The effect of varying inertia weight on Particle Swarm Optimization (PSO) algorithm in optimizing PID controller of temperature control system

2012 ◽  
Author(s):  
Mohd Azri Abdul Aziz ◽  
Mohd Nasir Taib ◽  
Ramli Adnan
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Temperature Control ◽  
Pid Controller ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Temperature Control System ◽  
Swarm Optimization ◽  
Inertia Weight

Particle Swarm Optimization of PIλDμ Control of Heating Furnace Temperature Control System

2014 ◽  
Vol 721 ◽  
pp. 205-209
Author(s):  
Pei Guang Wang ◽  
Lian Zhang ◽  
Xiao Ping Zong
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Fractional Order ◽  
Temperature Control ◽  
Pid Controller ◽  
Dynamic Performance ◽  
Particle Swarm ◽  
Heating Furnace ◽  
Temperature Control System ◽  
Swarm Optimization

Due to the complexity of the heat transfer for heating furnace, some characteristics are caused such as big inertia, great lag. In the temperature control system for heating furnace, the traditional PID controller can not get satisfactory effect, that dynamic is instability and control accuracy is bad, which is very detrimental to the system to achieve optimum efficiency. A fractional order PIλDμ controller based on particle swarm optimization method was designed, at the same time compared with PID control. Simulation results show that, fractional order PIλDμ control based on particle swarm optimization has better convergence stability, faster response times and higher accuracy value. Fractional order PIλDμ controller has better dynamic performance, compared with traditional PID controller, greatly improves the quality control system.

Application of Fuzzy Immune PID Controller Based on Particle Swarm Optimization in Power Plant Steam Temperature Control System

2014 ◽  
Vol 950 ◽  
pp. 257-262 ◽  
Author(s):  
Fei Hu ◽  
Wu Neng Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Power Plant ◽  
Temperature Control ◽  
Pid Control ◽  
Pid Controller ◽  
Particle Swarm ◽  
Temperature Control System ◽  
Steam Temperature ◽  
Swarm Optimization ◽  
Adjustment Time

Power plant steam temperature control has characteristics of long delay and great inertia, a new method is proposed by analyzing above-mentioned problems and existing control methods on this paper. The method consists of an improved particle swarm optimization algorithm and a fuzzy immune PID controller. In addition, simulation results of PID, traditional fuzzy immune PID and fuzzy immune PID based on PSO are presented and compared. Fuzzy immune PID Control based on PSO has advantages of short adjustment time, quicker response time, better anti-interference ability and more stability. It can reduce the fluctuation of power plant steam temperature, and has better control performance and practical value.

PID Control of Main Steam Temperature Used Quantum Particle Swarm Optimization

2012 ◽  
Vol 591-593 ◽  
pp. 1204-1207
Author(s):  
Yan Min Nie ◽  
Tao Wang ◽  
Ying Bo An
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Temperature Control ◽  
Particle Swarm ◽  
Temperature Control System ◽  
Particle Swarm Algorithm ◽  
Steam Temperature ◽  
Swarm Optimization ◽  
Boiler Operation ◽  
Main Steam

The main steam temperature is always an important indicator of the boiler operation quality, high or low will affect the quality of boiler operation. At first, introduce a algorithm PSO, which can used to optimize the PID parameters of a main steam temperature control system. Then, improved the PSO, and studied a kind of improved particle swarm algorithm—quantum apply quantum-behaved particle swarm optimization (QPSO). And this algorithm is used to optimize the PID parameters of a main steam temperature control system, got the best parameters. In the end, simulation result shows that, compared with basic particle swarm optimization (PSO),QPSO can make main steam temperature control system has a better control of quality, and improves the system of static and dynamic characteristics.

A Inflexion Nonlinear Global Particle Swarm Optimization (PSO) Algorithm

2014 ◽  
Vol 989-994 ◽  
pp. 1582-1585
Author(s):  
Li Xia Lv ◽  
Xiang Yu Lin
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Pressure Control ◽  
Temperature Control System ◽  
Swarm Optimization ◽  
Position Information ◽  
Global Average ◽  
Long Time

According to the question of the standard particle swarm optimization (PSO) algorithm is prone to premature and no convergence phenomenon, this paper proposed an algorithm of Inflection nonlinear global PSO. The algorithm introduces nonlinear trigonometric factor and the global average location information in the formula of velocity updating. It take advantage of the convex of the triangle function cause the particles early in the larger velocity search maintain long time and in the later searching with smaller speed maintain long time, use the global average position information make the population can use more information to update their position. The method are applied in optimizing in the parameters of the main steam temperature control system and furnace pressure control system for comparison, the results show that the method in the search speed and precision than standard PSO has significantly improved.

scholarly journals OPTIMIZATION OF PID PARAMETERS BASED ON PARTICLE SWARM OPTIMIZATION FOR BALL AND BEAM SYSTEM

2020 ◽  
Vol 5 (9) ◽  
pp. 59-69
Author(s):  
Haytem Ali ◽  
Abdulgani Albagul ◽  
Alhade Algitta
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Pid Controller ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Trial And Error ◽  
Swarm Optimization ◽  
Beam Control ◽  
Trial And Error Method ◽  
Error Method

This paper introduces the application of an optimization technique, known as Particle Swarm Optimization (PSO) algorithm to the problem of tuning the Proportional-Integral-Derivative (PID) controller for a linearized ball and beam control system. After describing the basic principles of the Particle Swarm Optimization, the proposed method concentrates on finding the optimal solution of PID controller in the cascade control loop of the Ball and Beam Control System. Ball and Beam control system tends to balance a ball on a particular position on the beam as defined by the user. The efficiency of Particle Swarm Optimization algorithm for tuning the controller will be compared with a classical method, Trial and Error method. The comparison is based on the time response performance. The two tuning methods have been developed by simulation study using Matlab\ m-file software. The evaluations show that Evolutionary method Particle Swarm Optimization (PSO) algorithm gives a much better response than trial and error method.

An Improved Particle Swarm Optimization Algorithm with Adaptive Inertia Weights

2019 ◽  
Vol 18 (03) ◽  
pp. 833-866 ◽  
Author(s):  
Mi Li ◽  
Huan Chen ◽  
Xiaodong Wang ◽  
Ning Zhong ◽  
Shengfu Lu
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Local Optimum ◽  
Random Mutation ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Fitness Value ◽  
The One ◽  
Individual Particles

The particle swarm optimization (PSO) algorithm is simple to implement and converges quickly, but it easily falls into a local optimum; on the one hand, it lacks the ability to balance global exploration and local exploitation of the population, and on the other hand, the population lacks diversity. To solve these problems, this paper proposes an improved adaptive inertia weight particle swarm optimization (AIWPSO) algorithm. The AIWPSO algorithm includes two strategies: (1) An inertia weight adjustment method based on the optimal fitness value of individual particles is proposed, so that different particles have different inertia weights. This method increases the diversity of inertia weights and is conducive to balancing the capabilities of global exploration and local exploitation. (2) A mutation threshold is used to determine which particles need to be mutated. This method compensates for the inaccuracy of random mutation, effectively increasing the diversity of the population. To evaluate the performance of the proposed AIWPSO algorithm, benchmark functions are used for testing. The results show that AIWPSO achieves satisfactory results compared with those of other PSO algorithms. This outcome shows that the AIWPSO algorithm is conducive to balancing the abilities of the global exploration and local exploitation of the population, while increasing the diversity of the population, thereby significantly improving the optimization ability of the PSO algorithm.

scholarly journals On the Performance of Linear Decreasing Inertia Weight Particle Swarm Optimization for Global Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Martins Akugbe Arasomwan ◽  
Aderemi Oluyinka Adewumi
Keyword(s):  
Global Optimization ◽  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Search Space ◽  
Terminal Point ◽  
Simulation Experiments ◽  
Swarm Optimization ◽  
Inertia Weight

Linear decreasing inertia weight (LDIW) strategy was introduced to improve on the performance of the original particle swarm optimization (PSO). However, linear decreasing inertia weight PSO (LDIW-PSO) algorithm is known to have the shortcoming of premature convergence in solving complex (multipeak) optimization problems due to lack of enough momentum for particles to do exploitation as the algorithm approaches its terminal point. Researchers have tried to address this shortcoming by modifying LDIW-PSO or proposing new PSO variants. Some of these variants have been claimed to outperform LDIW-PSO. The major goal of this paper is to experimentally establish the fact that LDIW-PSO is very much efficient if its parameters are properly set. First, an experiment was conducted to acquire a percentage value of the search space limits to compute the particle velocity limits in LDIW-PSO based on commonly used benchmark global optimization problems. Second, using the experimentally obtained values, five well-known benchmark optimization problems were used to show the outstanding performance of LDIW-PSO over some of its competitors which have in the past claimed superiority over it. Two other recent PSO variants with different inertia weight strategies were also compared with LDIW-PSO with the latter outperforming both in the simulation experiments conducted.

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