基于积分滑模控制的非理想变时滞 神经网络有限时间同步

研究一类非理想变时滞神经网络的有限时间同步问题.首先,利用驱动-响应概念推导误差系统,并运用同步误差构造一个合适的积分滑模流型,若误差系统的状态轨迹在有限时间内到达滑模面,则同步误差将随其后在有限时间内收敛于零.然后,结合神经元激活函数的约束条件,设计一种合适的滑模控制器,根据所设计的控制器和Lyapunov稳定性理论,误差系统的状态轨迹能够在有限时间内到达滑模面,从而非理想变时滞神经网络的有限时间同步能够实现.最后,通过数值仿真结果验证所提出设计方法的有效性.

Finite-time synchronization of nonidentical neural networks with time-varying delay based on integral sliding mode control

The finite-time synchronization problem of a class of nonidentical neural networks with time-varying delay is studied. Firstly, by using the drive-response concept to derive an error system, a suitable integral sliding mode manifold is constructed by applying the synchronization error. If the state trajectories of the error system are driven onto the sliding mode surface, the synchronization error will thereafter converge to zero in finite time. Then, by combining the bounded conditions on neuron activation functions, a proper sliding mode controller is designed. Based on the designed controller and the Lyapunov stability theory, the state trajectories of the error system can be driven onto the sliding mode surface, such that the finite-time synchronization of nonidentical neural networks with time-varying delay can be performed. Finally, numerical simulation results verify the effectiveness of the proposed method.