Quantum genetic algorithm-based memory state feedback control for T–S fuzzy system

Abstract

In this paper, the authors utilize a linear matrix inequality (LMI) technique for designing a quantum genetic algorithm (QGA)-based memory state feedback control of a nonlinear system. The performance of the proposed model is enhanced using the QGA-based algorithm for finding the control gain matrices as a searching tool. To evaluate the fitness function of QGA, the LMI problem is formulated as a constrained optimization. The more general Lyapunov–Krasovskii (LKFs) functional is selected to analyze the closed-loop system stability and the criterion for its asymptotic stability. Numerical examples are provided to verify the effectiveness of the QGA-based proposed control scheme.