Search Results - (( control optimization method algorithm ) OR ( based optimization path algorithm ))*
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1
A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control
Published 2023“…The development of the Fuzzy Logic Controller requires information on the PID controller parameters that will be fuzzified and defuzzied based on the resulting 49 fuzzy rules. Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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2
A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control
Published 2022“…The development of the Fuzzy Logic Controller requires information on the PID controller parameters that will be fuzzified and defuzzied based on the resulting 49 fuzzy rules. Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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3
A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control
Published 2023“…The development of the Fuzzy Logic Controller requires information on the PID controller parameters that will be fuzzified and defuzzied based on the resulting 49 fuzzy rules. Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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4
A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control
Published 2023“…The development of the Fuzzy Logic Controller requires information on the PID controller parameters that will be fuzzified and defuzzied based on the resulting 49 fuzzy rules. Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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5
A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control
Published 2023“…The development of the Fuzzy Logic Controller requires information on the PID controller parameters that will be fuzzified and defuzzied based on the resulting 49 fuzzy rules. Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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6
A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control
Published 2023“…The development of the Fuzzy Logic Controller requires information on the PID controller parameters that will be fuzzified and defuzzied based on the resulting 49 fuzzy rules. Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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7
A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control
Published 2023“…The development of the Fuzzy Logic Controller requires information on the PID controller parameters that will be fuzzified and defuzzied based on the resulting 49 fuzzy rules. Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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8
A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control
Published 2023“…The development of the Fuzzy Logic Controller requires information on the PID controller parameters that will be fuzzified and defuzzied based on the resulting 49 fuzzy rules. Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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9
A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control
Published 2023“…The development of the Fuzzy Logic Controller requires information on the PID controller parameters that will be fuzzified and defuzzied based on the resulting 49 fuzzy rules. Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). …”
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10
Optimization of mobile robot path planning in semi-dynamic environment using genetic algorithm
Published 2023“…The environment design for mobile robots' path planning based on industry environment shows excellent combinations with the GA method that generate the optimal path for the mobile robot with a semi-dynamic obstacle.…”
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Thesis -
11
Transmission path optimization Based on Efficiency Communication System
Published 2022“…This paper points out the research focus of the wireless sensor network transmission path and discusses the optimization problem of the transmission based on grid model. …”
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Conference or Workshop Item -
12
Improvement and application of particle swarm optimization algorithm
Published 2025“…This method combines CPTD with the Genetic Algorithm and PSO (GAPSO), resulting in an effective strategy for dynamic formation reconfiguration and path optimization. …”
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13
Optimised multi-robot path planning via smooth trajectory generation
Published 2024“…Particle swarm optimization (PSO) outperforms conventional methods like artificial potential fields (APF), the Dijkstra algorithm, and the A* algorithm in path planning for mobile robots. …”
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Final Year Project / Dissertation / Thesis -
14
Modeling and multi-objective optimal sizing of standalone photovoltaic system based on evolutionary algorithms
Published 2020“…A new mutation vector inspired by the two-opposite path (2-Opt) algorithm with adaptive mutation scalar (F ) and crossover rate (CR) control parameters were employed to enhance the exploration and exploitation phases of the proposed algorithm. …”
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15
Three-dimensional coverage control for multi-unmanned aerial vehicle
Published 2024“…This study proposes a 3D coverage control algorithm to address complex real-world scenarios, wherein the 3D control algorithm will be developed based on the 2D MBC scheme. …”
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Final Year Project / Dissertation / Thesis -
16
Controller placement problem in the optimization of 5G based SDN and NFV architecture
Published 2021“…Furthermore, the approach is extended with a Particle Swarm Algorithm (PSO) and presented as a hybrid RASCP to validate the optimal location and number of controllers. …”
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17
A safe overtaking control scheme for autonomous vehicles using rapid-exploration random tree
Published 2022“…The rough path is optimized by applying the cubic spline interpolation method to solve the problem that the path cannot be applied to the actual vehicle driving task. …”
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Proceedings -
18
Design, development and performance optimization of a new artificial intelligent controlled multiple-beam optical scanning module
Published 2006“…This research presents a new approach to optimise the performance of a multiple-beam optical scanning system in terms of its marking combinations and speed, using Genetic Algorithm (GA). The problem has been decomposed into two sub problems; task segregation, where the marking tasks need to be segregated and assigned for each scanner head and path planning where the best combinatorial paths for each scanner are determined in order to minimise the total motion of marking time. …”
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19
Design, development and performance optimization of a new artificial intelligent controlled multiple-beam optical scanning module
Published 2023“…This research presents a new approach to optimise the performance of a multiple-beam optical scanning system in terms of its marking combinations and speed, using Genetic Algorithm (GA). The problem has been decomposed into two sub problems; task segregation, where the marking tasks need to be segregated and assigned for each scanner head and path planning where the best combinatorial paths for each scanner are determined in order to minimise the total motion of marking time. …”
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20
Design, development and performance optimization of a new artificial intelligent controlled multiple-beam optical scanning module
Published 2023“…This research presents a new approach to optimise the performance of a multiple-beam optical scanning system in terms of its marking combinations and speed, using Genetic Algorithm (GA). The problem has been decomposed into two sub problems; task segregation, where the marking tasks need to be segregated and assigned for each scanner head and path planning where the best combinatorial paths for each scanner are determined in order to minimise the total motion of marking time. …”
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