Radar leveling system is the key equipment for improving the radar mobility and survival capability. A combined quantitative feedback theory (QFT) controller is designed for the radar truck leveling simulator in this ...Radar leveling system is the key equipment for improving the radar mobility and survival capability. A combined quantitative feedback theory (QFT) controller is designed for the radar truck leveling simulator in this paper, which suffers from strong nonlinearities and system parameter uncertainties. QFT can reduce the plant uncertainties and stabilize the system, but it fails to obtain high-precision tracking. This drawback can be solved by a robust QFT control scheme based on zero phase error tracking control (ZPETC) compensation. The combined controller not only possesses high robustness, but greatly improves the system performance. To verify the effiectiveness and the potential of the proposed controller, a series of experiments have been carried out. Experimental results have demonstrated its robustness against a large range of parameters variation and high tracking precision performance, as well as its capability of restraining the load coupling among channels. The combined QFT controller can drive the radar truck leveling platform accurately, quickly and stably.展开更多
The progress of research and forecast techniques for tropical cyclone(TC)unusual tracks(UTs)in recent years is reviewed.A major research focus has been understanding which processes contribute to the evolution of the ...The progress of research and forecast techniques for tropical cyclone(TC)unusual tracks(UTs)in recent years is reviewed.A major research focus has been understanding which processes contribute to the evolution of the TC and steering flow over time,especially the reasons for the sharp changes in TC motion over a short period of time.When TCs are located in the vicinity of monsoon gyres,TC track forecast become more difficult to forecast due to the complex interaction between the TCs and the gyres.Moreover,the convection and latent heat can also feed back into the synoptic-scale features and in turn modify the steering flow.In this report,two cases with UTs are examined,along with an assessment of numerical model forecasts.Advances in numerical modelling and in particular the development of ensemble forecasting systems have proved beneficial in the prediction of such TCs.There are still great challenges in operational track forecasts and warnings,such as the initial TC track forecast,which is based on a poor pre-genesis analysis,TC track forecasts during interaction between two or more TCs and track predictions after landfall.Recently,artificial intelligence(AI)methods such as machine learning or deep learning have been widely applied in the field of TC forecasting.For TC track forecasting,a more effective method of center location is obtained by combining data from various sources and fully exploring the potential of AI,which provides more possibilities for improving TC prediction.展开更多
文摘Radar leveling system is the key equipment for improving the radar mobility and survival capability. A combined quantitative feedback theory (QFT) controller is designed for the radar truck leveling simulator in this paper, which suffers from strong nonlinearities and system parameter uncertainties. QFT can reduce the plant uncertainties and stabilize the system, but it fails to obtain high-precision tracking. This drawback can be solved by a robust QFT control scheme based on zero phase error tracking control (ZPETC) compensation. The combined controller not only possesses high robustness, but greatly improves the system performance. To verify the effiectiveness and the potential of the proposed controller, a series of experiments have been carried out. Experimental results have demonstrated its robustness against a large range of parameters variation and high tracking precision performance, as well as its capability of restraining the load coupling among channels. The combined QFT controller can drive the radar truck leveling platform accurately, quickly and stably.
基金supported by the National Key R&D Program of China(Grant 2023YFC3008501)the National Natural Science Foundation of China(41930972,42005141)the Science and Technology Development Foundation of the CAMS(grant number 2023KJ034).
文摘The progress of research and forecast techniques for tropical cyclone(TC)unusual tracks(UTs)in recent years is reviewed.A major research focus has been understanding which processes contribute to the evolution of the TC and steering flow over time,especially the reasons for the sharp changes in TC motion over a short period of time.When TCs are located in the vicinity of monsoon gyres,TC track forecast become more difficult to forecast due to the complex interaction between the TCs and the gyres.Moreover,the convection and latent heat can also feed back into the synoptic-scale features and in turn modify the steering flow.In this report,two cases with UTs are examined,along with an assessment of numerical model forecasts.Advances in numerical modelling and in particular the development of ensemble forecasting systems have proved beneficial in the prediction of such TCs.There are still great challenges in operational track forecasts and warnings,such as the initial TC track forecast,which is based on a poor pre-genesis analysis,TC track forecasts during interaction between two or more TCs and track predictions after landfall.Recently,artificial intelligence(AI)methods such as machine learning or deep learning have been widely applied in the field of TC forecasting.For TC track forecasting,a more effective method of center location is obtained by combining data from various sources and fully exploring the potential of AI,which provides more possibilities for improving TC prediction.
