A synchronization position control method based on dynamic particle swarm optimization algorithm in electro-hydraulic servo system

2013 ◽  
Author(s):  
Linghong Lai
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Position Control ◽  
Control Method ◽  
Particle Swarm ◽  
Servo System ◽  
Swarm Optimization ◽  
Hydraulic Servo ◽  
Hydraulic Servo System

An Integrator Backstepping Position Control of Electro-Hydraulic Servo System Based on Particle Swarm Optimization

2014 ◽  
Author(s):  
Ermiyas Assegu ◽  
Hamid Roozbahani ◽  
Heikki Handroos
Keyword(s):  
Particle Swarm Optimization ◽  
Position Control ◽  
Particle Swarm ◽  
Servo System ◽  
Test Bed ◽  
Hydraulic Actuator ◽  
Swarm Optimization ◽  
Integrator Backstepping ◽  
Hydraulic Servo ◽  
Hydraulic Servo System

This paper presents a position control of a single rod electro-hydraulic actuator based on an integrator backstepping approach and tuned controller gain parameters by using a Particle Swarm Optimization (PSO). The aim of this paper is to develop optimal Back Stepping Controller (BSC) architecture on a nonlinear Electro Hydraulic Servo System (EHSS) of asymmetric cylinder to improve the position control performance. The influences of getting the parameters on the control law are analyzed in order to establish some rules of optimization. This can be determined automatically and intelligently by minimizing Integral Square Error (ISE). The proposed controller is simulated and then implemented to the experimental test bed to track the desired signal and test the limits of its performance. The simulation and experimental results are given to demonstrate the effectiveness of the proposed controller.

Intelligent Control Method for Flanking Flight of Unmanned Aerial Vehicles

2019 ◽  
Vol 11 (4) ◽  
Author(s):  
Rui Wang ◽  
Xin-Li Yu ◽  
Nian-Chu Wu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Pid Control ◽  
Particle Swarm Optimization Algorithm ◽  
Control Method ◽  
Particle Swarm ◽  
Good Control ◽  
Control Effect ◽  
Swarm Optimization ◽  
Full Analysis

The angle control during the flight of UAV is the most important factor which affects its stability and safety. Since the traditional PID control method is difficult to automatically adjust the control parameters, a particle swarm optimization algorithm based on traditional PID control (PSO-PID), is proposed to construct a mathematical model of the flanking flight of the UAV. Based on the full analysis of the PID control principle, the UAV’s flanking flight controller based on PID control is constructed. The particle swarm optimization algorithm is introduced to optimize the PID parameters. The simulation model is built in MATLAB to investigate the position and altitude angle change of the UAV’s flank and compare it with the traditional PID control method. The experimental results show that the PSO-PID control strategy has a good control effect, which enables UAV’s flanking flight to reach the specified position more quickly and accurately than traditional PID controller alone.

scholarly journals A PID Parameter Tuning Method Based on the Improved QUATRE Algorithm

Algorithms ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 173
Author(s):  
Zhuo-Qiang Zhao ◽  
Shi-Jian Liu ◽  
Jeng-Shyang Pan
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Control Method ◽  
Particle Swarm ◽  
Parameter Tuning ◽  
Experimental Results ◽  
Control Effect ◽  
Swarm Optimization ◽  
Tuning Method

The PID (proportional–integral–derivative) controller is the most widely used control method in modern engineering control because it has the characteristics of a simple algorithm structure and easy implementation. The traditional PID controller, in the face of complex control objects, has been unable to meet the expected requirements. The emergence of the intelligent algorithm makes intelligent control widely usable. The Quasi-Affine Transformation Evolutionary (QUATRE) algorithm is a new evolutionary algorithm. Compared with other intelligent algorithms, the QUATRE algorithm has a strong global search ability. To improve the accuracy of the algorithm, the adaptive mechanism of online adjusting control parameters was introduced and the linear population reduction strategy was adopted to improve the performance of the algorithm. The standard QUATRE algorithm, particle swarm optimization algorithm and improved QUATRE algorithm were tested by the test function. The experimental results verify the advantages of the improved QUATRE algorithm. The improved QUATRE algorithm was combined with PID parameters, and the simulation results were compared with the PID parameter tuning method based on the particle swarm optimization algorithm and standard QUATRE algorithm. From the experimental results, the control effect of the improved QUATRE algorithm is more effective.

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