A new control system design for a small hydro-power plant based on particle swarm optimization-fuzzy sliding mode controller with Kalman estimator

2011 ◽  
Vol 34 (4) ◽  
pp. 388-400 ◽  
Author(s):  
A Zargari ◽  
R Hooshmand ◽  
M Ataei

One of the main problems in small hydro-power plants that are locally used is their frequency control system. In this paper, a suggested control system based on the fuzzy sliding mode controller is presented for controlling the network frequency. Also, the proposed control strategy is compared with a PI controller and conventional sliding mode controller. In order to regulate the membership functions of fuzzy system more accurately, the particle swarm optimization algorithm is also applied. Moreover, because of unavailability of the control system variables, an estimator is suggested for estimating and identifying the system variables. This estimator will reduce the costs of implementing the control method. The simulation results show the ability of controller system in controlling the local network frequency in the presence of load and parameter’s variations.

2020 ◽  
Vol 14 ◽  
Author(s):  
Gang Liu ◽  
Dong Qiu ◽  
Xiuru Wang ◽  
Ke Zhang ◽  
Huafeng Huang ◽  
...  

Background: The PWM Boost converter is a strongly nonlinear discrete system, especially when the input voltage or load varies widely, therefore, tuning the control parameters of which is a challenge work. Objective: In order to overcome the issues, particle swarm optimization (PSO) is employed for tuning the parameters of a sliding mode controller of a boost converter. Methods: Based on the analysis of the Boost converter model and its non-linear characteristics, a mathematic model of a boost converter with a sliding mode controller is built firstly. Then, the parameters of the Boost controller are adjusted based on the integrated time and absolute error (ITAE), integral square error (ISE) and integrated absolute error (IAE) indexes by PSO. Results: Simulation verification was performed, and the results show that the controllers tuned by the three indexes all have excellent robust stability. Conclusion: The controllers tuned by ITAE and ISE indexes have excellent steady-state performance, but the overshoot is large during the startup. The controller tuned by IAE index has better startup performance and slightly worse steady-state performance.


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