To counter BTT guidance mode, new relative motion equations of the targetaircraft and the attack aircraft are proposed. The inverse system theory of the nonlinearcontrol is used, and the direct BTT-180 guidance comman...To counter BTT guidance mode, new relative motion equations of the targetaircraft and the attack aircraft are proposed. The inverse system theory of the nonlinearcontrol is used, and the direct BTT-180 guidance command is solved, which can operatethe attack aircraft to automatically complete the flight mission of the preceding stage ofthe terminal weapon delivery, and thus the automatic attack is extended from the stage ofthe terminal weapon delivery to the preceding stage of the terminal weapon delivery.展开更多
A learning controller of nonhonolomic robot in real-time based on support vector machine(SVM)is presented.The controller includes two parts:one is kinematic controller based on nonlinear law,and the other is dynamic c...A learning controller of nonhonolomic robot in real-time based on support vector machine(SVM)is presented.The controller includes two parts:one is kinematic controller based on nonlinear law,and the other is dynamic controller based on SVM.The kinematic controller is aimed to provide desired velocity which can make the steering system stable.The dynamic controller is aimed to transform the desired velocity to control torque.The parameters of the dynamic system of the robot are estimated through SVM learning algorithm according to the training data of sliding windows in real time.The proposed controller can adapt to the changes in the robot model and uncertainties in the environment.Compared with artificial neural network(ANN)controller,SVM controller can converge to the reference trajectory more quickly and the tracking error is smaller.The simulation results verify the effectiveness of the method proposed.展开更多
Due to their intrinsically nonlinear characteristics,development of control strategies that are implementable and can fully utilize the capabilities of semiactive control devices is an important and challenging task.I...Due to their intrinsically nonlinear characteristics,development of control strategies that are implementable and can fully utilize the capabilities of semiactive control devices is an important and challenging task.In this study,two control strategies are proposed for protecting buildings against dynamic hazards,such as severe earthquakes and strong winds,using one of the most promising semiactive control devices,the magnetorheological (MR) damper.The first control strategy is implemented by introducing an inverse neural network (NN) model of the MR damper.These NN models provide direct estimation of the voltage that is required to produce a target control force calculated from some optimal control algorithms.The major objective of this research is to provide an effective means for implementation of the MR damper with existing control algorithms.The second control strategy involves the design of a fuzzy controller and an adaptation law.The control objective is to minimize the difference between some desirable responses and the response of the combined system by adaptively adjusting the MR damper.The use of the adaptation law eliminates the need to acquire characteristics of the combined system in advance. Because the control strategy based on the combination of the fuzzy controller and the adaptation law doesn't require a prior knowledge of the combined building-damper system,this approach provides a robust control strategy that can be used to protect nonlinear or uncertain structures subjected to random loads.展开更多
文摘To counter BTT guidance mode, new relative motion equations of the targetaircraft and the attack aircraft are proposed. The inverse system theory of the nonlinearcontrol is used, and the direct BTT-180 guidance command is solved, which can operatethe attack aircraft to automatically complete the flight mission of the preceding stage ofthe terminal weapon delivery, and thus the automatic attack is extended from the stage ofthe terminal weapon delivery to the preceding stage of the terminal weapon delivery.
基金Project(60910005)supported by the National Natural Science Foundation of China
文摘A learning controller of nonhonolomic robot in real-time based on support vector machine(SVM)is presented.The controller includes two parts:one is kinematic controller based on nonlinear law,and the other is dynamic controller based on SVM.The kinematic controller is aimed to provide desired velocity which can make the steering system stable.The dynamic controller is aimed to transform the desired velocity to control torque.The parameters of the dynamic system of the robot are estimated through SVM learning algorithm according to the training data of sliding windows in real time.The proposed controller can adapt to the changes in the robot model and uncertainties in the environment.Compared with artificial neural network(ANN)controller,SVM controller can converge to the reference trajectory more quickly and the tracking error is smaller.The simulation results verify the effectiveness of the method proposed.
基金Hong Kong Research Grant Council Competitive Earmarked Research Grant HKUST 6218/99Ethe National Science Foundation under grant CMS 99-00234.
文摘Due to their intrinsically nonlinear characteristics,development of control strategies that are implementable and can fully utilize the capabilities of semiactive control devices is an important and challenging task.In this study,two control strategies are proposed for protecting buildings against dynamic hazards,such as severe earthquakes and strong winds,using one of the most promising semiactive control devices,the magnetorheological (MR) damper.The first control strategy is implemented by introducing an inverse neural network (NN) model of the MR damper.These NN models provide direct estimation of the voltage that is required to produce a target control force calculated from some optimal control algorithms.The major objective of this research is to provide an effective means for implementation of the MR damper with existing control algorithms.The second control strategy involves the design of a fuzzy controller and an adaptation law.The control objective is to minimize the difference between some desirable responses and the response of the combined system by adaptively adjusting the MR damper.The use of the adaptation law eliminates the need to acquire characteristics of the combined system in advance. Because the control strategy based on the combination of the fuzzy controller and the adaptation law doesn't require a prior knowledge of the combined building-damper system,this approach provides a robust control strategy that can be used to protect nonlinear or uncertain structures subjected to random loads.
