In this paper, we deal with the vibration control problem of a flexible spacecraft system with unknown external disturbance and uncertain input backlash nonlinearity. The considered system is described by two partial differential equations and an ordinary differential equation as governing equations, and by ordinary differential equations as boundary conditions. The backlash nonlinearity is reformulated into the desired control input associated with an extra input nonlinear error. This input error and the external disturbance are combined into an unknown ''disturbance-like'' item. Two boundary control inputs are designed at the center body of the spacecraft, compensating for the unknown upper-bound of such items by applying proper online updating laws. As a result, the vibration of both solar panels of the flexible spacecraft is suppressed and their angle positions are regulated in the desired region. The numerical simulations are provided to verify the control performance of the proposed controls by the choice of proper parameters.INDEX TERMS Vibration control, adaptive control, flexible satellite, input backlash.
Traffic congestion is a common problem in many countries, especially in big cities. At present, China’s urban road traffic accidents occur frequently, the occurrence frequency is high, the accident causes traffic congestion, and accidents cause traffic congestion and vice versa. The occurrence of traffic accidents usually leads to the reduction of road traffic capacity and the formation of traffic bottlenecks, causing the traffic congestion. In this paper, the formation and propagation of traffic congestion are simulated by using the improved medium traffic model, and the control strategy of congestion dissipation is studied. From the point of view of quantitative traffic congestion, the paper provides the fact that the simulation platform of urban traffic integration is constructed, and a feasible data analysis, learning, and parameter calibration method based on RBF neural network is proposed, which is used to determine the corresponding decision support system. The simulation results prove that the control strategy proposed in this paper is effective and feasible. According to the temporal and spatial evolution of the paper, we can see that the network has been improved on the whole.
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