scholarly journals Hybrid improved particle swarm optimization-cuckoo search optimized fuzzy PID controller for micro gas turbine

2021 ◽  
Vol 7 ◽  
pp. 5446-5454
Author(s):  
Rui Yang ◽  
Yongbao Liu ◽  
Youhong Yu ◽  
Xing He ◽  
Hongsong Li
Keyword(s):  
Particle Swarm Optimization ◽  
Gas Turbine ◽  
Pid Controller ◽  
Particle Swarm ◽  
Cuckoo Search ◽  
Fuzzy Pid ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Micro Gas Turbine ◽  
Fuzzy Pid Controller

A Hybrid PSO-LEVY Flight Algorithm Based Fuzzy PID Controller for Automatic Generation Control of Multi Area Power Systems

2017 ◽  
Vol 6 (2) ◽  
pp. 42-63 ◽  
Author(s):  
Ajit Kumar Barisal ◽  
Tapas Kumar Panigrahi ◽  
Somanath Mishra
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Pid Controller ◽  
Particle Swarm ◽  
Automatic Generation ◽  
Automatic Generation Control ◽  
Fuzzy Pid ◽  
Swarm Optimization ◽  
Fuzzy Pid Controller ◽  
Generation Control

This article presents a hybrid PSO with Levy flight algorithm (LFPSO) for optimization of the PID controllers and employed in automatic generation control (AGC) of nonlinear power system. The superiority of the proposed LFPSO approach has been demonstrated with comparing to recently published Lozi map-based chaotic optimization algorithm (LCOA) and Particle swarm optimization to solve load-frequency control (LFC) problem. It is found that the proposed LFPSO method has robust dynamic behavior in terms of settling times, overshoots and undershoots by varying the system parameters and loading conditions from their nominal values as well as size and locations of disturbance. Secondly, a three-area thermal power system is considered with nonlinear as Generation Rate Constraints (GRC) and outperforms to the results of Bacteria Foraging algorithm based integral controller as well as hybrid Differential Evolution and Particle Swarm Optimization based fuzzy PID controller for the similar power system. Finally, the proficiency of the proposed controller is also verified by random load patterns.

Active Suspension Control Based on Particle Swarm Optimization

2020 ◽  
Vol 13 (1) ◽  
pp. 60-78
Author(s):  
Shaobin Lv ◽  
Guoqiang Chen ◽  
Jun Dai
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Ride Comfort ◽  
Control Strategies ◽  
Particle Swarm ◽  
Active Suspension ◽  
Fuzzy Pid ◽  
Swarm Optimization ◽  
Fuzzy Pid Controller ◽  
Suspension Control

Background: The active suspension can be adjusted in real time according to the change of road condition and vehicle state to enhance the performance of active suspension that has received widespread attention. Suspension control strategies and actuators are the key issues of the active suspension, and are the main research directions for active suspension patents. Objective: The numerical analysis method is proposed to study the performance characteristics of the active suspension controlled by different controllers. Methods: The active suspension control model and control strategy based on particle swarm optimization are established, and two active suspensions controlled by the sliding mode controller and the fuzzy PID controller are proposed. Moreover, two active suspension systems are optimized by particle swarm optimization. Results: The results of the analysis show that the performance of the active suspension is significantly improved compared with the passive suspension when the vehicle runs on the same road. The ride comfort of the active suspension controlled by the fuzzy PID controller has the best adaptive performance when the vehicle runs on different grade roads or white noise roads. The active suspension controlled by the fuzzy PID controller has the best ride comfort. Conclusion: A good control strategy can effectively improve the performance of the active suspension. To improve the performance of the active suspension, it can be controlled by utilizing different control strategies. The results lay a foundation for the active suspension experiments, the dynamic analysis and the optimization design of suspension structure.

scholarly journals Anti-synchronization of discrete-time chaotic systems using optimization algorithms

2011 ◽  
pp. 143-147
Author(s):  
Mahdieh Adeli ◽  
Hassan Zarabadipoor
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Control ◽  
Pid Controller ◽  
Chaotic Systems ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Modified Particle Swarm Optimization

In this paper, anti-synchronization of discrete chaotic system based on optimization algorithms are investigated. Different controllers have been used for anti-synchronization of two identical discrete chaotic systems. A proportional-integral-derivative (PID) control is used and its parameters is tuned by the four optimization algorithms, such as genetic algorithm (GA), particle swarm optimization (PSO), modified particle swarm optimization (MPSO) and improved particle swarm optimization (IPSO). Simulation results of these optimization methods to determine the PID controller parameters to anti-synchronization of two chaotic systems are compared. Numerical results show that the improved particle swarm optimization has the best result.

Parameter Optimization of PID Controller Based on an Improved Particle Swarm Optimization for the Induction Motor

2011 ◽  
Vol 130-134 ◽  
pp. 1938-1942
Author(s):  
Xia Bo Shi ◽  
Wei Xing Lin
Keyword(s):  
Particle Swarm Optimization ◽  
Induction Motor ◽  
Parameter Optimization ◽  
Pid Controller ◽  
Dynamic Performance ◽  
Particle Swarm ◽  
Motor Speed ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Ipso Algorithm

This paper presents a new approach of PID parameter optimization for the induction motor speed system by using an improved particle swarm optimization (IPSO). The induction motor speed is changed by the stator voltage controlled with PID controller. The performance of PID controller based on IPSO is compared to Linearly Decreasing Inertia Weight (LIWPSO). Simulation results demonstrate that the IPSO algorithm has better dynamic performance, higher accuracy and faster convergence and good performance for the PID controller.

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