非线性时变系统自适应backstepping学习控制

针对含有混合未知参数的高阶非线性系统,利用backstepping方法,提出了一种自适应重复学习控制方法,该方法与分段积分机制相结合,可以处理时变参数在一个未知紧集内周期性快时变的非线性系统,通过构造微分-差分参数自适应律,设计了一种自适应控制策略,使跟踪误差在误差平方范数意义下渐近收敛于零,利用Lyapunov泛函,给出了闭环系统收敛的一个充分条件.实例仿真结果说明了该方法的有效性.     

Generalized projective synchronization of chaotic systems via adaptive learning control

In this paper, a learning control approach is applied to the generalized projective synchronisation (GPS) of different 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 make the states of two different chaotic systems asymptotically synchronised. The scheme is successfully applied to the generalized projective synchronisation between the Lorenz system and Chen system. Moreover, numerical simulations results are used to verify the effectiveness of the proposed scheme.     

离散双线性系统二次型最优控制的迭代算法

1引言众所周知,某些复杂的工业过程用线性近似动态模型是不适宜的,而且许多对象如工业生产,生态,生物和社会经济等过程可以用双线性模型自然描述,能够在稳态工作点的一个较大领域内描述一大类严重非线性系统的动态特性,描述精度优于传统的线性模型