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.

Forecasting by designing Mamdani general type-2 fuzzy logic systems optimized with quantum particle swarm optimization algorithms

2019 ◽  
Vol 41 (10) ◽  
pp. 2886-2896 ◽  
Author(s):  
Yang Chen ◽  
Dazhi Wang
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
General Type ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Fuzzy Logic Systems ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization ◽  
Logic Systems

Much more attention has been focused on studying and applying general type-2 fuzzy logic systems (GT2 FLSs) in recent years. The paper designs a type of Mamdani GT2 FLS for studying forecasting problems based on the data of permanent magnetic drive (PMD) loss. During the system design process, we choose the primary membership functions (MFs) of antecedent, consequent and input measurement general type-2 fuzzy sets (GT2 FSs) as Gaussian type MFs with uncertain standard deviations. The corresponding vertical slices (secondary MFs) are chosen as the triangle MFs. All the parameters of Mamdani GT2 FLSs are optimized by the quantum particle swarm optimization (QPSO) algorithms. Noisy data of PMD loss are adopted for both training and testing the proposed FLSs forecasting approaches. Simulation studies and convergence analysis are employed to show the effectiveness and feasibility of the proposed GT2 FLSs forecasting methods compared with their T1 and IT2 counterparts.

Self-Tuning Robust Fuzzy Controller Design Based on Multi-Objective Particle Swarm Optimization Adaptation Mechanism

2017 ◽  
Vol 139 (7) ◽  
Author(s):  
Edson B. M. Costa ◽  
Ginalber L. O. Serra
Keyword(s):  
Particle Swarm Optimization ◽  
Fuzzy Controller ◽  
Controller Design ◽  
Particle Swarm ◽  
Parameters Estimation ◽  
Recursive Least Squares ◽  
Adaptation Mechanism ◽  
Swarm Optimization ◽  
Adaptive Fuzzy ◽  
Multi Objective

In this paper, an adaptive fuzzy controller design methodology via multi-objective particle swarm optimization (MOPSO) based on robust stability criterion is proposed. The plant to be controlled is modeled from its input–output experimental data considering a Takagi–Sugeno (TS) fuzzy nonlinear autoregressive with exogenous input model, by using the fuzzy C-means clustering algorithm (antecedent parameters estimation) and the weighted recursive least squares (WRLS) algorithm (consequent parameters estimation). An adaptation mechanism as MOPSO problem for online tuning of a fuzzy model based digital proportional-integral-derivative (PID) controller parameters, based on the gain and phase margins specifications, is formulated. Experimental results for adaptive fuzzy digital PID control of a thermal plant with time-varying delay are presented to illustrate the efficiency and applicability of the proposed methodology.

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