Metaheuristic algorithms for PID controller parameters tuning: review, approaches and open problems

Heliyon, Journal Year: 2022, Volume and Issue: 8(5), P. e09399 - e09399

Published: May 1, 2022

The simplicity, transparency, reliability, high efficiency and robust nature of PID controllers are some the reasons for their popularity acceptance control in process industries around world today. Tuning parameters has been a field active research still is. primary objectives to achieve minimal overshoot steady state response lesser settling time. With exception two popular conventional tuning strategies (Ziegler Nichols closed loop oscillation Cohen-Coon's reaction curve) several other methods have employed tuning. This work accords thorough review state-of-the-art classical controller using metaheuristic algorithms. Methods appraised categorized into optimization purposes. Details algorithms, application, equations implementation flowcharts/algorithms presented. Some open problems future also major goal this is proffer comprehensive reference source researchers scholars working on controllers.

Language: Английский

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Optimization Algorithms for Wireless Sensor Networks Node Localization: An Overview

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 50459 - 50488

Published: Jan. 1, 2024

Wireless Sensor Networks (WSNs) play a critical role in numerous applications, and the accurate localization of sensor nodes is vital for their effective operation. In recent years, optimization algorithms have garnered significant attention as means to enhance WSN node localization. This paper presents an in-depth exploration necessity offers comprehensive review used this purpose. The encompasses diverse range techniques, including evolutionary algorithms, swarm intelligence, metaheuristic approaches. Key factors, such accuracy, scalability, computational complexity, robustness, are systematically evaluated compared across various algorithms. Additionally, sheds light on strengths limitations each approach discusses applicability different deployment scenarios. insights provided serve valuable resource researchers practitioners seeking optimize localization, thus promoting efficient reliable operation WSNs real-world applications.

Language: Английский

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Robust Model Predictive Control Paradigm for Automatic Voltage Regulators against Uncertainty Based on Optimization Algorithms

Mathematics, Journal Year: 2021, Volume and Issue: 9(22), P. 2885 - 2885

Published: Nov. 12, 2021

This paper introduces a robust model predictive controller (MPC) to operate an automatic voltage regulator (AVR). The design strategy tends handle the uncertainty issue of AVR parameters. Frequency domain conditions are derived from Hermite–Biehler theorem maintain stability perturbed system. tuning MPC parameters is performed based on new evolutionary algorithm named arithmetic optimization (AOA), while expert designers use trial and error methods achieve this target. constraints handled during process. An effective time-domain objective formulated guarantee good performance for by minimizing maximum overshoot response settling time simultaneously. results suggested AOA-based compared with various techniques in literature. system demonstrates effectiveness robustness proposed low control effort against variations parameters’ other techniques.

Language: Английский

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A survey on population-based meta-heuristic algorithms for motion planning of aircraft

Swarm and Evolutionary Computation, Journal Year: 2021, Volume and Issue: 62, P. 100844 - 100844

Published: Feb. 3, 2021

Language: Английский

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Nature inspired optimization algorithms: a comprehensive overview

Evolving Systems, Journal Year: 2022, Volume and Issue: 14(1), P. 141 - 156

Published: March 19, 2022

Language: Английский

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An Arithmetic-Trigonometric Optimization Algorithm with Application for Control of Real-Time Pressure Process Plant

Sensors, Journal Year: 2022, Volume and Issue: 22(2), P. 617 - 617

Published: Jan. 13, 2022

This paper proposes a novel hybrid arithmetic–trigonometric optimization algorithm (ATOA) using different trigonometric functions for complex and continuously evolving real-time problems. The proposed adopts functions, namely sin, cos, tan, with the conventional sine cosine (SCA) arithmetic (AOA) to improve convergence rate optimal search area in exploration exploitation phases. is simulated 33 distinct test problems consisting of multiple dimensions showcase effectiveness ATOA. Furthermore, variants ATOA technique are used obtain controller parameters pressure process plant investigate its performance. obtained results have shown remarkable performance improvement compared existing algorithms.

Language: Английский

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Dynamic optimization of a wastewater treatment process for sustainable operation using multi-objective genetic algorithm and non-dominated sorting cuckoo search algorithm

Journal of Water Process Engineering, Journal Year: 2023, Volume and Issue: 53, P. 103775 - 103775

Published: May 13, 2023

Language: Английский

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Advancements in battery thermal management for electric vehicles: Types, technologies, and control strategies including deep learning methods

Ain Shams Engineering Journal, Journal Year: 2024, Volume and Issue: 15(9), P. 102908 - 102908

Published: June 21, 2024

As electric vehicles (EVs) become more commonplace, the development and deployment of advanced battery thermal management (BTM) technologies are vital for increasing sturdiness EV batteries, ultimately contributing to sustainable massive adoption mobility. This study comprehensively evaluates new advancements in BTM systems EVs, supplemented with a comparative evaluation various technologies, including active passive cooling strategies, structure design, control algorithms, deep learning methods. The also scrutinizes software's capabilities employed designing systems. cross-relevant papers related from 2019 early 2024, which rely on Scopus Web Science databases, considered. explores strengths obstacles different processes, shedding light their efficacy under varying operational conditions. Additionally, this discusses impact overall efficiency perspective considerations. Insights into current research trends, innovations, emerging trends field presented. Ultimately, state-of-the-art aims thoroughly understand latest EVs. findings offer insightful information scientists, engineers, professionals pursuing transportation continuous enhancement technology.

Language: Английский

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Applications of Machine Learning in Real-Time Control Systems: A Review

Measurement Science and Technology, Journal Year: 2024, Volume and Issue: 36(1), P. 012003 - 012003

Published: Oct. 21, 2024

Abstract Real-time control systems (RTCSs) have become an indispensable part of modern industry, finding widespread applications in fields such as robotics, intelligent manufacturing and transportation. However, these face significant challenges, including complex nonlinear dynamics, uncertainties various constraints. These challenges result weakened disturbance rejection reduced adaptability, which make it difficult to meet increasingly stringent performance requirements. In fact, RTCSs generate a large amount data, presents important opportunity enhance effectiveness. Machine learning, with its efficiency extracting valuable information from big holds potential for RTCSs. Exploring the machine learning is great importance guiding scientific research industrial production. This paper first analyzes currently faced by RTCSs, elucidating motivation integrating into systems. Subsequently, discusses aspects, system identification, controller design optimization, fault diagnosis tolerance, perception. The indicates that data-driven methods exhibit advantages addressing multivariable coupling characteristics systems, well arising environmental disturbances faults, thereby effectively enhancing system’s flexibility robustness. compared traditional methods, also faces issues poor model interpretability, high computational requirements leading insufficient real-time performance, strong dependency on high-quality data. proposes future directions.

Language: Английский

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Performance Improvement of Three-Phase Boost Power Factor Correction Rectifier Through Combined Parameters Optimization of Proportional-Integral and Repetitive Controller

IEEE Access, Journal Year: 2021, Volume and Issue: 9, P. 58893 - 58909

Published: Jan. 1, 2021

This paper performs parameter optimization of proportional-integral (PI) and repetitive controller (RC) with a new objective function by adding two degrees freedom for three-phase boost power factor correction (PFC) rectifier. The main objectives are to optimize the multiple control loop parameters total harmonics distortion (THD) reduction dynamic performance indices improvement, including overshoot, rise time, zero steady-state error. PFC rectifier optimized through standard genetic algorithm. After obtaining optimal PI RC values, fast Fourier transform response analysis were performed using MATLAB. Moreover, separate evaluation functions used validate results in terms THD improvement. Furthermore, compared existing show proposed superiority. Simulation demonstrated that our outperforms achieve value. Finally, simulation validated experimental results. setup includes 5kW DSP TMS320F28335 prototype hardware verify performance.

Language: Английский

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