Fractional Order Controller Design for Ball and Beam System

2013 ◽  
Vol 313-314 ◽  
pp. 544-548 ◽  
Author(s):  
Mehmet Korkmaz ◽  
Omer Aydogdu
Keyword(s):  
Genetic Algorithms ◽  
Particle Swarm Optimization ◽  
Evolutionary Algorithms ◽  
Fractional Order ◽  
Controller Design ◽  
Swarm Optimization ◽  
System Parameters ◽  
Beam System ◽  
Simulation Results ◽  
Fractional Order Controller

Fractional order controllers which has mostly used recently have investigated in this paper. It is benefit from ball & beam system to show effects of controllers. Fractional order controller and its integer form are compared with simulation results for the mentioned system. Parameters of controllers have obtained by using evolutionary algorithms techniques which are particle swarm optimization (PSO) and genetic algorithms (GAs). According to results, it is confirmed the advantage of fractional controllers. Beside, PSO has a little bit superiority over GAs technique for determining optimum values of controller parameters.

scholarly journals Performance of Resource Allocation in Device-to-Device Communication Systems Based on Evolutionally Optimization Algorithms

2018 ◽  
Author(s):  
Hsu-Tan Tan ◽  
Bor-An Chen ◽  
Yung-Fa Huang
Keyword(s):  
Particle Swarm Optimization ◽  
Evolutionary Algorithms ◽  
Communication Systems ◽  
Particle Swarm ◽  
Optimal Solution ◽  
System Capacity ◽  
Swarm Optimization ◽  
Device To Device ◽  
Simulation Results ◽  
Capacity Performance

In this study, the resource blocks (RB) are allocated to user equipment (UE) according to the evolutional algorithms for long term evolution (LTE) systems. Particle Swarm Optimization (PSO) algorithm is one of the evolutionary algorithms, based on the imitation of a flock of birds foraging behavior through learning and grouping the best experience. In previous work, the Simple Particle Swarm Optimization (SPSO) algorithm was proposed for RB allocation to enhance the throughput of Device-to-Device (D2D) communications and improve the system capacity performance. In simulation results, with less population size of M = 10, the SPSO can perform quickly convergence to sub-optimal solution in the 100th generation and obtained sub-optimum performance with more 2 UEs than the Rand method. Genetic algorithm (GA) is one of the evolutionary algorithms, based on Darwinian models of natural selection and evolution. Therefore, we further proposed a Refined PSO (RPSO) and a novel GA to enhance the throughput of UEs and to improve the system capacity performance. Simulation results show that the proposed GA with 100 populations, in 200 generations can converge to suboptimal solutions. Therefore, with comparing with the SPSO algorithm the proposed GA and RPSO can improve system capacity performance with 1.8 and 0.4 UEs, respectively.

SIWPSO-Based Controller Design for AUV

2014 ◽  
Vol 525 ◽  
pp. 736-740
Author(s):  
Jau Woei Perng ◽  
Yi Shyang Huang ◽  
Shiang Shiuan Huang ◽  
Guan Yan Chen ◽  
Chin Yin Chen ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Dynamic Model ◽  
Pid Controller ◽  
Autonomous Underwater Vehicle ◽  
Controller Design ◽  
Underwater Vehicle ◽  
Proportional Integral Derivative ◽  
Swarm Optimization ◽  
Linear And Nonlinear ◽  
Simulation Results

A strategy is proposed for a control system with a linearized autonomous underwater vehicle (AUV) dynamic model. The proposed approach combines the particle swarm optimization (PSO) and proportional-integral-derivative (PID) controller to adjust the parameters of the linearized dynamic model. The linear and nonlinear model are both considered in our work. The proposed techniques is verified by using the simulation results to the model of AUV.

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