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.

Solving Nonlinear Optimization Problems of Steering Trapezoid Mechanism Based on an IPSO

2014 ◽  
Vol 945-949 ◽  
pp. 607-613
Author(s):  
Ling Liu ◽  
Pei Zhou ◽  
Jun Luo ◽  
Zan Pi
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Nonlinear Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Steering Wheel ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Rotational Angle ◽  
Improved Particle Swarm Optimization

The paper focus on an improved particle swarm optimization (IPSO) used to solve nonlinear optimization problems of steering trapezoid mechanism. First of all, nonlinear optimization model of steering trapezoid mechanism is established. Sum of absolute value of difference between actual rotational angle of anterolateral steering wheel and theoretical rotational angle of anterolateral steering wheel is taken as objective function, bottom angle and steering arm length of steering trapezoid mechanism are selected to be design variables. After that, an improved particle swarm optimization algorithm is proposed by introducing Over-flow exception dealing functions to deal with complicated nonlinear constraints. Finally, codes for IPSO are programmed and parameters of steering trapezoid mechanism for different models are optimized, and numerical result shows that errors of objective function's ideal values and objective function's optimization values are minimal. Performance comparison experiment of different intelligent algorithms indicates that the proposed new algorithm is superior to Particle swarm algorithm based on simulated annealing (SA-PSO) and traditional particle swarm optimization (TPSO) in good and fast convergence and small calculating quantity, but a little inferior to particle swarm algorithm based on simulated annealing (SA-PSO) in calculation accuracy in the process of optimization.

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.

scholarly journals Particle Swarm Optimization Algorithm in Improved Electrical Control System

2021 ◽  
Vol 2066 (1) ◽  
pp. 012020
Author(s):  
Xiaotao Tian
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Control Systems ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Electrical Control

Abstract In today’s social background where high-tech emerges endlessly, various production fields in our country have fully entered the era of mechanical automation and electrical automation, and electrical control systems have been widely used in our country’s electrical appliance manufacturing industry. This paper is based on the theoretical analysis of the particle swarm optimization algorithm. Based on this optimization algorithm, a brand-new particle swarm optimization algorithm is obtained. It is applied to the electrical control system to improve it and makes full use of the improved particle swarm optimization algorithm. The existing electrical control system is optimized. This article firstly analyzes the types of common electrical control systems, puts forward some basic methods to improve the control system, and then explains the effective techniques for improvement, hoping to make reference to the improvement of electrical control systems later in this article. This article first improves the particle swarm optimization algorithm, adding the ability to adjust the control system and dynamic learning factors, focusing on strengthening the later stage of the optimization of the particle swarm algorithm and the ability to converge to improve the efficiency of the calculation. The second is to improve the traditional particle swarm optimization algorithm and update the calculation method of the formula to reduce the possibility of selecting undesirable particles and affecting the optimization results. Finally, through MATLAB and reverse simulation analysis, compared with the traditional electrical control system algorithm, the improved particle swarm optimization algorithm has a faster convergence speed and high control system efficiency. The experimental research results show that the particle swarm optimization algorithm proposed in this paper has a huge advantage compared with other algorithms, and its parameter optimization gives full play to the powerful performance of the electrical control system.

Improved Particle Swarm Optimization Algorithm and its Application Based on the Aggregation Degree

2013 ◽  
Vol 427-429 ◽  
pp. 1934-1938
Author(s):  
Zhong Rong Zhang ◽  
Jin Peng Liu ◽  
Ke De Fei ◽  
Zhao Shan Niu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Population Diversity ◽  
Function Optimization ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

The aim is to improve the convergence of the algorithm, and increase the population diversity. Adaptively particles of groups fallen into local optimum is adjusted in order to realize global optimal. by judging groups spatial location of concentration and fitness variance. At the same time, the global factors are adjusted dynamically with the action of the current particle fitness. Four typical function optimization problems are drawn into simulation experiment. The results show that the improved particle swarm optimization algorithm is convergent, robust and accurate.

Introduction of New Anti-dumping Algorithm in EU under WTO Law System Based on Particle Swarm Optimization

2021 ◽  
Vol 7 (5) ◽  
pp. 4558-4567
Author(s):  
Wenwen Deng
Keyword(s):  
Particle Swarm Optimization ◽  
Operation Mode ◽  
Particle Swarm ◽  
Algorithm Analysis ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Law System ◽  
Incomplete System ◽  
Swarm Algorithm ◽  
The Eu

Objectives: Anti dumping new algorithm is an innovative ability based on the WTO legal system, which has made an important contribution to the economic development of the EU system. Methods: At present, the operation mode of new antidumping algorithm has some defects, such as structure confusion and incomplete system implementation, which affects the development progress of EU economic growth. Results: Based on the above problems, in this paper, particle swarm algorithm is introduced, based on the optimization analysis of the website structure of the new antidumping algorithm, through the independent screening analysis of particle swarm optimization, combining the WTO economy with the EU status theory, Conclusion: the paper obtains the optimized anti-dumping innovation scheme on the basis of particle swarm algorithm analysis, and finally passes the input test. The feasibility of the scheme is established.

Sign in / Sign up

Export Citation Format

Share Document