基于自适应重复学习的不确定多涡卷混沌系统同步控制

基于滞环函数提出一种参数可调的多涡卷混沌系统构造方法. 针对复杂不确定性系统, 综合利用自适应神经网络和重复学习控制方法设计一种自适应重复学习同步控制器; 利用自适应重复学习控制方法对周期时变参数化不确定性进行处理; 对函数型不确定性利用神经网络逼近技术进行补偿; 设计鲁棒学习项对神经网络逼近误差和扰动上界进行估计; 通过构造类Lyapunov 复合能量函数证明了同步误差学习的收敛性. 仿真结果验证了所提出方法的有效性.

Adaptive repetitive learning-based synchronization control of uncertain multi-scroll chaotic systems

Based on hysteresis functions, a kind of multi-scroll chaos systems constructing method is proposed, parameters of which can be adjusted. For a class of chaotic systems with complicated uncertainties, a kind of adaptive repetitive learning synchronization controller is presented by combining the adaptive neural network method and the repetitive learning scheme. The difficulty from periodic time-varying parametric uncertainties are overcomed by using the adaptive repetitive learning method scheme, while the function uncertainties are compensated by using the neural approximation technique. The robust learning term is designed to estimate the upper bounds of neural approximation error and the disturbance. A Lyapunov- like function is constructed to prove the convergence of synchronization errors. Simulation results show the effectiveness of the proposed adaptive repetitive learning synchronization scheme.