Suppressing vibration in a functionally graded material plate using genetic algorithm particle swarm optimization and sliding mode control system

2014 ◽  
Vol 230 (2) ◽  
pp. 120-133 ◽  
Author(s):  
Jalal Javadi Moghaddam ◽  
Ahmad Bagheri
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Sliding Mode Control ◽  
Functionally Graded Material ◽  
Sliding Mode ◽  
Particle Swarm ◽  
Functionally Graded ◽  
Swarm Optimization ◽  
Graded Material

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
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Sliding Mode ◽  
Particle Swarm ◽  
Local Network ◽  
Sliding Mode Controller ◽  
Swarm Optimization ◽  
Hydro Power ◽  
Fuzzy Sliding Mode ◽  
System Variables

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.

scholarly journals Nonsingular Terminal Sliding Mode Control Based on Binary Particle Swarm Optimization for DC–AC Converters

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2099 ◽  
Author(s):  
En-Chih Chang ◽  
Chun-An Cheng ◽  
Lung-Sheng Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Particle Swarm ◽  
Binary Particle Swarm Optimization ◽  
Terminal Sliding Mode Control ◽  
Terminal Sliding Mode ◽  
Swarm Optimization ◽  
Nonsingular Terminal Sliding Mode ◽  
Ac Converters

This paper proposes an improved feedback algorithm by binary particle swarm optimization (BPSO)-based nonsingular terminal sliding mode control (NTSMC) for DC–AC converters. The NTSMC can create limited system state convergence time and allow singularity avoidance. The BPSO is capable of finding the global best solution in real-world application, thus optimizing NTSMC parameters during digital implementation. The association of NTSMC and BPSO extends the design of classical terminal sliding mode to converge to non-singular points more quickly and introduce optimal methodology to avoid falling into local extremum and low convergence precision. Simulation results show that the improved technique can achieve low total harmonic distortion (THD) and fast transients with both plant parameter variations and sudden step load changes. Experimental results of a DC–AC converter prototype controlled by an algorithm based on digital signal processing have been shown to confirm mathematical analysis and enhanced performance under transient and steady-state load conditions. Since the improved DC–AC converter system has significant advantages in tracking accuracy and solution quality over classical terminal sliding mode DC–AC converter systems, this paper will be applicable to designers of relevant robust control and optimal control technique.

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