scholarly journals Finite-time Projective Synchronization for Semi-markovian Neural Networks Systems via Event-triggered Sliding Mode Control

2021 ◽  
Author(s):  
Junchao Ren ◽  
xuejiao Li
Keyword(s):  
Neural Networks ◽  
Sliding Mode Control ◽  
Finite Time ◽  
Sliding Mode ◽  
Projective Synchronization ◽  
Linear Matrix ◽  
Markov Jump ◽  
Mode Control ◽  
Markov Jump System ◽  
Event Triggered

Abstract This paper investigates the projection synchronization problem of stochastic neural networked systems based on event-triggered sliding mode control (SMC) covering a finite-time period. For improve transmission efficiency and save network resources, a related event-triggered scheme is proposed for the error system, which can identify whether the measurement error should be transmitted to the controller. For finite-time projective synchronization under given event-triggered mechanism, a semi-Markov jump system model is proposed. Secondly, by creating Lyapunov Krasovsky functional and using linear matrix inequality (LMI) technology, as well as considering a proper sliding surface, a sliding mode controller is designed to implement finite-time projection synchronization of different neural networks. Finally, numerical simulations are exploited to illustrate the effectiveness of the main results.

Robust chattering-free optimal sliding-mode control using recurrent neural networks: An H∞-based approach

2019 ◽  
Vol 41 (13) ◽  
pp. 3565-3580 ◽  
Author(s):  
Hamid Toshani ◽  
Mohammad Farrokhi
Keyword(s):  
Neural Networks ◽  
Sliding Mode Control ◽  
Recurrent Neural Networks ◽  
Sliding Mode ◽  
Linear Matrix ◽  
Control Law ◽  
Tracking Accuracy ◽  
Mode Control ◽  
Chattering Free ◽  
Feedback Error

In this paper, a robust and chattering-free sliding-mode control strategy using recurrent neural networks (RNNs) and H∞ approach for a class of nonlinear systems with uncertainties is proposed. The dynamic and algebraic models of the RNN are extracted based on the nominal model of the system and formulation of a quadratic programming problem. For tuning the parameters of the sliding surface, the performance index and the switching coefficient, a robust approach based on the H∞ method is developed. To this end, the control law is divided into two parts: (1) the main term, which includes the feedback error and (2) other terms, which include the network states, the reference input and its derivatives and the effects of the uncertainties. The feedback error gain is tuned by solving a linear matrix inequality. The neural optimizer determines the sliding-mode control law without being directly affected by the uncertainties. By applying the proposed method to the continuous-stirred reactor tank and the inverted pendulum problems, the performance of the proposed controller has been evaluated in terms of the tracking accuracy, elimination of the chattering, robustness against the uncertainties and feasibility of the control signals. Moreover, the results are compared with the conventional and twisting sliding-mode control methods.

Finite-time H∞ sliding mode control of uncertain T-S fuzzy system with time-varying delay based on observer

2021 ◽  
Vol 40 (1) ◽  
pp. 983-999
Author(s):  
Huan Li ◽  
Pengyi Tang ◽  
Yuechao Ma
Keyword(s):  
Sliding Mode Control ◽  
Finite Time ◽  
Sliding Mode ◽  
Linear Matrix ◽  
Time Interval ◽  
Sliding Mode Controller ◽  
Time Varying ◽  
Sliding Surface ◽  
State Estimator ◽  
Mode Control

In this paper, a class of observer-based sliding mode controller is designed, and the finite-time H∞ control problem of uncertain T-S fuzzy systems with time-varying is studied. Firstly, an integral-type sliding surface function with time-delay is devised based on the state estimator, and sufficient criteria of finite-time bounded and finite-time H∞ bounded can be obtained for the T-S systems. Moreover, the proposed sliding mode control law is integrated to ensure the dynamics of controlled system into the sliding surface in a finite-time interval. Then, according to the linear matrix inequalities (LMIs), the desired gain matrices of fuzzy sliding mode controller and state estimator are derived. Finally, effectiveness gives some illustrative examples may be used to display the value of the current proposed method as well as a significant improvement.

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