Metaheuristic Design and Optimization of Fuzzy-Based Gas Turbine Engine Fuel Controller Using Hybrid Invasive Weed Optimization/Particle Swarm Optimization Algorithm

2013 ◽  
Vol 136 (3) ◽  
Author(s):  
E. Mohammadi ◽  
M. Montazeri-Gh ◽  
P. Khalaf
Keyword(s):  
Particle Swarm Optimization ◽  
Objective Function ◽  
Gas Turbine ◽  
Pso Algorithm ◽  
Engine Fuel ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Swarm Optimization ◽  
Turbine Engine ◽  
Design And Optimization

This paper presents the metaheuristic design and optimization of fuzzy-based gas turbine engine (GTE) fuel flow controller by means of a hybrid invasive weed optimization/particle swarm optimization (IWO/PSO) algorithm as an innovative guided search technique. In this regard, first, a Wiener model for the GTE as a block-structured model is developed and validated against experimental data. Subsequently, because of the nonlinear nature of GTE, a fuzzy logic controller (FLC) strategy is considered for the engine fuel system. For this purpose, an initial FLC is designed and the parameters are then tuned using a hybrid IWO/PSO algorithm where the tuning process is formulated as an engineering optimization problem. The fuel consumption, engine safety, and time response are the performance indices of the defined objective function. In addition, two sets of weighting factors for objective function are considered, whereas in one of them savings in fuel consumption and in another achieving a short response time for the engine is a priority. Moreover, the optimization process is performed in two stages during which the database and the rule base of the initial FLC are tuned sequentially. The simulation results confirm that the IWO/PSO-FLC approach is effective for GTE fuel controller design, resulting in improved engine performance as well as ensuring engine protection against physical limitations.

Fuzzy-Based Gas Turbine Engine Fuel Controller Design Using Particle Swarm Optimization

2011 ◽  
Vol 110-116 ◽  
pp. 3215-3222 ◽  
Author(s):  
M. Montazeri-Gh ◽  
E. Mohammadi ◽  
S. Jafari
Keyword(s):  
Particle Swarm Optimization ◽  
Gas Turbine ◽  
Fuzzy Logic Controller ◽  
Particle Swarm ◽  
Gas Turbine Engine ◽  
Time Response ◽  
Rule Base ◽  
Engine Fuel ◽  
Swarm Optimization ◽  
Turbine Engine

This paper presents the application of Particle Swarm Optimization (PSO) algorithm for optimization of the Gas Turbine Engine (GTE) fuel control system. In this study, the Wiener model for GTE as a block structure model is firstly developed. This representation is an appropriate model for controller tuning. Subsequently, based on the nonlinear GTE nature, a Fuzzy Logic Controller (FLC) with an initial rule base is designed for the engine fuel system. Then, the initial FLC is tuned by PSO with emphasis on the engine safety and time response. In this study, the optimization process is performed in two stages during which the Data Base (DB) and the Rule Base (RB) of the initial FLC are tuned sequentially. The results obtained from the simulation show the ability of the approach to achieve an acceptable time response and to attain a safe operation by limiting the turbine rotor acceleration.

scholarly journals Recursive Neural Networks Based on PSO for Image Parsing

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Guo-Rong Cai ◽  
Shui-Li Chen
Keyword(s):  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Objective Function ◽  
State Of The Art ◽  
Pso Algorithm ◽  
Training Algorithm ◽  
Image Parsing ◽  
Swarm Optimization ◽  
Recursive Neural Networks ◽  
Parsing Algorithm

This paper presents an image parsing algorithm which is based on Particle Swarm Optimization (PSO) and Recursive Neural Networks (RNNs). State-of-the-art method such as traditional RNN-based parsing strategy uses L-BFGS over the complete data for learning the parameters. However, this could cause problems due to the nondifferentiable objective function. In order to solve this problem, the PSO algorithm has been employed to tune the weights of RNN for minimizing the objective. Experimental results obtained on the Stanford background dataset show that our PSO-based training algorithm outperforms traditional RNN, Pixel CRF, region-based energy, simultaneous MRF, and superpixel MRF.

scholarly journals Design and Optimization of a Millimetre Wave Compact Folded Magic-T

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
Guang Hua ◽  
Jiefu Zhang ◽  
Jiudong Wu ◽  
Wei Hong
Keyword(s):  
Particle Swarm Optimization ◽  
Return Loss ◽  
Pso Algorithm ◽  
Geometrical Parameters ◽  
Design Features ◽  
Swarm Optimization ◽  
Millimetre Wave ◽  
Design And Optimization ◽  
Centre Frequency ◽  
W Band

A millimetre wave-folded magic-T junction compensated with metal cone is designed using a particle swarm optimization (PSO) algorithm. An off-centred metallic frustum was used to enhance the bandwidth and a metallic post is used to compensate the mismatched E-arm. The geometrical parameters of the frustum and the post are optimized by PSO. The optimized magic-T for W-band application is designed and tested. The design features are simple in structure and easy to fabricate. The 2% bandwidth with centre frequency of 94 GHz and return loss less than −20 dB is achieved.

Neural Network Assisted Particle Swarm Optimization of Machining Process

2013 ◽  
Vol 581 ◽  
pp. 511-516
Author(s):  
Uros Zuperl ◽  
Franci Cus
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Objective Function ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Machining Process ◽  
Neural Network Modeling ◽  
Swarm Optimization ◽  
Turning Operation ◽  
Optimization System

In this paper, optimization system based on the artificial neural networks (ANN) and particle swarm optimization (PSO) algorithm was developed for the optimization of machining parameters for turning operation. The optimization system integrates the neural network modeling of the objective function and particle swarm optimization of turning parameters. New neural network assisted PSO algorithm is explained in detail. An objective function based on maximum profit, minimum costs and maximum cutting quality in turning operation has been used. This paper also exhibits the efficiency of the proposed optimization over the genetic algorithms (GA), ant colony optimization (ACO) and simulated annealing (SA).

scholarly journals Conditional nonlinear optimal perturbations based on the particle swarm optimization and their applications to the predictability problems

2017 ◽  
Vol 24 (1) ◽  
pp. 101-112 ◽  
Author(s):  
Qin Zheng ◽  
Zubin Yang ◽  
Jianxin Sha ◽  
Jun Yan
Keyword(s):  
Particle Swarm Optimization ◽  
Objective Function ◽  
Extreme Values ◽  
Initial Perturbation ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Strong Nonlinearity ◽  
Swarm Optimization ◽  
Prediction Time ◽  
The Impact

Abstract. In predictability problem research, the conditional nonlinear optimal perturbation (CNOP) describes the initial perturbation that satisfies a certain constraint condition and causes the largest prediction error at the prediction time. The CNOP has been successfully applied in estimation of the lower bound of maximum predictable time (LBMPT). Generally, CNOPs are calculated by a gradient descent algorithm based on the adjoint model, which is called ADJ-CNOP. This study, through the two-dimensional Ikeda model, investigates the impacts of the nonlinearity on ADJ-CNOP and the corresponding precision problems when using ADJ-CNOP to estimate the LBMPT. Our conclusions are that (1) when the initial perturbation is large or the prediction time is long, the strong nonlinearity of the dynamical model in the prediction variable will lead to failure of the ADJ-CNOP method, and (2) when the objective function has multiple extreme values, ADJ-CNOP has a large probability of producing local CNOPs, hence making a false estimation of the LBMPT. Furthermore, the particle swarm optimization (PSO) algorithm, one kind of intelligent algorithm, is introduced to solve this problem. The method using PSO to compute CNOP is called PSO-CNOP. The results of numerical experiments show that even with a large initial perturbation and long prediction time, or when the objective function has multiple extreme values, PSO-CNOP can always obtain the global CNOP. Since the PSO algorithm is a heuristic search algorithm based on the population, it can overcome the impact of nonlinearity and the disturbance from multiple extremes of the objective function. In addition, to check the estimation accuracy of the LBMPT presented by PSO-CNOP and ADJ-CNOP, we partition the constraint domain of initial perturbations into sufficiently fine grid meshes and take the LBMPT obtained by the filtering method as a benchmark. The result shows that the estimation presented by PSO-CNOP is closer to the true value than the one by ADJ-CNOP with the forecast time increasing.

Sign in / Sign up

Export Citation Format

Share Document