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

Xiao, Liao

doi:10.1155/2021/7287796

Cited by 14 publications

(6 citation statements)

References 11 publications

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“…ese rules are then used to control the system. us, fuzzy control is particularly suitable for simulation experts' control of unknown, complex, and nonlinear systems of mathematical models [13,14]. e fuzzy control system is similar to the common negative feedback closed-loop control system.…”

Section: Fuzzy Control Conceptmentioning

confidence: 99%

Fuzzy Control PID Parameter Tuning and Mathematical Modeling Based on Mobile Virtual Reality Technology

Liu

2022

Mobile Information Systems

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A simple fuzzy rectification model based on mobile virtual reality technology based on PID parameters and mathematical modeling is reduced. In the PID control algorithm, parameter rectification is very important, which directly affects the control performance. The fixed parameters of the traditional method in some systems do not consider all the control indicators. Therefore, fuzzy control adjusts PID parameters and performs several experimental methods. The results show that the mobile virtual reality technology overadjusts the amount and rise time of the control system, when 5% of the response time, etc., are required; the parameters set by the ZN method cannot be taken into account. The PID parameter tuning method is practical in modern control and is an effective advanced method. This method has a certain adaptive ability, and the fuzzy control of PID parameters rectification based on mobile virtual reality technology can achieve a better control effect.

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Section: Fuzzy Control Conceptmentioning

confidence: 99%

Fuzzy Control PID Parameter Tuning and Mathematical Modeling Based on Mobile Virtual Reality Technology

Liu

2022

Mobile Information Systems

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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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“…In [5], the hydraulic servo system uses a genetic algorithm to search for the optimal PID controller gain to achieve accurate control of the displacement of the valve-controlled hydraulic cylinder in the system. Xiao et al [6] proposed an improved genetic algorithm and verified it using eight test functions. The results showed that the probability of convergence to the optimal value was improved, and it was verified on a beer-filling machine system.…”

Section: Introductionmentioning

confidence: 99%

Cuckoo Coupled Improved Grey Wolf Algorithm for PID Parameter Tuning

Chen,

Xiao,

Wang

et al. 2023

Applied Sciences

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In today’s automation control systems, the PID controller, as a core technology, is widely used to maintain the system output near the set value. However, in some complex control environments, such as the application of ball screw-driven rotating motors, traditional PID parameter adjustment methods may not meet the requirements of high precision, high performance, and fast response time of the system, making it difficult to ensure the stability and production efficiency of the mechanical system. Therefore, this paper proposes a cuckoo search optimisation coupled with an improved grey wolf optimisation (CSO_IGWO) algorithm to tune PID controller parameters, aiming at resolving the problems of the traditional grey wolf optimisation (GWO) algorithm, such as slow optimisation speed, weak exploitation ability, and ease of falling into a locally optimal solution. First, the tent chaotic mapping method is used to initialise the population instead of using random initialization to enrich the diversity of individuals in the population. Second, the value of the control parameter is adjusted by the nonlinear decline method to balance the exploration and development capacity of the population. Finally, inspired by the cuckoo search optimisation (CSO) algorithm, the Levy flight strategy is introduced to update the position equation so that grey wolf individuals are enabled to make a big jump to expand the search area and not easily fall into local optimisation. To verify the effectiveness of the algorithm, this study first verifies the superiority of the improved algorithm with eight benchmark test functions. Then, comparing this method with the other two improved grey wolf algorithms, it can be seen that this method increases the average and standard deviation by an order of magnitude and effectively improves the global optimal search ability and convergence speed. Finally, in the experimental section, three parameter tuning methods were compared from four aspects: overshoot, steady-state time, rise time, and steady-state error, using the ball screw motor as the control object. In terms of overall dynamic performance, the method proposed in this article is superior to the other three parameter tuning methods.

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

Cited by 14 publications

References 11 publications

Fuzzy Control PID Parameter Tuning and Mathematical Modeling Based on Mobile Virtual Reality Technology

Fuzzy Control PID Parameter Tuning and Mathematical Modeling Based on Mobile Virtual Reality Technology

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

Cuckoo Coupled Improved Grey Wolf Algorithm for PID Parameter Tuning

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