scholarly journals Application of PID optimization control strategy based on particle swarm optimization (PSO) for battery charging system

2020 ◽  
Vol 15 (4) ◽  
pp. 528-535
Author(s):  
Tiezhou Wu ◽  
Cuicui Zhou ◽  
Zhe Yan ◽  
Huigang Peng ◽  
Linzhang Wu
Keyword(s):  
Particle Swarm Optimization ◽  
Control Method ◽  
Dynamic Performance ◽  
Particle Swarm ◽  
Control Effect ◽  
Swarm Optimization ◽  
Battery Charging ◽  
Charging System ◽  
Modified Particle Swarm Optimization ◽  
Charging Efficiency

Abstract The battery charging process has nonlinear and hysteresis properties. PID (Proportion Integration Differentiation) control is a conventional control method used in the battery charging process. The control effect is determined by the PID control parameters ${K}_p$,  ${K}_i$  and  ${K}_d$. The traditional PID parameter setting method is difficult to give the appropriate parameters, which affects the battery charging efficiency. In this paper, the particle swarm optimization (PSO) is used to optimize the PID parameters. Aiming at the defects of basic PSO, such as slow convergence speed, low convergence precision and easy to be premature, a modified particle swarm optimization algorithm is proposed, and the optimized PID parameters are applied to the battery charging control system. Also, the experimental results show that the battery charging process possesses better dynamic performance and the charging efficiency of the battery has increased from 86.44% to 91.47%, and the charging temperature rise has dropped by 1°C.

Double-Loop PID Parameter Tuning for DC Motor Based on Modified Particle Swarm Optimization

2011 ◽  
Vol 130-134 ◽  
pp. 3139-3142
Author(s):  
Tao Cheng ◽  
Wei Xing Lin
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
High Performance ◽  
Dynamic Performance ◽  
Particle Swarm ◽  
Parameter Tuning ◽  
Parameters Optimization ◽  
Double Loop ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization

This paper proposes a modified particle swarm optimization to solve identification of tuning PID controller parameters. This paper elaborates the process that MPSO algorithm optimizes PID parameters in double-loop speed control system modeled by simulink. Through analyzing the results of the MPSO optimization, and comparing with standard PSO(SPSO) and traditional method, MPSO algorithm has better dynamic performance, provides a high performance methods for PID parameters optimization.

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.

scholarly journals Particle swarm optimization algorithm for the optimization of rescue task allocation with uncertain time constraints

2021 ◽  
Author(s):  
Na Geng ◽  
Zhiting Chen ◽  
Quang A. Nguyen ◽  
Dunwei Gong
Keyword(s):  
Particle Swarm Optimization ◽  
Task Allocation ◽  
Optimal Algorithm ◽  
Particle Swarm ◽  
Evolutionary Process ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Fitness Evaluation ◽  
Modified Particle Swarm Optimization ◽  
Uncertain Time

AbstractThis paper focuses on the problem of robot rescue task allocation, in which multiple robots and a global optimal algorithm are employed to plan the rescue task allocation. Accordingly, a modified particle swarm optimization (PSO) algorithm, referred to as task allocation PSO (TAPSO), is proposed. Candidate assignment solutions are represented as particles and evolved using an evolutionary process. The proposed TAPSO method is characterized by a flexible assignment decoding scheme to avoid the generation of unfeasible assignments. The maximum number of successful tasks (survivors) is considered as the fitness evaluation criterion under a scenario where the survivors’ survival time is uncertain. To improve the solution, a global best solution update strategy, which updates the global best solution depends on different phases so as to balance the exploration and exploitation, is proposed. TAPSO is tested on different scenarios and compared with other counterpart algorithms to verify its efficiency.

Sign in / Sign up

Export Citation Format

Share Document