Fractional Order PID Control using Ant Colony Optimization

Singh, Richa; Kumar, Ambreesh; Sharma, Rajneesh

doi:10.1109/icpeices.2016.7853387

Cited by 21 publications

(5 citation statements)

References 8 publications

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“…These techniques are categorized as nature-inspired metaheuristic optimization algorithms. A broad array of such algorithms is documented in literature for tuning parameters of PID and PID-based controllers, including but not limited to the grey wolf optimizer [22], JOA [23,24], hybrid cuckoo search and grey wolf optimizer [25], genetic algorithm [26], crow search algorithm [27], arithmetic optimization algorithm [28], interactive teaching-learning optimizer [29], ant colony optimization (ACO) [30], firefly algorithms [31], teaching learning-based optimization [32], sine cosine algorithm [33], gravitation search algorithm [34], pattern search algorithm [35], WOA [36], bee colony optimizer [37], chaotic atom search algorithm [38], and golden jackal optimization [39].…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring solar energy systems: A comparative study of optimization algorithms, MPPTs, and controllers

Güven

2024

IET Control Theory & Appl

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This study elucidates the use of optimization algorithms to identify the controller parameters employed in adjusting the current and voltage values of loads powered by solar energy systems and battery groups. Parameters for these controllers were independently derived using a combination of ant colony optimization with Levy flight, hybrid firefly‐particle swarm optimization, hybrid gravitation search algorithm‐particle swarm optimization, alongside the implementation of Jaya and whale optimization algorithms. The results from each method were juxtaposed for thorough analysis. In addition, three distinct Maximum Power Point Tracker (MPPT) algorithms were employed in the system: perturbation and observation, open circuit voltage, and incremental conductance (IC). To assess the system’s adaptability to real‐world conditions, it was tested against varying temperatures and sunlight levels. Moreover, potential changes in the loads were considered by varying the load. The efficacy of the controllers was examined by altering both the environment and load. The effectiveness of the controllers was examined by referring to the integral of time‐weighted absolute error value. The system was simulated using MATLAB/Simulink software. This study demonstrates that the fractional‐order PID controller achieves the most effective results, the Jaya algorithm provides the best controller parameters, and the IC technique exhibits the highest performance in MPPT.

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Section: Literature Reviewmentioning

confidence: 99%

Exploring solar energy systems: A comparative study of optimization algorithms, MPPTs, and controllers

Güven

2024

IET Control Theory & Appl

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“…Hence, it requires efficient techniques for tuning the parameters of the controllers and reducing the output error and augmenting the performance. With the emergence of swarm intelligence based metaheuristic algorithms, several researchers have utilized various optimization algorithms for tuning the parameters of fuzzy based PID controllers such as particle swarm optimization (PSO) [16], genetic algorithm (GA) [17], and ant colony optimization (ACO) [18] and harmony search algorithm (HSA) [19] for optimizing the DFPID controller.…”

Section: Introductionmentioning

confidence: 99%

Dual fuzzy logic PID controller based regulating of dc motor speed control with optimization using Harmony Search algorithm

Khather,

Ibrahim,

Ibrahim

2023

EEJET

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This paper discusses the implementation of a Proportional-Integral-Derivative (PID) controller for regulating the speed of a closed loop four quadrant chopper fed DC motor. The PID controller is combined with a Dual Fuzzy Logic Controller to form a DFPID controller for enhancing the performance of speed control of the DC motor. The DFLC is optimized using a metaheuristic algorithm known as Harmony Search Algorithm (HSA). The major aim of this research is to gain an effective control over the speed of the motor in the closed loop environment. For achieving this, the parameters for the DFPID are selected through time domain analysis which aims to satisfy the requisites such as settling time and peak overshoot. Initially, the fuzzy logic controller in the DFPID controls the coefficients of the PID achievement gain an effective control over the system error and rate of error change. Further, the DFPID is improved by the HAS for obtaining a precise correction. The solutions obtained by tuning the DFPID controller are evaluated from simulation analysis conducted on a MATLAB/SIMULINK platform. The closed loop performance is analyzed in both time and frequency domain analysis and the performance of DFPID is optimized using the HSA algorithm to obtain precise value of the control process. As observed from the Simulation analysis, the DFPID-HSA generates optimized control signals to the DC motor for controlling the speed. The performance of the intended speed control approach is analyzed in terms of different evaluation metrics such as motor speed, torque and armature current. Experimental outcomes show that the proposed approach achieves better control performance and faster speed of DC motor compared to conventional PID controllers and SMC controllers

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“…The simulation results show that the PSO algorithm outperforms the Nelder-Mead and genetic algorithms in terms of control performance. Singh et al [19] presented an ACO-based parameter optimization strategy for FOPID controllers. The algorithm has been applied to integer and fractional order plants, and the results show high control precision and quick response.…”

Section: Introductionmentioning

confidence: 99%

An examination of the impact of the selected error criterion on the ACO performance in BLDC drives

Dr.Hamza Khalifa Alzarok

2023

jhas

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Brushless direct current (BLDC) motor drives are becoming increasingly popular in motion control applications. As a result, a low-cost but effective BLDC motor speed controller is required. They are used in industrial and home appliances with conventional motor drive technology, such as robots, refrigerators and air conditioning systems. Brushless direct current (BLDC) drives are well known for their high efficiency and low maintenance. This work aims to design a non-conventional PID controller via Ant Colony Optimisers (ACO) in order to obtain an efficient speed performance for a BLDC motor "namely, Maxon EC flat". Moreover, when ACO is used for tuning of PID control, the objective function is required. Therefore, in this work, four different objective functions which are MSE, ISE, IAE, and ITAE are applied and their performance was compared in terms of motor' speed stability. In a simulation environment, the PID controller is used to control the speed of BLDC motors under three testing conditions which are Small, medium, and high inertia loads. The simulation results demonstrate that MSE offers the best performance under low and high inertial loading cases. Whereas, IAE has been superior compared with other criterions when medium loads are applied. Moreover, this paper shows a stability speed- power consumption trade-off problem. In other words, achieving a fast stability via any type of error indices doesn’t guarantee a low amount of consuming energy.

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Fractional Order PID Control using Ant Colony Optimization

Cited by 21 publications

References 8 publications

Exploring solar energy systems: A comparative study of optimization algorithms, MPPTs, and controllers

Exploring solar energy systems: A comparative study of optimization algorithms, MPPTs, and controllers

Dual fuzzy logic PID controller based regulating of dc motor speed control with optimization using Harmony Search algorithm

An examination of the impact of the selected error criterion on the ACO performance in BLDC drives

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