Particle Swarm Optimization: An efficient tool for the design of Automatic Control laws

2009 ◽  
Author(s):  
G. Sandou
Keyword(s):  
Particle Swarm Optimization ◽  
Automatic Control ◽  
Particle Swarm ◽  
Efficient Tool ◽  
Swarm Optimization ◽  
Control Laws

Multi-Objective Particle Swarm Optimization for Control Laws Design

2013 ◽  
Vol 333-335 ◽  
pp. 1361-1365
Author(s):  
Xiao Xiong Liu ◽  
Heng Xu ◽  
Yan Wu ◽  
Peng Hui Li
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Tracking Error ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Control Laws ◽  
Reference Models ◽  
Multi Objective ◽  
Simulation Results

In order to overcome the difficult of large amount of calculation and to satisfy multiple design indicators in the design of control laws, an improved multi-objective particle swarm optimization (PSO) algorithm was used to design control laws of aircraft. Firstly, the hybrid concepts of genetic algorithm were introduced to particle swarm optimization (PSO) algorithm to improve the algorithm. Then based on aircraft flying quality the reference models were built, and then the tracking error, settling time and overshoot were used as the optimization goal of the control laws design. Based on this multi-objective optimize problem the attitude hold control laws were designed. The simulation results show the effectiveness of the algorithm.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

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