Stabilization of a class of fractional-order systems with both model uncertainty and external disturbance

Author(s):  
Runlong Peng ◽  
Zuosheng Sun ◽  
Rongwei Guo
Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 877
Author(s):  
Rongwei Guo ◽  
Yaru Zhang ◽  
Cuimei Jiang

This paper is concerned with complete synchronization of fractional-order chaotic systems with both model uncertainty and external disturbance. Firstly, we propose a new dynamic feedback control method for complete synchronization of fractional-order nominal systems (without both uncertainty and disturbance). Then, a new uncertainty and disturbance estimator (UDE)-based dynamic feedback control method for the fractional-order systems with both uncertainty and disturbance is presented, by which the synchronization problem of such fractional-order chaotic systems is realized. Finally, the fractional-order Lorenz system is used to demonstrate the practicability of the proposed results.


2017 ◽  
Vol 40 (6) ◽  
pp. 1808-1818 ◽  
Author(s):  
Ehsan Ghotb Razmjou ◽  
Seyed Kamal Hosseini Sani ◽  
Jalil Sadati

This paper develops a novel controller called adaptive iterative learning sliding mode (AILSM) to control linear and nonlinear fractional-order systems. This controller applies a hybrid structure of adaptive and iterative learning control in to sliding mode method. It can switch between both adaptive and iterative learning control in order to use the advantages of both controllers simultaneously and therefore achieve better control performance. This controller is designed in a way to be robust against the external disturbance. It also estimates unknown parameters of fractional-order system. The proposed controller, unlike the conventional iterative learning control, does not need to apply direct control input to output of the system. It is shown that the controller performs well in partial and complete observable conditions. Illustrative examples verify the performance of the proposed control in presence of unknown disturbances and model uncertainties.


2021 ◽  
Author(s):  
Chao Cheng ◽  
Huanqing Wang ◽  
Haikuo Shen ◽  
Peter Xiaoping Liu

Abstract This article addresses the tracking control problem of uncertain fractional-order nonlinear systems in the presence of input quantization and external disturbance by combining with radial basis function(RBF) neural networks(NNs), fractional-order disturbance observer(FODO) and backstepping method. The unknown nonlinearities of fractional-order systems is approximated by RBF NNs. The design of hysteretic quantizer achieves quantification of input signal and avoids chattering. The FODO is utilized to evaluate the external disturbance exist in fractional-order systems. According to fractioanlorder Lyapunov stability analysis, the bounds of all the signals in the closedloop system is proved. The effectiveness of the proposed method is confirmed by the simulation results.


2008 ◽  
Vol 42 (6-8) ◽  
pp. 825-838 ◽  
Author(s):  
Saïd Guermah ◽  
Saïd Djennoune ◽  
Maâmar Bettayeb

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Aziz Khan ◽  
Hashim M. Alshehri ◽  
J. F. Gómez-Aguilar ◽  
Zareen A. Khan ◽  
G. Fernández-Anaya

AbstractThis paper is about to formulate a design of predator–prey model with constant and time fractional variable order. The predator and prey act as agents in an ecosystem in this simulation. We focus on a time fractional order Atangana–Baleanu operator in the sense of Liouville–Caputo. Due to the nonlocality of the method, the predator–prey model is generated by using another FO derivative developed as a kernel based on the generalized Mittag-Leffler function. Two fractional-order systems are assumed, with and without delay. For the numerical solution of the models, we not only employ the Adams–Bashforth–Moulton method but also explore the existence and uniqueness of these schemes. We use the fixed point theorem which is useful in describing the existence of a new approach with a particular set of solutions. For the illustration, several numerical examples are added to the paper to show the effectiveness of the numerical method.


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