scholarly journals Observer-Based H ∞ Fuzzy Synchronization and Output Tracking Control of Time-Varying Delayed Chaotic Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Kuan-Yi Lin ◽  
Tung-Sheng Chiang ◽  
Chian-Song Chiu ◽  
Wen-Fong Hu ◽  
Peter Liu

Tracking control for the output using an observer-based H ∞ fuzzy synchronization of time-varying delayed discrete- and continuous-time chaotic systems is proposed in this paper. First, from a practical point of view, the chaotic systems here consider the influence of time-varying delays, disturbances, and immeasurable states. Then, to facilitate a uniform control design approach for both discrete- and continuous-time chaotic systems, the dynamic models along with time-varying delays and disturbances are reformulated using the T-S (Takagi–Sugeno) fuzzy representation. For control design considering immeasurable states, a fuzzy observer achieves master-slave synchronization. Third, combining both a fuzzy observer for state estimation and a controller (solved from generalized kinematic constraints) output tracking can be achieved. To make the design more practical, we also consider differences of antecedent variables between the plant, observer, and controller. Finally, using Lyapunov’s stability approach, the results are sufficient conditions represented as LMIs (linear matrix inequalities). The contributions of the method proposed are threefold: (i) systemic and unified problem formulation of master-slave synchronization and tracking control for both discrete and continuous chaotic systems; (ii) practical consideration of time-varying delay, immeasurable state, different antecedent variables (of plant, observer, and controller), and disturbance in the control problem; and (iii) sufficient conditions from Lyapunov’s stability analysis represented as LMIs which are numerically solvable observer and controller gains from LMIs. We carry out numerical simulations on a chaotic three-dimensional discrete-time system and continuous-time Chua’s circuit. Satisfactory numerical results further show the validity of the theoretical derivations.

2020 ◽  
Vol 4 (2) ◽  
pp. 355-360 ◽  
Author(s):  
Amol Yerudkar ◽  
Carmen Del Vecchio ◽  
Luigi Glielmo

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Feng Zhou ◽  
Peidong Zhu ◽  
Minghua Xie ◽  
Jun Wu ◽  
Lihua Cao

A two-stage scheduling robust predictive control (RPC) algorithm, which is based on the time-varying coefficient information of the state-dependent ARX (SD-ARX) model, is designed for the output tracking control of a class of nonlinear systems. First, by using the parameter variation range information of the SD-ARX, a strategy for constructing the system’s polytopic model is designed. To further reduce the conservativeness of the convex polytopic sets which are designed to wrap the system’s future dynamics, the variation range information of the SD-ARX model’s parameters is also considered and compressed. In this method, the polytopic state-space model of the system is constructed directly based on the special structure of the SD-ARX model itself, and there is no need to make such assumption that the bounds on the parameter’s variation range in the system model are known or measurable. And then, a two-stage scheduling RPC algorithm is designed for the output tracking control. A numerical example is presented to demonstrate the effectiveness of the proposed RPC strategy.


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