Adaptive Tracking Control for Uncertain Chaotic Systems with Mixed Parameters

In this paper, a learning control approach is applied to the synchronisation tracking of chaotic systems with unknown periodically time-varying parameters. Using the Lyapunov-Krasovskii functional stability theory, a differential-difference mixed parametric learning law and an adaptive learning control law are constructed to ensure the asymptotic convergence of the extended tracking error in the sense of square error norm. Also, a sufficient condition of the convergence of the method is given. A simulation example illustrates the effectiveness and the feasibility of the proposed scheme.