In this paper, adaptive neural network based dynamic surface control (DSC) is developed for a class of nonlinear strict-feedback systems with unknown direction control gains and input saturation. A Gaussian error function based saturation model is employed such that the backstepping technique can be used in the control design. The explosion of complexity in traditional backstepping design is avoided by utilizing DSC. Based on backstepping combined with DSC, adaptive radial basis function neural network control is developed to guarantee that all the signals in the closed-loop system are globally bounded, and the tracking error converges to a small neighborhood of origin by appropriately choosing design parameters. Simulation results demonstrate the effectiveness of the proposed approach and the good performance is guaranteed even though both the saturation constraints and the wrong control direction are occurred.
In this note, adaptive neural network (NN) control is investigated for a class of uncertain nonlinear systems with asymmetric saturation actuators and external disturbances. To handle the effect of nonsmooth asymmetric saturation nonlinearity, a Gaussian error function-based continuous differentiable asymmetric saturation model is employed such that the backstepping technique can be used in the control design. The explosion of complexity in traditional backstepping design is avoided using dynamic surface control. Using radial basis function NN, adaptive control is developed to guarantee that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded, and the tracking error converges to a small neighborhood of origin by appropriately choosing design constants. The effectiveness of the proposed control is demonstrated in the simulation study.
In the recent decade, hiatus is the hottest issue in the community of climate change. As the area of great importance, the Tibetan Plateau (TP), however, did not appear to have any warming stoppage in the hiatus period. In fact, the TP showed a continuous warming in the recent decade. To explore why the TP did not show hiatus, we divide the surface air temperature into dynamically-induced temperature (DIT) and radiatively-forced temperature (RFT) by applying the dynamical adjustment method. Our results show that DIT displayed a relatively uniform warming background in the TP, with no obvious correlations with dynamic factors. Meanwhile, as the major contribution to warming, the RFT effect over the TP played the dominant role. The warming role is illustrated using the temperature change between perturbed and control simulation responses to CO2 or black carbon (BC) forcing via Community Earth System Model (CESM). It shows that an obvious warming in the TP is induced by the CO2 warming effect, and BC exhibits an amplifying effect on the warming. Therefore, the continuous warming in the TP was a result of uniform DIT warming over a large scale and enhanced RFT warming at a regional scale.
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