Design of heat pump temperature control system based on particle swarm optimization fuzzy PID

2021 ◽  
Author(s):  
Han Qingqing ◽  
Yang Yi ◽  
Yu Jingya ◽  
Gu Haiqin ◽  
Ren Xiaolin ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Heat Pump ◽  
Temperature Control ◽  
Particle Swarm ◽  
Temperature Control System ◽  
Fuzzy Pid ◽  
Swarm Optimization

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.

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.

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.

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.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

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