Synchronization of Chaos Systems Using Fuzzy Logic

Ababneh, Nedal

doi:10.3844/jcssp.2011.197.205

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…Uncertain discrete system (1) is asymptotically stabilized by a dynamic controller with output feedback, minimizing an upper born of the quadratic constrained if it exist at each sampling time matrices Z, H∈ ℝ n*n , L ∈ ℝ m*n , F, H∈ ℝ n*q and symmetric positive matrices definite X, Y, P ∈ ℝ n*n solutions of the following optimization problem Eq. 11-15 (Torre and Migliore, 2011;Ababneh et al, 2011) miny (11) Under:…”

Section: Quadratic Functionmentioning

confidence: 99%

Synthesis of Robust Dynamic Controller, Model Predictive Control Based

Sellami¹

2012

American Journal of Applied Sciences

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Problem statement: More advanced control techniques have been developed in recent decades following the great progress of calculations means. Without considering the constraints on system variables, the response of controlled system moves away from the desired response. Hence the control strategy must provide the ability to integrate these constraints in the design phase of the controller. Approach: This study presents a design of robust dynamic controller which was based on the control strategy MPC for an uncertain discrete system described by a multimodel by solving an optimization problem. The Model Predictive Control (MPC) strategy uses a dynamic model of the process in order to predict its future behavior. This control strategy, that we propose, makes it possible to integrate these constraints in the design phase of the controller. The design of the robust dynamic controller must maintain the stability and performance of the system in the presence of uncertainties. The principle of this method was to solve, at each calculation step, a convex optimization problem that calculates the matrices characterizing the dynamic controller. LMI formulation of the constraints, on process variables, was introduced into the design phase of the dynamic controller robust. The optimization study was also implemented in the MATLAB software and simulation studies had been presented. Results: Simulation results had proved the effectiveness of this study. The robust dynamic controller designed for uncertain systems guarantees stability in closed loop systems by integrating constraints on system variables. Conclusion: The described approach explain how to integrate constraints, MPC type, during the phase of design of the robust controller: the simulation concluded on a benchmark proof the powerful of this approach.

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Section: Quadratic Functionmentioning

confidence: 99%