scholarly journals Design and Implementation of Wheel Chair Control System Using Particle Swarm Algorithm

2021 ◽  
Vol 66 (2) ◽  
pp. 2005-2023
Author(s):  
G. Mousa ◽  
Amr Almaddah ◽  
Ayman A. Aly
Keyword(s):  
Control System ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Design And Implementation ◽  
Wheel Chair ◽  
Swarm Algorithm

scholarly journals Control system research in wave compensation based on particle swarm optimization

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gang Tang ◽  
Peng Lu ◽  
Xiong Hu ◽  
Shaoyang Men
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Peak Time ◽  
Optimization Strategy ◽  
Swarm Optimization ◽  
Compensation Control ◽  
Swarm Algorithm

AbstractFor the offshore wave compensation control system, its controller setting will directly affect the platform's compensation effect. In order to study the wave compensation control system and optimization strategy, we build and simulate the wave compensation control model by using particle swarm optimization (PSO) to optimize the controller's control parameters and compare the results with other intelligent algorithms. Then we compare the response errors of the wave compensation platform under different PID controllers; and compare the particle swarm algorithm's response results and the genetic algorithm to the system controller optimization. The results show that the particle swarm algorithm is 63.94% lower than the genetic algorithm overshoot, and the peak time is 0.26 s lower, the adjustment time is 1.4 s lower than the genetic algorithm. It shows that the control effect of the wave compensation control system has a great relationship with the controller's parameter selection. Meanwhile, the particle swarm optimization algorithm's optimization can set the wave compensation PID control system, and it has the optimization effect of small overshoot and fast response time. This paper proposes the application of the particle swarm algorithm to the wave compensation system. It verifies the superiority of the method after application, and provides a new research reference for the subsequent research on the wave compensation control systems.

Research on Optimization and Verification Method of Sensor Arrangement in the Chemical and Volume Control System

2021 ◽  
Author(s):  
Gui Zhou ◽  
Hang Wang ◽  
Minjun Peng
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Volume Control ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Basic Particle ◽  
Swarm Algorithm

Abstract In order to avoid the nuclear accidents during the operation of nuclear power plants, it is necessary to always monitor the status of relevant facilities and equipment. The premise of condition monitoring is that the sensor can provide sufficient and accurate operating parameters. Therefore, the sensor arrangement must be rationalized. As one of the nuclear auxiliary systems, the chemical and volume control system plays an important role in ensuring the safe operation of nuclear power plants. There are plenty of sensor measuring points arranged in the chemical and volume control system. These sensors are not only for detecting faults, but also for running and controlling services. Particle swarm algorithm has many applications in solving the problem of sensor layout optimization but the disadvantage of the basic particle swarm optimization algorithm is that the parameters are fixed, the particles are single, and it is easy to fall into the local optimization. In this paper, the basic particle swarm optimization algorithm is improved by Non-linearly adjusting inertia weight factor, asynchronously changing learning factor, and variating particle. The improved particle swarm optimization algorithm is used to optimize the sensor placement. The numerical analysis verified that a smaller number of sensors can meet the fault detection requirements of the chemical and volume control system in this paper, and Experiments have proved that the improved particle swarm algorithm can improve the basic particle swarm algorithm, which is easy to fall into the shortcomings of local optimization and single particles. This method has good applicability, and could be also used to optimize other systems with sufficient parameters and consistent objective function.

Optimization Technology of PID Parameter in Control System Based on Improved Particle Swarm Optimization Algorithms

2014 ◽  
Vol 908 ◽  
pp. 547-550
Author(s):  
Tian Shun Huang ◽  
Xiao Qiang Li ◽  
Hong Yun Lian ◽  
Zhi Qiang Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Mixed Integer ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Combinatorial Optimization Problems ◽  
Swarm Algorithm

Particle swarm algorithm has been proven to be very good solving many global optimization problems. Firstly we improved particle swarm optimization algorithm, the improved PSO algorithm for continuous optimization problem, in solving the nonlinear combinatorial optimization problems and mixed integer nonlinear optimization problems is very effective. This design adopts the improved particle swarm algorithm to optimize the PID parameters of the control system, and the effectiveness of the improved algorithm is proved by experiment.

scholarly journals Optimal Piezoelectric Actuators and Sensors Configuration for Vibration Suppression of Aircraft Framework Using Particle Swarm Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Quanzhen Huang ◽  
Suxia Chen ◽  
Huayan Pu ◽  
Ning Zhang
Keyword(s):  
Control System ◽  
Objective Function ◽  
Particle Swarm ◽  
Piezoelectric Actuators ◽  
Optimal Location ◽  
Particle Swarm Algorithm ◽  
Sensors And Actuators ◽  
Optimal Criterion ◽  
Swarm Algorithm ◽  
Piezoelectric Actuators And Sensors

Numbers and locations of sensors and actuators play an important role in cost and control performance for active vibration control system of piezoelectric smart structure. This may lead to a diverse control system if sensors and actuators were not configured properly. An optimal location method of piezoelectric actuators and sensors is proposed in this paper based on particle swarm algorithm (PSA). Due to the complexity of the frame structure, it can be taken as a combination of many piezoelectric intelligent beams and L-type structures. Firstly, an optimal criterion of sensors and actuators is proposed with an optimal objective function. Secondly, each order natural frequency and modal strain are calculated and substituted into the optimal objective function. Preliminary optimal allocation is done using the particle swarm algorithm, based on the similar optimization method and the combination of the vibration stress and strain distribution at the lower modal frequency. Finally, the optimal location is given. An experimental platform was established and the experimental results indirectly verified the feasibility and effectiveness of the proposed method.

Dynamic simulation of wind field in three-dimensional display space considering particle swarm algorithm fuzzy logic

2021 ◽  
pp. 1-6
Author(s):  
Honglei Xu ◽  
Linhuan Wang
Keyword(s):  
Fuzzy Logic ◽  
Dynamic Simulation ◽  
Wind Field ◽  
Three Dimensional ◽  
Measurement Data ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Three Dimensional Display ◽  
Swarm Algorithm ◽  
Synchronous Measurement

In order to improve the accuracy of dynamic detection of wind field in the three-dimensional display space, system software is carried out on the actual scene and corresponding airborne radar observation information data, and the particle swarm algorithm fuzzy logic algorithm is introduced into the wind field dynamic simulation process in three-dimensional display space, to analyze the error of the filtering result in detail, to process the hurricane Lily Doppler radar measurement data with the optimal adaptive filtering according to the error data. The three-dimensional wind field synchronous measurement data obtained by filtering was compared with three-dimensional wind field synchronous measurement data of the GPS dropsonde in this experiment, the sea surface wind field measurement data of the multi-band microwave radiometer, and the wind field data at aircraft altitude.

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