scholarly journals Reaction Control System Optimization for Maneuverable Reentry Vehicles Based on Particle Swarm Optimization

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Hang Gui ◽  
Ruisheng Sun ◽  
Wei Chen ◽  
Bin Zhu
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
System Optimization ◽  
Suboptimal Control ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Reentry Vehicles ◽  
Reaction Control System

This paper presents a new parametric optimization design to solve a class of reaction control system (RCS) problem with discrete switching state, flexible working time, and finite-energy control for maneuverable reentry vehicles. Based on basic particle swarm optimization (PSO) method, an exponentially decreasing inertia weight function is introduced to improve convergence performance of the PSO algorithm. Considering the PSO algorithm spends long calculation time, a suboptimal control and guidance scheme is developed for online practical design. By tuning the control parameters, we try to acquire efficacy as close as possible to that of the PSO-based solution which provides a reference. Finally, comparative simulations are conducted to verify the proposed optimization approach. The results indicate that the proposed optimization and control algorithm has good performance for such RCS of maneuverable reentry vehicles.

Improved Particle Swarm Optimization Approach for Vibration Vision Measurement

2020 ◽  
pp. 2159011
Author(s):  
Chunli Zhu ◽  
Yuan Shen ◽  
Xiujun Lei
Keyword(s):  
Particle Swarm Optimization ◽  
Motion Estimation ◽  
Search Algorithm ◽  
Computational Cost ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Vision Measurement ◽  
Improved Pso

Traditional template matching-based motion estimation is a popular but time-consuming method for vibration vision measurement. In this study, the particle swarm optimization (PSO) algorithm is improved to solve this time-consumption problem. The convergence speed of the algorithm is increased using the adjacent frames search method in the particle swarm initialization process. A flag array is created to avoid repeated calculation in the termination strategy. The subpixel positioning accuracy is ensured by applying the surface fitting method. The robustness of the algorithm is ensured by applying the zero-mean normalized cross correlation. Simulation results demonstrate that the average extraction error of the improved PSO algorithm is less than 1%. Compared with the commonly used three-step search algorithm, diamond search algorithm, and local search algorithm, the improved PSO algorithm consumes the least number of search points. Moreover, tests on real-world image sequences show good estimation accuracy at very low computational cost. The improved PSO algorithm proposed in this study is fast, accurate, and robust, and is suitable for plane motion estimation in vision measurement.

scholarly journals A Novel Optimal Control Method for Impulsive-Correction Projectile Based on Particle Swarm Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Ruisheng Sun ◽  
Qiao Hong ◽  
Gang Zhu
Keyword(s):  
Optimal Control ◽  
Particle Swarm Optimization ◽  
Control Algorithm ◽  
Reference Model ◽  
Particle Swarm ◽  
Suboptimal Control ◽  
Time Interval ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Model Linearization

This paper presents a new parametric optimization approach based on a modified particle swarm optimization (PSO) to design a class of impulsive-correction projectiles with discrete, flexible-time interval, and finite-energy control. In terms of optimal control theory, the task is described as the formulation of minimum working number of impulses and minimum control error, which involves reference model linearization, boundary conditions, and discontinuous objective function. These result in difficulties in finding the global optimum solution by directly utilizing any other optimization approaches, for example, Hp-adaptive pseudospectral method. Consequently, PSO mechanism is employed for optimal setting of impulsive control by considering the time intervals between two neighboring lateral impulses as design variables, which makes the briefness of the optimization process. A modification on basic PSO algorithm is developed to improve the convergence speed of this optimization through linearly decreasing the inertial weight. In addition, a suboptimal control and guidance law based on PSO technique are put forward for the real-time consideration of the online design in practice. Finally, a simulation case coupled with a nonlinear flight dynamic model is applied to validate the modified PSO control algorithm. The results of comparative study illustrate that the proposed optimal control algorithm has a good performance in obtaining the optimal control efficiently and accurately and provides a reference approach to handling such impulsive-correction problem.

A novel particle swarm optimization based robust H-infinity control for rotorcrafts

2014 ◽  
Vol 31 (4) ◽  
pp. 726-741 ◽  
Author(s):  
Jiyang Dai ◽  
Jin Ying ◽  
Chang Tan
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Adaptive Mutation ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
H Infinity ◽  
Content Type ◽  
Modified Particle Swarm Optimization ◽  
H Infinity Control

Purpose – The purpose of this paper is to present a novel optimization approach to design a robust H-infinity controller. Design/methodology/approach – To use a modified particle swarm optimization (PSO) algorithm and to search for the optimal parameters of the weighting functions under the circumstance of the given structures of three weighting matrices in the H-infinity mixed sensitivity design. Findings – This constrained multi-objective optimization is a non-convex, non-smooth problem which is solved by a modified PSO algorithm. An adaptive mutation-based PSO (AMBPSO) algorithm is proposed to improve the search accuracy and convergence of the standard PSO algorithm. In the AMBPSO algorithm, the inertia weights are modified as a function with the gradient descent and the velocities and positions of the particles. Originality/value – The AMBPSO algorithm can efficiently solve such an optimization problem that a satisfactory robust H-infinity control performance can be obtained.

scholarly journals A Weighted Ensemble Learning Algorithm Based on Diversity Using a Novel Particle Swarm Optimization Approach

Algorithms ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 255
Author(s):  
Gui-Rong You ◽  
Yeou-Ren Shiue ◽  
Wei-Chang Yeh ◽  
Xi-Li Chen ◽  
Chih-Ming Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Ensemble Learning ◽  
Learning Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Ensemble Classifier ◽  
Optimization Approach ◽  
Classifier Ensembles ◽  
Swarm Optimization ◽  
Factors Affecting

In ensemble learning, accuracy and diversity are the main factors affecting its performance. In previous studies, diversity was regarded only as a regularization term, which does not sufficiently indicate that diversity should implicitly be treated as an accuracy factor. In this study, a two-stage weighted ensemble learning method using the particle swarm optimization (PSO) algorithm is proposed to balance the diversity and accuracy in ensemble learning. The first stage is to enhance the diversity of the individual learner, which can be achieved by manipulating the datasets and the input features via a mixed-binary PSO algorithm to search for a set of individual learners with appropriate diversity. The purpose of the second stage is to improve the accuracy of the ensemble classifier using a weighted ensemble method that considers both diversity and accuracy. The set of weighted classifier ensembles is obtained by optimization via the PSO algorithm. The experimental results on 30 UCI datasets demonstrate that the proposed algorithm outperforms other state-of-the-art baselines.

A Particle Swarm Optimization Approach for Minimizing GD&T Error in Additive Manufactured Parts

2017 ◽  
Vol 7 (3) ◽  
pp. 69-80 ◽  
Author(s):  
Vimal Kumar Pathak ◽  
Amit Kumar Singh
Keyword(s):  
Particle Swarm Optimization ◽  
Process Parameters ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Geometrical Accuracy ◽  
Bed Temperature ◽  
Design Variables ◽  
Flatness Error

This paper presents a particle swarm optimization (PSO) approach to improve the geometrical accuracy of additive manufacturing (AM) parts by minimizing geometrical dimensioning and tolerancing (GD&T) error. Four AM process parameters viz. Bed temperature, nozzle temperature, Infill, layer thickness are taken as input while circularity and flatness error in ABS part are taken as response. A mathematical model is developed for circularity and flatness error individually using regression technique in terms of process parameters as design variables. For the optimum search of the AM process parameter values, minimization of circularity and flatness are formulated as multi-objective, multi-variable optimization problem which is optimized using particle swarm optimization (PSO) algorithm and hence improving the geometrical accuracy of the ABS part.

A Inflexion Nonlinear Global Particle Swarm Optimization (PSO) Algorithm

2014 ◽  
Vol 989-994 ◽  
pp. 1582-1585
Author(s):  
Li Xia Lv ◽  
Xiang Yu Lin
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Pressure Control ◽  
Temperature Control System ◽  
Swarm Optimization ◽  
Position Information ◽  
Global Average ◽  
Long Time

According to the question of the standard particle swarm optimization (PSO) algorithm is prone to premature and no convergence phenomenon, this paper proposed an algorithm of Inflection nonlinear global PSO. The algorithm introduces nonlinear trigonometric factor and the global average location information in the formula of velocity updating. It take advantage of the convex of the triangle function cause the particles early in the larger velocity search maintain long time and in the later searching with smaller speed maintain long time, use the global average position information make the population can use more information to update their position. The method are applied in optimizing in the parameters of the main steam temperature control system and furnace pressure control system for comparison, the results show that the method in the search speed and precision than standard PSO has significantly improved.

An Particle Swarm Optimization Approach for Assembly Sequence Planning

2009 ◽  
Vol 16-19 ◽  
pp. 1228-1232 ◽  
Author(s):  
Hong Yu ◽  
Jia Peng Yu ◽  
Wen Lei Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimization Techniques ◽  
Assembly Sequence Planning ◽  
Assembly Planning ◽  
Optimization Approach ◽  
Assembly Sequence ◽  
Swarm Optimization ◽  
Sequence Planning

Assembly sequence planning (ASP) is the foundation of the assembly planning which plays a key role in the whole product life cycle. Although the ASP problem has been tackled via a variety of optimization techniques, the particle swarm optimization (PSO) algorithm is scarcely used. This paper presents a PSO algorithm to solve ASP problem. Unlike generic versions of particle swarm optimization, the algorithm redefines the particle's position and velocity, and operation of updating particle positions. In order to overcome the problem of premature convergence, a new study mechanism is adopted. The geometrical constraints, assembly stability and the changing times of assembly directions are used as the criteria for the fitness function. To validate the performance of the proposed algorithm, a 29-component product is tested by this algorithm. The experimental results indicate that the algorithm proposed in this paper is effective for the ASP.

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