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针对一类非线性关联大系统在结构扩展时的跟踪控制问题,提出一种采用自适应神经网络的控制方法.该方法要求在不改变原结构系统控制律的前提下设计新加入子系统的控制律和自适应律,使扩展后所有子系统都具有很好的跟踪性能.这里主要利用神经网络的逼近功能以及Backstepping技术来设计自适应律和控制律,通过Lyapunov理论证明在该控制器的作用下闭环系统的所有信号均是有界的,并可使系统准确跟踪.仿真结果验证了所提出方法的有效性.
Aiming at the problem of tracking control of a class of nonlinear large-scale interconnected systems in structure expansion, a control method based on adaptive neural network is proposed, which requires that the control of newly added subsystems be designed without changing the control law of the original structural system Law and adaptive law to make all the subsystems have good tracking performance after extension.It mainly uses the approximation function of neural network and Backstepping technology to design adaptive law and control law and prove the role of the controller in Lyapunov theory All signals of the closed-loop system are bounded, and the system can track accurately.The simulation results verify the effectiveness of the proposed method.