Real-Time Implementation of Tuning PID Controller Based on Whale Optimization Algorithm for Micro-robotics System

Ghith, Ehab Seif; Tolba, Farid Abdel Aziz

doi:10.1109/iccae55086.2022.9762448

Cited by 10 publications

(6 citation statements)

References 25 publications

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“…As with any optimization algorithm, parameter tuning and adaptation to specific problem domains are crucial for achieving optimal results. At the outset, the PID values were initially tuned using the empirical method outlined in the [36]. However, in pursuit of optimized performance, four optimization algorithms (BAS, PSO, POA, EO) were employed.…”

Section: Pelican Optimization Algorithm (Poa)mentioning

confidence: 99%

“…Physical System with DC Motor Parameters and Inverse Kinematic & Dynamic Model. The DC motor used in the system has the following values: Armature Resistance (Ra: 1.32 Ohm), Armature Inductance (La: 1.59 mH), Rotational Inertia (Jm: 0.0023 Kg.m²), Back-EMF Constant (Ke: 0.082 V sec/rad), Motor Torque Constant (Kt: 0.6 N m/A), and Viscous Friction (Bm: 0.01 N/rad/sec)[36]. In the inverse kinematic and dynamic model, the angular velocity of the left wheel is denoted as F1 = ω L , while the angular velocity of the right wheel is marked as F2 = ω R .…”

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confidence: 99%

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Design and Development of FOODIEBOT Robot: From Simulation to Design

Moshayedi,

Roy,

Liao

et al. 2024

IEEE Access

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This investigation is centrally focused on the comprehensive evolution and enhancement of FOODIEBOT, an adaptive service automaton with a wide range of functionalities. Its capabilities encompass sophisticated image processing methods, seamlessly integrated via mobile applications (APP) and web interfaces, tailored specifically for intricate object manipulation in dining hall settings.During its developmental phase, the precise calibration of PID controller coefficients emerged as an essential requirement. The model underwent meticulous scrutiny through detailed simulations using MATLAB software. Following this phase, its operational efficiency navigating through circular, elliptical, spiral, and octagonal trajectories underwent rigorous examination, utilizing optimization methodologies like BAS, PSO, POA, and EO. The exposition emphasizes the diverse dispersion of optimized coefficients within each algorithmic framework. The pinnacle of this effort involved a comprehensive evaluation of pathway performance, amalgamating insights from each optimization paradigm. The discussion extensively delineates both simulated and realtime performance metrics of the robot, validating the accuracy and reliability of simulation in deriving PID controller values.In the comprehensive evaluation of methodologies and the robotic system's effectiveness, the BAS technique excels in operational efficiency. This method consistently outperforms its counterparts in execution time, primarily due to its meticulous optimization of particle count. The comparative analysis across various trajectories reveals intriguing insights. The EO approach showcases outstanding accuracy in Path 1, while the POA method achieves optimal precision in Path 3. Impressively, the BAS technique demonstrates unparalleled accuracy in Path 4. Furthermore, in terms of solution optimization, the BAS method consistently displays the shortest execution times across all traversed pathways. When examining maximum velocity along these routes, the PSO method excels in Paths 1, 3, and 4, consistently achieving the highest speeds. Notably, Path 2 uniquely displays the peak velocity attained by the POA method.This article presents comprehensive insights into the constituent elements of the robotic system's design. The inquiry delves into the intricate nuances of optimization methodologies, elucidating their profound impact on the service automaton's performance across diverse orientations. The pragmatic implications underscore the critical role of temporal considerations in the judicious selection of these methodologies. The observed congruence between simulated and practical performance serves as a definitive validation, affirming the precision of simulation computations and the subsequent derivation of PID controller values.

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Section: Pelican Optimization Algorithm (Poa)mentioning

confidence: 99%

mentioning

confidence: 99%

Design and Development of FOODIEBOT Robot: From Simulation to Design

Moshayedi,

Roy,

Liao

et al. 2024

IEEE Access

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“…Consequently, several enhancement schemes, including hybridization [15], [16], [17], the introduction of new learning mechanisms [18], [19], [20] and others, have been developed to improve the balance in AOA variants. Since its introduction, both the original AOA and its enhanced variants have been successfully applied to a wide array of engineering optimization problems [15], [16], [19], [21], [22], [23], [24], [25].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Global Optimization Performance of Arithmetic Optimization Algorithm with a Modified Population Initialization Scheme

Ting,

Rahman,

Chiong

et al. 2024

Proceedings of International Conference on Artificial Life and Robotics

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Arithmetic Optimization Algorithm (AOA) is widely used to solve global optimization problems. However, it often faces premature convergence challenges in complex optimization scenarios. A key factor affecting AOA's performance is the solution quality of the initial population. The conventional initialization scheme, despite its prevalence, lacks reliability in ensuring high-quality solutions due to inherent stochastic processes. To address this issue, we propose a modified initialization scheme that improves initial population quality by integrating chaotic maps and oppositional-based learning. Through extensive simulation studies, we demonstrate that the enhanced AOA, equipped with this new initialization scheme, exhibits superior performance in solving a range of benchmark functions with improved accuracy.

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“…The same experiment was simulated by Ramy et al [3], but by using a solid coil and 8 µm settling error. Ehab et al [4]- [10] simulated the same experiment, but with a 4 µm settling VOLUME XX, 2017 1 error. It is ob-served that the error rate can be reduced up to 50% by using the SSA approach, as com-pared to the previous works.…”

Section: Introductionmentioning

confidence: 99%

Tuning PID Controllers Based on Hybrid Arithmetic Optimization Algorithm and Artificial Gorilla Troop Optimization for Micro-Robotics Systems

Ghith

Tolba

2023

IEEE Access

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Micro particles have the potentials to be used for many medical purposes in-side the human body such as drug delivery and other operations. This paper attempts to provide a thorough comparison between five meta-heuristic search algorithms: Arithmetic optimization algorithm (AOA), Artificial Gorilla troop's optimization (GTO), Seagull optimization algorithm (SOA), Parasitism-predation Algorithm (PPA), and hybrid between AOA and GTO (HAOAGTO). These approaches were used to calculate the PID controller optimal indicators with the application of different functions, including Integral Absolute Error (IAE), Integral of Time Multiplied by Square Error (ITSE), Integral Square Time multiplied square Error (ISTES), Integral Square Error (ISE), Integral of Square Time multiplied by square Error ( (ISTSE), and Integral of Time multiplied by Absolute Error (ITAE). Every method of controlling was presented in a MATLAB Simulink numerical model. It is observed that the PPA technique achieves the highest values of best fitness value for simulation results among other control approaches, while the HAOAGTO approach reduces the best fitness function compared to other optimization techniques used. The results indicate that HAOAGTO is the best method among all approaches and that ISTES is the best choice of PID for optimizing the controlling parameters.

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Real-Time Implementation of Tuning PID Controller Based on Whale Optimization Algorithm for Micro-robotics System

Cited by 10 publications

References 25 publications

Design and Development of FOODIEBOT Robot: From Simulation to Design

Design and Development of FOODIEBOT Robot: From Simulation to Design

Enhancing Global Optimization Performance of Arithmetic Optimization Algorithm with a Modified Population Initialization Scheme

Tuning PID Controllers Based on Hybrid Arithmetic Optimization Algorithm and Artificial Gorilla Troop Optimization for Micro-Robotics Systems

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