非线性关联大系统分散自适应滑模控制研究

基于滑模控制原理，针对具有强关联作用并且各子系统内亦有不确定性干扰的非线性大系统的控制进行了研究，提出了一种分散自适应控制策略，根据神经网络逼近理论，用前向神经网络逼近控制增益函数，给出了逼近误差的自适应律，较好地解决了控制增益函数的确定问题，这样在控制算法中，只需要知道系统中各非线性函数的上界即可，最后用Lyapunov方法证明了闭环系统的稳定性，而且跟踪误差收敛到零的一个领域。

Decentralized Sliding Mode Adaptive Controller Design for Large-Scale Interconnected Nonlinear Systems

A decentralized adaptive control methodology is presented based on sliding mode principle for large-scale nonlinear systems with strong interconnection. In the paper a feedforward neural network is used to approximate the control function. The adaptive law of the approximation error is also given. In the control algorithm, only the bounds of the disturbances are needed. By using Lyapunov's stability theory, the global stability of the closed-loop system is proven, and the conclusion that the tracking error can be converged to the neighborhood of the zero is obtained.