Chaos synchronization based on unknown input proportional multiple-integral fuzzy observer

This paper presents an unknown input Proportional Multiple-Integral Observer (PIO) for synchronization of chaotic systems based on Takagi-Sugeno (TS) fuzzy chaotic models subject to unmeasurable decision variables and unknown input. In a secure communication configuration, this unknown input is regarded as a message encoded in the chaotic system and recovered by the proposed PIO. Both states and outputs of the fuzzy chaotic models are subject to polynomial unknown input with kth derivative zero. Using Lyapunov stability theory, sufficient design conditions for synchronization are proposed. The PIO gains matrices are obtained by resolving linear matrix inequalities (LMIs) constraints. Simulation results show through two TS fuzzy chaotic models the validity of the proposed metho

Chaos synchronization based on unknown input proportional multiple-integral fuzzy observer

This paper presents an unknown input Proportional Multiple-Integral Observer (PIO) for synchronization of chaotic systems based on Takagi-Sugeno (TS) fuzzy chaotic models subject to unmeasurable decision variables and unknown input. In a secure communication configuration, this unknown input is regarded as a message encoded in the chaotic system and recovered by the proposed PIO. Both states and outputs of the fuzzy chaotic models are subject to polynomial unknown input with kth derivative zero. Using Lyapunov stability theory, sufficient design conditions for synchronization are proposed. The PIO gains matrices are obtained by resolving linear matrix inequalities (LMIs) constraints. Simulation results show through two TS fuzzy chaotic models the validity of the proposed method

Une approche améliorée pour l'optimisation des contrôleurs flous

In this paper, an improved adaptation mechanism for tuning of fuzzy logic controllers using gradient descent method is proposed. The proposed algorithm is used for input/output membership functions tuning of a fuzzy controller by minimising some criterion on the control output. The optimisation problem is solved using gradient descent technique. In this tuning procedure, the constant which controls how much the fuzzy controller parameters are altered at each iteration is updated using a fuzzy logic approximate reasoning modelled as a set of IF-THEN rules. To illustrate the usefulness and the effectiveness of the improved algorithm, we consider the problem of minimising the matching error induced by an additive noise affecting the input information of a Pi-like fuzzy controlle

Implementation of a Fuzzy Logic System to Tune a PI Controller Applied to an Induction Motor

The simplicity of traditional regulators makes them popular and the most used solution in the nowadays industry. However, they suffer from some limitations and cannot deal with nonlinear dynamics and system parameters variation. In the literature, several strategies of adaptation are developed to alleviate these limitations. In this paper, we propose a combination of two strategies for PI parameters supervision and adaptation. We apply the obtained structure to the control of induction machine speed. Simulation and experimental results of the proposed schema show good performances as compared to two strategies