New social-based radius particle swarm optimization

Munlin, Mud-Armeen; Anantathanavit, Mana

doi:10.1109/iciea.2017.8282956

Cited by 8 publications

(8 citation statements)

References 12 publications

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“…The mathematical model for the dynamic part of mobile robot can be described in state space. Where the state variables for the robot is defined as X= [ , v, θ], the worked adaptable inputs variables such as u= [ur, ul ] The following equations (16) and (17) represent the relation between the input torques to the robot (ur and ul) and the output of the controller( uv and uφ):…”

Section: Dynamics Of Mobile Robotmentioning

confidence: 99%

“…The dispassionate purposes reconnoitered are founded on the looked-for criterion. The most corporate presentation principles are founded on the miscalculation criterion such as Integrated Absolute Error (IAE), Integrated of Time weight Square Error (ITSE) and Integrated of Error Square (ISE) [15][16][17]. Miscellany of these principles be determined by on both organization and controller.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…In this article, an Integral of Squared Error (ISE) dispassionate function is applied to treasure trove the optimal explanation with a minimum speed and azimuth miscalculation. The cost fitness is calculated as found in [14][15][16][17][18]:…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

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Design fuzzy neural petri net controller for trajectory tracking control of mobile robot

Saleh

Mohammed²,

Kadhim

et al. 2018

IJET

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In this paper, a Fuzzy Neural Petri Net (FNPN) controller has been designed established on Particle Swarm Optimization (PSO) for controlling the path tracking of Wheeled Mobile Robot (WMR). The path planning controller problem has been solved using two FNPN controllers to get the desired velocity and azimuth. The PSO method has used to detection the optimal values parameters of FNPN controllers. The overall models of wheeled mobile robot for path tracking control created on PSO algorithm are implemented in Simulink-Matlab. Simulation outcomes demonstrate the suggested FNPN controllers is more effectiveness and has good dynamic performance than the conventional methods.

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Section: Dynamics Of Mobile Robotmentioning

confidence: 99%

Section: Particle Swarm Optimizationmentioning

confidence: 99%

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Design fuzzy neural petri net controller for trajectory tracking control of mobile robot

Saleh

Mohammed²,

Kadhim

et al. 2018

IJET

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show abstract

“…For some time, however, the philosophy of the approach to the problem has been changing: Now, the group of drones is seen as composed of more autonomous elements, with much looser mutual constraints: The personal initiative of the individual vehicle becomes, from a philosophical-point-of-view implementation, much more important than “choral work”: Despite this radical change, it is noted that the swarm is able, in any case, to carry out the mission assigned to it and, often, in a faster and more efficient way [ 6 , 7 , 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Simulation of Autonomous Underwater Vehicles (AUVs) Swarm Diffusion

Petritoli

Cagnetti

Leccese

2020

Sensors

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The paper shows the simulation of the behavior of a swarm of underwater drones (AUV) diffused in a closed section of the sea and inserted from a single starting point: Based on a few essential rules, we will see how their behavior evolves and how they manage to spread throughout the area assigned to them. In the first part of this work, after defining the design of the vehicle, we introduce our vision of the swarm, its problems, and its strengths. Later, we show how to spread a series of underwater drones with “diffused intelligence” (swarm) and its microscopic diffusion model. In the last part, we present the simulation that supports our approach to the swarm.

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“…Rosli et al [27] pointed out through tests that the precision of BPNN optimized by particle swarm optimization (PSO) is higher than that optimized by GA. Although PSO is an excellent global optimization algorithm, there are premature convergence and local optimum problems in traditional PSO when solving complex problems [28].…”

Section: Introductionmentioning

confidence: 99%

Prediction NOX Emissions for High Speed DI Diesel Engine Based on PSOBP

Tan¹,

Yang²,

Yang³

et al. 2020

dteees

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In this paper, BP neural network (BPNN) is studied to model and predict NO X emission of direct injection diesel engine. The model selects four parameters as input, namely rotation speed, load, exhaust temperature and fuel-air ratio. Through testing, it is concluded that the prediction performance of BPNN model will be greatly affected by the initial weight and threshold, so that the prediction accuracy of the model is not high. In order to reduce the influence of initial weight and threshold on the prediction performance of BPNN model, this paper adopts Particle Swarm Optimization (PSO) algorithm to optimize the initial weight and threshold of BPNN, and establishes the corresponding prediction model of NO X emission of diesel engine. The results show that the prediction model of BPNN optimized by PSO algorithm can effectively reduce the influence of initial weight and threshold on BPNN and make the prediction results of the model more reliable. In particular, when PSO adopts non-linear dynamic weight strategy and synchronous learning factor strategy, the prediction performance of BPNN model is more provided with reliability.

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New social-based radius particle swarm optimization

Cited by 8 publications

References 12 publications

Design fuzzy neural petri net controller for trajectory tracking control of mobile robot

Design fuzzy neural petri net controller for trajectory tracking control of mobile robot

Simulation of Autonomous Underwater Vehicles (AUVs) Swarm Diffusion

Prediction NOX Emissions for High Speed DI Diesel Engine Based on PSOBP

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