Abstract
This article presents a synchronization control method based on poles’ placement, disturbances, and uncertainty estimation (DUE) for a pair of Takagi-Sugeno fuzzy systems. First, a 3-D chaotic system was completely converted into a Takagi-Sugeno (T-S) fuzzy model by applying the nonlinearity sector method, which consists of if-then rules and sub-linear systems. Second, two identical T-S fuzzy systems with different initial conditions were synchronized by applying the linear matrix inequality (LMI) to place the eigenvalues of the state error equations in the stable region. Third, the sum of the time-varying disturbances and uncertainties of two nonidentical T-S fuzzy systems were deleted by a disturbance and uncertainty estimation. The given output signals confirmed that the proposed method is suitable and ideal for synchronizing T-S fuzzy systems. The ideas of control theory were implemented by using two experimental scenarios in MATLAB Simulink for two computers connected via an internet router and an electronics circuit’s communication.
Highlights
In recent years, industrial production has been rapidly growing to adapt to the 4.0 industry revolution requirements, and the database is one of the most important keys to the success of this revolution’s
This paper presented the control synchronization of two nonidentical chaotic systems, which were converted to a new form of the T-S fuzzy system
The control synchronization was based on the linear matrix inequality convex optimization method and a time-varying disturbance observer
Summary
Industrial production has been rapidly growing to adapt to the 4.0 industry revolution requirements, and the database is one of the most important keys to the success of this revolution’s. V-N Giap et al.: Disturbance Observer Based LMI for the Synchronization of Takagi-Sugeno Fuzzy Chaotic Systems communication via a local network. Disturbance and uncertainty are complication of the chaotic system, and they were deleted mostly by applying the time-varying disturbance observer in this paper. In [3], the control method is simple, which leads to larger synchronization-error rise times and overshooting than our paper These papers dealt with continuous-bounded functions to cope with disturbances and uncertainty, with the assumptions of a timevarying function and a stratified Lipschitz function. The disturbance observer for system (1) is difficult to complete Because of this weak point, system (1) could be represented by a T-S fuzzy model, which is achieved by applying the nonlinearity sector method to obtain the system model in the fuzzy rules and linear subsystems.
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