The Experts below are selected from a list of 44076 Experts worldwide ranked by ideXlab platform
Rui Zhang - One of the best experts on this subject based on the ideXlab platform.
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high order tracking differentiator based adaptive neural control of a flexible air breathing hypersonic vehicle subject to actuators constraints
Isa Transactions, 2015Co-Authors: Mingyan Tian, Jiaqi Huang, Rui ZhangAbstract:In this paper, an adaptive neural controller is exploited for a constrained flexible air-breathing hypersonic vehicle (FAHV) based on high-order tracking differentiator (HTD). By utilizing Functional Decomposition methodology, the dynamic model is reasonably decomposed into the respective velocity subsystem and altitude subsystem. For the velocity subsystem, a dynamic inversion based neural controller is constructed. By introducing the HTD to adaptively estimate the newly defined states generated in the process of model transformation, a novel neural based altitude controller that is quite simpler than the ones derived from back-stepping is addressed based on the normal output-feedback form instead of the strict-feedback formulation. Based on minimal-learning parameter scheme, only two neural networks with two adaptive parameters are needed for neural approximation. Especially, a novel auxiliary system is explored to deal with the problem of control inputs constraints. Finally, simulation results are presented to test the effectiveness of the proposed control strategy in the presence of system uncertainties and actuators constraints.
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novel adaptive neural control of flexible air breathing hypersonic vehicles based on sliding mode differentiator
Chinese Journal of Aeronautics, 2015Co-Authors: Rui ZhangAbstract:Abstract A novel adaptive neural control strategy is exploited for the longitudinal dynamics of a generic flexible air-breathing hypersonic vehicle (FAHV). By utilizing Functional Decomposition method, the dynamics of FAHV is decomposed into the velocity subsystem and the altitude subsystem. For each subsystem, only one neural network is employed for the unknown function approximation. To further reduce the computational burden, minimal-learning parameter (MLP) technology is used to estimate the norm of ideal weight vectors rather than their elements. By introducing sliding mode differentiator (SMD) to estimate the newly defined variables, there is no need for the strict-feedback form and virtual controller. Hence the developed control law is considerably simpler than the ones derived from back-stepping scheme. Finally, simulation studies are made to illustrate the effectiveness of the proposed control approach in spite of the flexible effects, system uncertainties and varying disturbances.
Zhongke Shi - One of the best experts on this subject based on the ideXlab platform.
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direct neural control of hypersonic flight vehicles with prediction model in discrete time
Neurocomputing, 2013Co-Authors: Danwei Wang, Fuchun Sun, Zhongke ShiAbstract:Abstract In this paper, the direct adaptive neural controller is investigated for the longitudinal dynamics of a generic hypersonic flight vehicle (HFV). The objective of the controller is to make the altitude and velocity to follow a given desired trajectory in the presence of aerodynamic uncertainties. Based on the Functional Decomposition, the adaptive discrete-time nonlinear controllers are developed using feedback linearization and neural approximation for the two subsystems. Different from the back-stepping design, the altitude subsystem is transformed into the explicit four-step ahead prediction model. With the prediction model, the controller is proposed without virtual controller design. Furthermore, only one direct neural network (NN) is employed for the lumped system uncertainty approximation. The controller is considerably simpler than the ones based on back-stepping scheme and the algorithm needs less NN parameters to be adjusted online. The semiglobal uniform ultimate boundedness (SGUUB) stability is investigated by the discrete-time Lyapunov analysis and the output tracking error is made within a neighborhood of zero. Accordingly, the NN controller is designed for velocity subsystem. The simulation is presented to show the effectiveness of the proposed control approach.
Bin Xu - One of the best experts on this subject based on the ideXlab platform.
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robust adaptive neural control of flexible hypersonic flight vehicle with dead zone input nonlinearity
Nonlinear Dynamics, 2015Co-Authors: Bin XuAbstract:This paper presents adaptive dynamic surface control for the flexible model of hypersonic flight vehicle in the presence of unknown dynamics and input nonlinearity. By modeling the flexible coupling as disturbance of rigid body, based on the Functional Decomposition, the dynamics is divided into attitude subsystem and velocity subsystem. Flight path angle, pitch angle, and pitching rate are involved in the attitude subsystem. To eliminate the inherent problem of “explosion of complexity” in back-stepping, the dynamic surface control is investigated to construct the controller. Furthermore, direct neural control with robust design is proposed without estimating the control gain function and in this way the singularity problem could be avoided. In the last step of dynamic surface design, through the use of Nussbaum-type function, stable adaptive control is presented for the unknown dynamics with time- varying control gain function. The uniform ultimate boundedness stability of the closed-loop system is guaranteed. Simulation result shows the feasibility of the proposed method.
Hugo Tullberg - One of the best experts on this subject based on the ideXlab platform.
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The METIS 5G architecture: A summary of METIS work on 5G architectures
IEEE Vehicular Technology Conference, 2015Co-Authors: Heinz Droste, Neiva Lindqvist, Makis Stamatelatos, Joseph Eichinger, Uwe Dötsch, Venkatkumar Venkatasubramanian, Gerd Zimmermann, Josef Eichinger, Hugo TullbergAbstract:During the last two years, the METIS project ("Mobile and wireless communications Enablers for the Twenty-twenty Information Society") has been conducting research on 5G-enabling technology components. This paper provides a summary of METIS work on 5G architectures. The architecture description is presented from different viewpoints. First, a Functional architecture is presented that may lay a foundation for development of first novel 5G network functions. It is based on Functional Decomposition of most relevant 5G technology components provided by METIS. The logical orchestration & control architecture depicts the realization of flexibility, scalability and service orientation needed to fulfil diverse 5G requirements. Finally, a third viewpoint reveals deployment aspects and function placement options for 5G.
Mingyan Tian - One of the best experts on this subject based on the ideXlab platform.
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high order tracking differentiator based adaptive neural control of a flexible air breathing hypersonic vehicle subject to actuators constraints
Isa Transactions, 2015Co-Authors: Mingyan Tian, Jiaqi Huang, Rui ZhangAbstract:In this paper, an adaptive neural controller is exploited for a constrained flexible air-breathing hypersonic vehicle (FAHV) based on high-order tracking differentiator (HTD). By utilizing Functional Decomposition methodology, the dynamic model is reasonably decomposed into the respective velocity subsystem and altitude subsystem. For the velocity subsystem, a dynamic inversion based neural controller is constructed. By introducing the HTD to adaptively estimate the newly defined states generated in the process of model transformation, a novel neural based altitude controller that is quite simpler than the ones derived from back-stepping is addressed based on the normal output-feedback form instead of the strict-feedback formulation. Based on minimal-learning parameter scheme, only two neural networks with two adaptive parameters are needed for neural approximation. Especially, a novel auxiliary system is explored to deal with the problem of control inputs constraints. Finally, simulation results are presented to test the effectiveness of the proposed control strategy in the presence of system uncertainties and actuators constraints.
