A combined static output feedback-PID control for TITO process based particle swarm optimization: simulation and practical implementation for the poultry house system

2022 ◽  
Author(s):  
Ilyas Lahlouh ◽  
Fathallah Rerhrhaye ◽  
Ahmed Elakkary ◽  
Nacer Sefiani ◽  
Abdelmajid Bybi
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Control ◽  
Output Feedback ◽  
Particle Swarm ◽  
Practical Implementation ◽  
Static Output Feedback ◽  
Swarm Optimization ◽  
Poultry House ◽  
Optimization Simulation ◽  
Static Output

scholarly journals Position Control of Ultrasonic Motor Using IMC-PID Combined with Tribes Type NN Algorithm

2011 ◽  
Vol 2-3 ◽  
pp. 12-17
Author(s):  
Sheng Lin Mu ◽  
Kanya Tanaka
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Pid Control ◽  
Position Control ◽  
Back Propagation ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Ultrasonic Motor ◽  
Swarm Optimization

In this paper, we propose a novel scheme of IMC-PID control combined with a tribes type neural network (NN) for the position control of ultrasonic motor (USM). In this method, the NN controller is employed for tuning the parameter in IMC-PID control. The weights of NN are designed to be updated by the tribes-particle swarm optimization (PSO) algorithm. This method makes it possible to compensate for the characteristic changes and nonlinearity of USM. The parameter-free tribes-PSO requires no information about the USM beforehand; hence its application overcomes the problem of Jacobian estimation in the conventional back propagation (BP) method of NN. The effectiveness of the proposed method is confirmed by experiments.

Particle Swarm Optimization for the design of H∞ static output feedbacks

2011 ◽  
Vol 44 (1) ◽  
pp. 12581-12586 ◽  
Author(s):  
Mohamed Yagoubi ◽  
Guillaume Sandou
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Static Output

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.

Particle swarm optimization-tuned adaptive fuzzy output feedback motion tracking control of piezo-positioning mechanism with unknown hysteresis

2019 ◽  
Vol 41 (10) ◽  
pp. 2897-2908 ◽  
Author(s):  
Mohsen Hasanpour Naseriyeh ◽  
Adeleh Arabzadeh Jafari ◽  
Mehrnoosh Zaeifi ◽  
Seyed Mohammad Ali Mohammadi
Keyword(s):  
Particle Swarm Optimization ◽  
Output Feedback ◽  
Motion Tracking ◽  
Tracking Error ◽  
Particle Swarm ◽  
Small Neighborhood ◽  
Output Feedback Control ◽  
Fuzzy Logic Systems ◽  
Swarm Optimization ◽  
Adaptive Fuzzy

This paper considers the problem of observer-based adaptive fuzzy output feedback control for a piezo-positioning mechanism with unknown hysteresis. In this paper, fuzzy logic systems (FLSs) are used to estimate the unknown nonlinear functions, and also Nussbaum function is utilized to overcome the unknown direction hysteresis. Based on the Lyapunov method, the control scheme is constructed by using the backstepping and adaptive technique. In order to better control performance in reducing tracking error, the particle swarm optimization (PSO) algorithm is utilized for tuning the controller parameters. Proposed adaptive controller guarantees that all the closed-loop signals are semiglobally uniformly ultimately bounded (SGUUB) and the tracking error can converge to a small neighborhood of the origin. Finally, the simulation results are provided to demonstrate the effectiveness and robustness of the proposed approach.

scholarly journals Particle Swarm Optimization Simulation via Optimal Halton Sequences

2016 ◽  
Vol 80 ◽  
pp. 772-781 ◽  
Author(s):  
Ganesha Weerasinghe ◽  
Hongmei Chi ◽  
Yanzhao Cao
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Optimization Simulation ◽  
Halton Sequences
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