A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control

Razali, Muhammad Razmi; Faudzi, Ahmad Athif Mohd; Shamsudin, Abu Ubaidah; Mohamaddan, Shahrol

doi:10.3389/frobt.2022.1087371

Cited by 7 publications

(4 citation statements)

References 31 publications

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“…ANN are computational models that attempt to make mathematical representations of the structure of the brain, consisting of a set of elements to perform simple manipulations that communicate through weighted connections [3].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Robot Arm Control Using Hybrid intelligent

Elmenfy,

Mosa,

Abdelsalam

2023

International Journal of Applied Sciences &Amp; Development

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The design of a fuzzy logic control suffers from select parameters of the membership functions, scaling factors, defuzzification action, Inference engine and base rules. speaking generally, such prosecutions are executed by traditionally techniques which do not assure an robust fuzzy control system design. There are various techniques introduced in literatures that used Genetic Algorithms to optimize a fuzzy logic control component. . In this paper, the suggested control law consists of Fuzzy Logic Control (FLC) tuning via Geneticn Algorithm (GA). The FLC used because it is efficient tools for control of nonlinear and uncertain parameters systems. GA is mainly presented to find a simultaneous near optimum design of the membership functions, scaling factors, defuzzification Method, Inference engine and control base rules. GA with different fitness functions in a form of the cumulative response error which are widely used as an efficient optimization technique. This paper also introduce a new methodology with new multi-objective function to improve fuzzy control parameters based on Genetic Algorithm techniques. The dynamic model of the robot manipulator its done by differential equations, these equations are hardly nonlinear, parameters uncertainty and time varying with multiple input and multiple output (MIMO).The manipulator robot and the fuzzy genetic control are modeled in MATLAB SIMULINK; the manipulator robot model driven nonlinear controller to draw a circle in the space with and without parameters uncertainties. The proposed techniques showed that the proposed fuzzy controller gives superior response in the output performance. When the parameter uncertainties including in the system, given satisfactory response.

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Robot Arm Control Using Hybrid intelligent

Elmenfy,

Mosa,

Abdelsalam

2023

International Journal of Applied Sciences &Amp; Development

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“…Genetic algorithm (GA) is a search method that mimics the natural evolutionary process by simulating the Darwinian evolutionary theory of "natural selection" and the biological evolutionary process of genetics [22]. In order to ensure the ideal search effect for the path distance minimization task, the article combines the idea of simulated annealing and constructs a genetic-annealing algorithm (GAA), the main components of which are described below.…”

Section: Planning Algorithmsmentioning

confidence: 99%

Time-Dependent Vehicle Speed Variation Based Global Path Planning for Intelligent Connected Vehicles

Chen¹,

Wang²,

Liu³

et al. 2023

IJAMM

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Article Time-Dependent Vehicle Speed Variation Based Global Path Planning for Intelligent Connected Vehicles Sihao Chen 1,2, Zhenfeng Wang 1,2, Zhengbai Liu 3, Xianyi Yang 1,2, and Heng Wang 1,2,* 1 College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou 450002, China 2 Henan Provincial Cold Chain Information and Equipment Laboratory for Logistics of Agricultural Products, Zhengzhou 450002, China 3 College of Innovation and Entrepreneurship, Southern University of Science and Technology, Shenzhen 518055, China * Correspondence: dawn.wangh@henau.edu.cn Received: 8 May 2023 Accepted: 29 May 2023 Published: 21 June 2023 Abstract: When an intelligent connected vehicle (ICV) autonomously completes an intelligent driving assignment, the decision planning layer needs to plan an optimal path from the starting location to the target location for the vehicle, which is referred to as global path planning (GPP) for the ICV. For the GPP of ICVs undertaking long-distance and multi-location driving assignments, a fixed open travelling salesman problem (TSP) was constructed in conjunction with travel time analysis. To better address this issue, a genetic annealing algorithm (GAA) was proposed, and corresponding simulations were conducted using genetic algorithm, ant colony algorithm, and GAA respectively. Based on the optimization processes and results, the GAA outperformed the traditional genetic algorithm and ant colony algorithm in tackling this issue. Therefore, the method proposed in this paper can be applied to the global path planning platform for intelligent networked vehicles.

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“…Optimization is finding a suitable set of variable values to minimize (maximize) the value of some optimization objective under certain constraints. Optimization algorithms are widely used in engineering design 1 – 3 , engineering practice 4 – 6 , motion control 7 – 9 , and task scheduling in the real world 10 – 12 . At this stage, algorithms for solving optimization problems contain two main categories 13 .…”

Section: Introductionmentioning

confidence: 99%

Symmetric projection optimizer: concise and efficient solving engineering problems using the fundamental wave of the Fourier series

Su,

Dong,

Liu

et al. 2024

Sci Rep

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The fitness function value is a kind of important information in the search process, which can be more targeted according to the guidance of the fitness function value. Most existing meta-heuristic algorithms only use the fitness function value as an indicator to compare the current variables as good or bad but do not use the fitness function value in the search process. To address this problem, the mathematical idea of the fitting is introduced into the meta-heuristic algorithm, and a symmetric projection optimizer (SPO) is proposed to solve numerical optimization and engineering problems more efficiently. The SPO algorithm mainly utilizes a new search mechanism, the symmetric projection search (SP) method. The SP method quickly completes the fitting of the projection plane, which is located through the symmetry of the two points and finds the minima in the projection plane according to the fitting result. Fitting by using the fitness function values allows the SP to find regions where extreme values may exist more quickly. Based on the SP method, exploration and exploitation strategies are constructed, respectively. The exploration strategy is used to find better regions, and the exploitation strategy is used to optimize the discovered regions continuously. The timing of the use of the two strategies is designed so that the SPO algorithm can converge faster while avoiding falling into local optima. The effectiveness of the SPO algorithm is extensively evaluated using seven test suites, including CEC2017, CEC2019, CEC2020, and CEC2022. It is also compared with two sets of 19 recent competitive algorithms. Statistical analyses are performed using five metrics such as the Wilcoxon test, the Friedman test, and variance. Finally, the practicality of the SPO algorithm is verified by four typical engineering problems and a real spacecraft trajectory optimization problem. The results show that the SPO algorithm can find superior results in 94.6% of the comparison tests and is a promising alternative for solving real-world problems.

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A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control

Cited by 7 publications

References 31 publications

Robot Arm Control Using Hybrid intelligent

Robot Arm Control Using Hybrid intelligent

Time-Dependent Vehicle Speed Variation Based Global Path Planning for Intelligent Connected Vehicles

Symmetric projection optimizer: concise and efficient solving engineering problems using the fundamental wave of the Fourier series

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