时变时滞非仿射大系统的分散自适应控制

Decentralized adaptive control of large-scale nonaffine time-varying delay systems

针对一类具有未知时变时滞的非仿射互联大系统基于神经网络的逼近能力,提出了一种分散自适应神经网络控制方案。该方案利用中值定理对未知非仿射函数进行分离;利用分离技术和Young's不等式放宽了对未知时滞及时滞互联不确定项的限制,同时大大减少了在线调节参数的数量。此外,利用Lyapunov-Krasovskii泛函补偿了未知时滞带来的不确定性。通过理论分析,证明了闭环系统所有信号是有界的,输出跟踪误差收敛到原点的一个小邻域内。最后,仿真结果验证了所提控制方案的有效性。

Based on the approximation capability of the neural networks,a decentralized adaptive neural network control scheme was proposed for a kind of unknown time-varying delay non-affine interconnected large-scale system.The unknown nonaffine functions were separated by the mean value theorem,while the restrictions of the unknown time delays and the uncertain time-varying delay interconnections were relaxed by utilizing the separation technique and the Young＇s inequality in the design.The number of adjustable parameters was considerably reduced.In addition,time delay uncertainties were compensated by using Lyapunov-Krasovskii functionals.Through the theoretical analysis,all of the signals in the closed-loop system are proven to be bounded,while the output tracking errors converge to a small neighborhood of the origin.The simulation results show the effectiveness of the proposed control scheme.