scholarly journals Adaptive fuzzy synchronization of uncertain fractional-order chaotic systems with different structures and time-delays

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Xiaoli Qin ◽  
Shenggang Li ◽  
Heng Liu
Author(s):  
Bachir Bourouba

In this chapter a new direct adaptive fuzzy optimal sliding mode control approach is proposed for the stabilization of fractional chaotic systems with different initial conditions of the state under the presence of uncertainties and external disturbances. Using Lyapunov analysis, the direct adaptive fuzzy optimal sliding mode control approach illustrates asymptotic convergence of error to zero as well as good robustness against external disturbances and uncertainties. The authors present a method for optimum tuning of sliding mode control system parameter using particle swarm optimization (PSO) algorithm. PSO is a robust stochastic optimization technique based on the movement and intelligence of swarm, applying the concept of social interaction to problem solving. Simulation examples for the control of nonlinear fractional-order systems are given to illustrate the effectiveness of the proposed fractional adaptive fuzzy control strategy.


Author(s):  
Tsung-Chih Lin ◽  
Chia-Hao Kuo ◽  
Valentina E. Balas

In this paper, in order to achieve tracking performance of uncertain fractional order chaotic systems an adaptive hybrid fuzzy controller is proposed. During the design procedure, a hybrid learning algorithm combining sliding mode control and Lyapunov stability criterion is adopted to tune the free parameters on line by output feedback control law and adaptive law. A weighting factor, which can be adjusted by the trade-off between plant knowledge and control knowledge, is adopted to sum together the control efforts from indirect adaptive fuzzy controller and direct adaptive fuzzy controller. To confirm effectiveness of the proposed control scheme, the fractional order chaotic response system is fully illustrated to track the trajectory generated from the fractional order chaotic drive system. The numerical results show that tracking error and control effort can be made smaller and the proposed hybrid intelligent control structure is more flexible during the design process.


2015 ◽  
Vol 64 (7) ◽  
pp. 070503 ◽  
Author(s):  
Liu Heng ◽  
Li Sheng-Gang ◽  
Sun Ye-Guo ◽  
Wang Hong-Xing

Sign in / Sign up

Export Citation Format

Share Document