantages over other types. Therefore, the analysis of the system using this type has become very important. In this paper, a wind turbine modelling was introduced with suggested controllers, in order to enhance the system response, with respect to both pitch control and maximum output power. Cuckoo search algorithm (CSA), a meta-heuristic optimization technique, was implemented to determine the gains of a proportional-integral (PI) controller and fractional order proportional-integral-derivative (FOPID) controller to optimize the system, which considered three control loops: pitch, rotor-side converter, and grid-side converter control loop. Simulation results were determined using MATLAB/Simulink. The comparative analysis of the results showed that the PI Controller gave the simplest and the best response in case of the pitch and rotor-side control loops while the FOPID was the best when applied to the grid-side control loop. Based on the results and discussion, a suggestion of using a compination of each controller was introduced.

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Section: Optimization Techniquementioning

confidence: 99%

Cuckoo search algorithm based for tunning both PI and FOPID controllers for the DFIG-Wind energy conversion system

Al‐Gabalawy

Hosny

Hussien

2020

IJECE

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“…Ye [ 20 ] improved the searching ability of the particle swarm optimization algorithm by adjusting the adaptive inertia factor, which has been applied to adjust the PID parameters of the temperature control system of injection molding machine online and further improve the control accuracy. Considering the limitations of the conventional PID controller, Zhao and Fu proposed a scheme combining the fuzzy system and BP neural network [ 21 ]. Meanwhile, whale optimization algorithm (WOA) was adopted for further optimization in the improvement of dynamic performance and steady-state accuracy of the control system.…”

Section: Introductionmentioning

confidence: 99%

Parameter Tuning of PID Controller for Beer Filling Machine Liquid Level Control Based on Improved Genetic Algorithm

Xiao

2021

Computational Intelligence and Neuroscience

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Parameter tuning of PID controller for liquid level control of beer filling machine was studied in this paper, which can meet the demand of accurate controlling in beer production and improve the rapidity under the same conditions. Firstly, an improved genetic algorithm was proposed which has been verified by eight kinds of test functions. Simulation results revealed that, in comparation with other modified particle swarm optimization algorithm and modified genetic algorithm, the algorithm proposed in this work is not only capable to improve the convergence speed and precision under the same experimental conditions but also to improve the probability to converge to the optimal value. Finally, the proposed algorithm was applied to the parameter tuning of the PID controller of beer filling machine for liquid level control. Superior property had been obtained, which implied an effective improvement in the rapidity with the premise of steady-state error exclusion.

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Data-driven adaptive tuning method of traditional incremental integral control law

Bian

Chi

2023

2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)

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Self-tuning of fuzzy neural PID parameter based on chaotic ant colony optimization

Cited by 3 publications

References 3 publications

Cuckoo search algorithm based for tunning both PI and FOPID controllers for the DFIG-Wind energy conversion system

Cuckoo search algorithm based for tunning both PI and FOPID controllers for the DFIG-Wind energy conversion system

Parameter Tuning of PID Controller for Beer Filling Machine Liquid Level Control Based on Improved Genetic Algorithm

Data-driven adaptive tuning method of traditional incremental integral control law

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