Adaptive Neural Network Decentralized Stabilization For Nonlinear Large Scale Interconnected Systems With Expanding Construction

Structural changes or reconstructions of large-scale interconnected systems have a major impact on the stability of it. For the stabilization of the expending large scale system, four relevant problems in terms of structural stability control of nonlinear large scale systems are studied in this paper. Specific contents are as follows:1. A decentralized stabilization method is studied for a class of nonlinear large-scale systems with expanding construction. The expending construction of large scale interconnected systems means that there are some new subsystems added in the original construction system during operation. For the stabilization of the expending large scale system, it is more realistic to keep the decentralized control laws of the original subsystems unchanged. And the decentralized control laws of the new subsystems must be designed to stabilize both itself and the resulting expanded nonlinear large scale system. The decentralized control law and the parameter adaptive law of the new stochastic nonlinear subsystem is designed by using RBF neural networks and Backstepping technique. Based on Lyapunov stability theory, the uniform ultimate boundedness of all signals in the closed-loop system is proved. Simulation results show the feasibility and superiority of the proposed method.2. The decentralized stabilization problem is studied for a class of stochastic nonlinear large-scale systems with expanding construction when structure of the systems is reconstructed. According to Lyapunov theory approach, the decentralized control law and the parameter adaptive law of the new stochastic nonlinear subsystem is designed by using RBF neural networks and Backstepping technique, and the uniform ultimate boundedness of all signals in the closed-loop system is proved. Meanwhile, simulation results for the subsystems with different dimensions and control methods are given.3. The decentralized control approach is studied for a class of nonlinear time-delay large-scale systems with expanding construction. According to Lyapunov theory approach, the decentralized control law and the parameter adaptive law of the new nonlinear time-delay subsystem is designed by using RBF neural networks and Backstepping technique, the uniform ultimate boundedness of all signals in the closed-loop system is proved. An illustrative example shows the feasibility.4. The decentralized tracking control approach is studied for a class of stochastic nonlinear large-scale systems with expanding construction. Based on the Razumikhin function, a adaptive neural controller is developed by using the backstepping technique. The proposed adaptive controller guarantees the uniform ultimate boundedness of all signals in the closed-loop system, and the tracking error remains in a neighborhood. The effectiveness of the proposed method is verified by some simulation results.