Particle swarm optimization based PID controller tuning for level control of two tank system

Vincent, Anju K; Nersisson, Ruban

doi:10.1088/1757-899x/263/5/052001

Cited by 15 publications

(4 citation statements)

References 4 publications

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“…The basic principle of calculating PSO is by having a large population which is called a swarm and every single population is called a particle. Each particle represents a potential solution to the problem [24], [25]. These particles will move in the search space according to some basic formulas.…”

Section: Low Control Level Based On Pi Controllersmentioning

confidence: 99%

Energy management for hybrid electric vehicles using rule based strategy and PI control tuned by particle swarming optimization algorithm

Al-Flehawee

Al-Mayyahi

2022

IJECE

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<span lang="EN-US">Recently, hybrid electric vehicles are increasingly being used to replace conventional vehicles. In this paper, a control methodology is designed that can reduce fuel consumption and improve the vehicle’s dynamic response. As the control unit based on this methodology consists of two levels, the first depends on the application of a rule-based strategy for energy management between the main components of the vehicle, and this strategy is based on a set of rules that are activated according to parameters such as vehicle speed and the battery <a name="_Hlk108852620"></a>state of charge (SOC) that control the activation/deactivation of the internal combustion engine (ICE), motor, and generator. This level also makes ICE operate at operation points with high efficiency, which is represented by the optimal operating line (OOL). The second level is called the low control level, and it consists of two proportional-integral (PI) controllers used to control the speed of each ICE and the motor to obtain the appropriate torque for both of them to drive the vehicle properly. The particle swarming optimization (PSO) algorithm is utilized to tune the parameters of the PI controllers. The obtained results have effectively minimized fuel consumption and improved the performance of the vehicle.</span>

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Section: Low Control Level Based On Pi Controllersmentioning

confidence: 99%

Energy management for hybrid electric vehicles using rule based strategy and PI control tuned by particle swarming optimization algorithm

Al-Flehawee

Al-Mayyahi

2022

IJECE

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“…As mentioned in [18,30], the steps of the searching procedure for the PSO technique are listed as follows:…”

Section: Optimization Proceduresmentioning

confidence: 99%

Hybrid Control using Adaptive Particle Swarm Optimization and Integral Backstepping Control of Grid-connected Doubly Fed Induction Generator

et al. 2021

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We proposed an analysis of a hybrid control of active and reactive power for a doubly-fed induction generator for variable velocity wind energy injection into the electrical grid using a combination of adaptive particle swarm optimization and integral backstepping control in this paper. The stability of the Lyapunov function is utilized to establish the latter. Six controllers are developed as part of the proposed control process: The first is concerned with the maximum PowerPoint. The stator powers are managed by the second and third regulators, which are performed by the optimal PI controller using adaptive particle swarm optimization. The DC link voltage is kept constant by the fourth controller. The fifth and sixth are employed to pilot the rotor powers and ensure that the power factor is maintained to 1. These three controllers are synthesized by using the nonlinear integral backstepping control. These control strategies show excellent results compared to field-oriented control under a variable wind speed profile and changing generator settings in a Matlab/Simulink environment. According to the test findings, using integral backstepping, the overshoot of the DC-link voltage is decreased by 99.16 %. Furthermore, the particle swarm optimization reduces its time to reach the equilibrium state to 4.3 m s and demonstrates robustness against parameter generator changes. HIGHLIGHTS The regulation of the produced power by the wind energy conversion system (WECS) based on a doubly-fed induction generator is becoming increasingly important to researchers. This system is modeled and simulated in the Matlab/Simulink software environment to apply the proposed control In order to extract the maximum power from the variable wind source, a maximum power point tracking method is developed based on the PI controller For piloting the wind energy system conversion (WECS) based on a DFIG, a combination of the integrated Backstepping controller and adaptive PSO is proposed and realized in this paper Robustness tests are established by adjusting the generator parameters, and a comparative study is conducted to verify the superiority of the suggested control over the indirect vector control GRAPHICAL ABSTRACT

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“…To address this issue, researchers have been actively exploring the potential of metaheuristic algorithms, which offer robust and adaptive optimization capabilities in tackling complex control problems [11]- [14]. Among these algorithms, a promising contender is the particle swarm optimization [15], modified grey wolf optimization [16], cuckoo search algorithm [17], [18], and improved genetic algorithm [19], along with non-dominated sorting genetic algorithm and multiobjective particle swarm optimization [20]. The primary objective in the context of this study is to design and implement a PID controlled three-tank liquid level system using the prairie dog optimization (PDO) [21] as a novel metaheuristic algorithm in this specific field of research.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Three-Tank Liquid Level Control: Insights from Prairie Dog Optimization

Izci,

Ekinci

2023

IJRCS

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The management of chemical process liquid levels poses a significant challenge in industrial process control, affecting the efficiency and stability of various sectors such as food processing, nuclear power generation, and pharmaceutical industries. While Proportional-Integral-Derivative (PID) control is a widely-used technique for maintaining liquid levels in tanks, its efficacy in optimizing complex and nonlinear systems has limitations. To overcome this, researchers are exploring the potential of metaheuristic algorithms, which offer robust optimization capabilities. This study introduces a novel approach to liquid level control using the Prairie Dog Optimization (PDO) algorithm, a metaheuristic algorithm inspired by prairie dog behavior. The primary objective is to design and implement a PID-controlled three-tank liquid level system that leverages PDO to regulate liquid levels effectively, ensuring enhanced stability and performance. The performance of the proposed system is evaluated using the ZLG criterion, a time domain metric-based objective function that quantifies the system's efficiency in maintaining desired liquid levels. Several analysis techniques are employed to understand the behavior of the system. Convergence curve analysis assesses the PDO-controlled system's convergence characteristics, providing insights into its efficiency and stability. Statistical analysis determines the algorithm's reliability and robustness across multiple runs. Stability analysis from both time and frequency response perspectives further validates the system's performance. A comprehensive comparison study with state-of-the-art metaheuristic algorithms, including AOA-HHO, CMA-ES, PSO, and ALC-PSODE, is conducted to benchmark the performance of PDO. The results highlight PDO's superior convergence, stability, and optimization capabilities, establishing its efficacy in real-world industrial applications. The research findings underscore the potential of PDO in PID control applications for three-tank liquid level systems. By outperforming benchmark algorithms, PDO demonstrates its value in industrial control scenarios, contributing to the advancement of metaheuristic-based control techniques and process optimization. This study opens avenues for engineers and practitioners to harness advanced control solutions, thereby enhancing industrial processes and automation.

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Particle swarm optimization based PID controller tuning for level control of two tank system

Cited by 15 publications

References 4 publications

Energy management for hybrid electric vehicles using rule based strategy and PI control tuned by particle swarming optimization algorithm

Energy management for hybrid electric vehicles using rule based strategy and PI control tuned by particle swarming optimization algorithm

Hybrid Control using Adaptive Particle Swarm Optimization and Integral Backstepping Control of Grid-connected Doubly Fed Induction Generator

Optimizing Three-Tank Liquid Level Control: Insights from Prairie Dog Optimization

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