LAUSR

WNCS (Whole network control system) is a network-based distributed control system. The control loop formed by the serial network usually includes several subcontrol systems. WNCS optimal control is a complex and multiparameter coupled highly nonlinear system. Combining the advantages of GA (genetic algorithm), neural network, and fuzzy control, a WNCS optimal control method based on improved GA is proposed. This scheme has both the strong global searching ability of GA and the robustness and self-learning ability of neural network. The simulation results show that the algorithm can keep the diversity of population genes and effectively restrain the premature convergence of the algorithm. On this basis, the optimal control problem of WNCS with short time delay with information integrity scale is studied. The model transformation is used to transform the long time-delay system into a formal nondelay nonlinear system, and then the transformed nondelay nonlinear system obtains the optimal control law that meets the infinite time-domain quadratic performance index without considering packet loss by successive approximation method. The simulation results verify the effectiveness and correctness of the compensation algorithm for nonlinear WNCS.