The Experts below are selected from a list of 72723 Experts worldwide ranked by ideXlab platform
Seiichi Koakutsu - One of the best experts on this subject based on the ideXlab platform.
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a pareto optimal solution visualization method using som ng with Learning Parameter optimization
Systems Man and Cybernetics, 2016Co-Authors: Yusuke Kobayashi, Takashi Okamoto, Seiichi KoakutsuAbstract:The visualization of the Pareto optimal solution set is one of important issues of the decision-making process on the multi-objective optimization problem. The Pareto optimal solution visualization method using the self-organizing maps (SOM) is one of promising visualization methods. This method has two shortcomings in the Pareto optimal solution representation capability. One is that the maps have incorrect points that represent non-Pareto optimal solutions. The other is that the coverage of the maps for the edge region of the Pareto optimal solution set is not good. This study proposes a Pareto optimal solution visualization method using SOM-NG. In SOM, winner nodes affect neighbor nodes on the map space irrespective of similarity on the input data space. This causes the above-mentioned shortcomings. SOM-NG can form maps with considering similarity on the input space; hence, the shortcomings are expected to be overcome. In addition, in the proposed method, the Learning Parameter optimization is introduced. The effectiveness of the proposed method is confirmed on the incorrectness and the coverage of maps through numerical experiments.
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SMC - A Pareto optimal solution visualization method using SOM-NG with Learning Parameter optimization
2016 IEEE International Conference on Systems Man and Cybernetics (SMC), 2016Co-Authors: Yusuke Kobayashi, Takashi Okamoto, Seiichi KoakutsuAbstract:The visualization of the Pareto optimal solution set is one of important issues of the decision-making process on the multi-objective optimization problem. The Pareto optimal solution visualization method using the self-organizing maps (SOM) is one of promising visualization methods. This method has two shortcomings in the Pareto optimal solution representation capability. One is that the maps have incorrect points that represent non-Pareto optimal solutions. The other is that the coverage of the maps for the edge region of the Pareto optimal solution set is not good. This study proposes a Pareto optimal solution visualization method using SOM-NG. In SOM, winner nodes affect neighbor nodes on the map space irrespective of similarity on the input data space. This causes the above-mentioned shortcomings. SOM-NG can form maps with considering similarity on the input space; hence, the shortcomings are expected to be overcome. In addition, in the proposed method, the Learning Parameter optimization is introduced. The effectiveness of the proposed method is confirmed on the incorrectness and the coverage of maps through numerical experiments.
Jiaqi Huang - One of the best experts on this subject based on the ideXlab platform.
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guaranteeing preselected tracking quality for air breathing hypersonic non affine models with an unknown control direction via concise neural control
Journal of The Franklin Institute-engineering and Applied Mathematics, 2016Co-Authors: Daozhi Wei, Jiaqi HuangAbstract:Abstract A simplified neural controller is addressed for the longitudinal dynamics of an air-breathing hypersonic vehicle (AHV) with a completely unknown control direction by utilizing the prescribed performance control scheme. Unlike the existing literatures, the exploited methodology does not require an affine AHV model or any prior information about the sign of control gains. Moreover, the proposed strategy can provide preselected bounds on the transient and steady performance of velocity and altitude tracking errors. The altitude dynamics is converted into a pure feedback formulation with an unknown control direction, based on which, a novel adaptive neural controller that is quite simpler than the ones derived from back-stepping designs is achieved. For the problem of the unknown control direction, a Nussbaum-type function is introduced to handle it. By employing the minimal-Learning Parameter (MLP) technique to regulate the norm instead of the elements of the ideal weight vector, only one Learning Parameter is required for neural approximation. Thus, a low computational burden design is obtained. Finally, simulations are performed to verify the presented control approach.
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neural approximation based robust adaptive control of flexible air breathing hypersonic vehicles with parametric uncertainties and control input constraints
Information Sciences, 2016Co-Authors: Xiangwei Bu, Xiaoyan Wu, Jiaqi HuangAbstract:In this paper, a neural-approximation-based robust adaptive control methodology is proposed for a constrained flexible air-breathing hypersonic vehicle (FAHV) subject to parametric uncertainties. To reduce the computational costs, only two radial basis function neural networks (RBFNNs) are applied to approximate the lumped unknown nonlinearities of the velocity subsystem and the altitude subsystem, while guaranteeing the exploited controller with satisfactory robustness against system uncertainties. Furthermore, a minimal-Learning Parameter (MLP) approach is employed to update the norm rather than the elements of RBFNNs' weight vectors, which yields a low computational load design. By constructing a novel auxiliary system to compensate the desired control laws, the effects of magnitude constraints on actuators are tackled. The Lyapunov synthesis proves that the closed-loop uniformly ultimately bounded stability can be achieved even when the physical limitations on actuators are in effect. Finally, simulation results are presented to verify the efficacy of the addressed control strategy in the presence of uncertain Parameters, external disturbances and control input constraints.
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a guaranteed transient performance based adaptive neural control scheme with low complexity computation for flexible air breathing hypersonic vehicles
Nonlinear Dynamics, 2016Co-Authors: Jiaqi Huang, Daozhi WeiAbstract:A robust adaptive neural control scheme is addressed for a generic flexible air-breathing hypersonic vehicle, capable of guaranteeing velocity and altitude tracking errors with desired transient performance. Different from the back-stepping design, a novel neural approximation controller is explored for the altitude subsystem based on a quite simple normal output-feedback formulation rather than a strict-feedback one, while there is no need of the complex recursive design procedure of virtual control laws. Furthermore, on the basis of the minimal Learning Parameter technique, the updating Parameters are reduced greatly. Thus, the exploited strategy exhibits good low-complexity computation. In particular, a new finite-time-convergent differentiator is devised to estimate the newly generated states and it is also employed to provide the necessary high-order time derivatives of reference commands, based on which the proposed control methodology becomes achievable. Finally, the effectiveness of the design is confirmed by simulation results.
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minimal Learning Parameter based simplified adaptive neural back stepping control of flexible air breathing hypersonic vehicles without virtual controllers
Neurocomputing, 2016Co-Authors: Jiaqi Huang, Rui ZhangAbstract:In this paper, a novel adaptive neural control methodology is addressed for a flexible air-breathing hypersonic vehicle (FAHV) by a fusion of improved back-stepping and a minimal-Learning-Parameter (MLP) scheme. To facilitate the control design, the vehicle dynamics is decomposed into the altitude subsystem and the velocity subsystem. Different from the traditional back-stepping design, in this paper, the virtual control laws for the altitude dynamics are artificial intermediate variables required only for analytic purpose while only the final actual controller is needed to be implemented. For each subsystem, only one neural network is employed to approximate the lumped uncertainty. Moreover, by the merit of the MLP technique, only one Learning Parameter is required for neural approximation in each subsystem. The novel contribution with respect to the existing literatures is that the proposed control strategy is concise and the computational load is low. Finally, the effectiveness of the exploited control approach is verified by simulation results in the presence of uncertain Parameters.
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novel auxiliary error compensation design for the adaptive neural control of a constrained flexible air breathing hypersonic vehicle
Neurocomputing, 2016Co-Authors: Rui Zhang, Jiaqi HuangAbstract:This paper investigates the design of auxiliary error compensation for adaptive neural control of the longitudinal dynamics of a flexible air-breathing hypersonic vehicle (FAHV) with magnitude constraints on actuators. The control objective pursued is to steer velocity and altitude to follow their respective reference trajectories in the presence of actuator saturation and system uncertainties. To guarantee the exploited controller's robustness with respect to parametric uncertainties, neural network (NN) is applied to approximate the lumped uncertainty of each subsystem of FAHV model. Different from the traditional Parameter updating technique, in this paper, the minimal-Learning-Parameter (MLP) scheme is introduced to estimate the norm rather than the elements of NN's weight vector while the computational load is reduced. The special contribution is that novel auxiliary systems are developed to compensate both the tracking errors and desired control laws, based on which the explored controller can still provide effective tracking of velocity and altitude commands when the actuators are saturated. Finally, numerical simulations are performed to illustrate the command tracking performance of the proposed strategy.
Rui Zhang - One of the best experts on this subject based on the ideXlab platform.
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minimal Learning Parameter based simplified adaptive neural back stepping control of flexible air breathing hypersonic vehicles without virtual controllers
Neurocomputing, 2016Co-Authors: Jiaqi Huang, Rui ZhangAbstract:In this paper, a novel adaptive neural control methodology is addressed for a flexible air-breathing hypersonic vehicle (FAHV) by a fusion of improved back-stepping and a minimal-Learning-Parameter (MLP) scheme. To facilitate the control design, the vehicle dynamics is decomposed into the altitude subsystem and the velocity subsystem. Different from the traditional back-stepping design, in this paper, the virtual control laws for the altitude dynamics are artificial intermediate variables required only for analytic purpose while only the final actual controller is needed to be implemented. For each subsystem, only one neural network is employed to approximate the lumped uncertainty. Moreover, by the merit of the MLP technique, only one Learning Parameter is required for neural approximation in each subsystem. The novel contribution with respect to the existing literatures is that the proposed control strategy is concise and the computational load is low. Finally, the effectiveness of the exploited control approach is verified by simulation results in the presence of uncertain Parameters.
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novel auxiliary error compensation design for the adaptive neural control of a constrained flexible air breathing hypersonic vehicle
Neurocomputing, 2016Co-Authors: Rui Zhang, Jiaqi HuangAbstract:This paper investigates the design of auxiliary error compensation for adaptive neural control of the longitudinal dynamics of a flexible air-breathing hypersonic vehicle (FAHV) with magnitude constraints on actuators. The control objective pursued is to steer velocity and altitude to follow their respective reference trajectories in the presence of actuator saturation and system uncertainties. To guarantee the exploited controller's robustness with respect to parametric uncertainties, neural network (NN) is applied to approximate the lumped uncertainty of each subsystem of FAHV model. Different from the traditional Parameter updating technique, in this paper, the minimal-Learning-Parameter (MLP) scheme is introduced to estimate the norm rather than the elements of NN's weight vector while the computational load is reduced. The special contribution is that novel auxiliary systems are developed to compensate both the tracking errors and desired control laws, based on which the explored controller can still provide effective tracking of velocity and altitude commands when the actuators are saturated. Finally, numerical simulations are performed to illustrate the command tracking performance of the proposed strategy.
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novel prescribed performance neural control of a flexible air breathing hypersonic vehicle with unknown initial errors
Isa Transactions, 2015Co-Authors: Fujing Zhu, Jiaqi Huang, Rui ZhangAbstract:A novel prescribed performance neural controller with unknown initial errors is addressed for the longitudinal dynamic model of a flexible air-breathing hypersonic vehicle (FAHV) subject to parametric uncertainties. Different from traditional prescribed performance control (PPC) requiring that the initial errors have to be known accurately, this paper investigates the tracking control without accurate initial errors via exploiting a new performance function. A combined neural back-stepping and minimal Learning Parameter (MLP) technology is employed for exploring a prescribed performance controller that provides robust tracking of velocity and altitude reference trajectories. The highlight is that the transient performance of velocity and altitude tracking errors is satisfactory and the computational load of neural approximation is low. Finally, numerical simulation results from a nonlinear FAHV model demonstrate the efficacy of the proposed strategy.
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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.
Yusuke Kobayashi - One of the best experts on this subject based on the ideXlab platform.
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a pareto optimal solution visualization method using som ng with Learning Parameter optimization
Systems Man and Cybernetics, 2016Co-Authors: Yusuke Kobayashi, Takashi Okamoto, Seiichi KoakutsuAbstract:The visualization of the Pareto optimal solution set is one of important issues of the decision-making process on the multi-objective optimization problem. The Pareto optimal solution visualization method using the self-organizing maps (SOM) is one of promising visualization methods. This method has two shortcomings in the Pareto optimal solution representation capability. One is that the maps have incorrect points that represent non-Pareto optimal solutions. The other is that the coverage of the maps for the edge region of the Pareto optimal solution set is not good. This study proposes a Pareto optimal solution visualization method using SOM-NG. In SOM, winner nodes affect neighbor nodes on the map space irrespective of similarity on the input data space. This causes the above-mentioned shortcomings. SOM-NG can form maps with considering similarity on the input space; hence, the shortcomings are expected to be overcome. In addition, in the proposed method, the Learning Parameter optimization is introduced. The effectiveness of the proposed method is confirmed on the incorrectness and the coverage of maps through numerical experiments.
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SMC - A Pareto optimal solution visualization method using SOM-NG with Learning Parameter optimization
2016 IEEE International Conference on Systems Man and Cybernetics (SMC), 2016Co-Authors: Yusuke Kobayashi, Takashi Okamoto, Seiichi KoakutsuAbstract:The visualization of the Pareto optimal solution set is one of important issues of the decision-making process on the multi-objective optimization problem. The Pareto optimal solution visualization method using the self-organizing maps (SOM) is one of promising visualization methods. This method has two shortcomings in the Pareto optimal solution representation capability. One is that the maps have incorrect points that represent non-Pareto optimal solutions. The other is that the coverage of the maps for the edge region of the Pareto optimal solution set is not good. This study proposes a Pareto optimal solution visualization method using SOM-NG. In SOM, winner nodes affect neighbor nodes on the map space irrespective of similarity on the input data space. This causes the above-mentioned shortcomings. SOM-NG can form maps with considering similarity on the input space; hence, the shortcomings are expected to be overcome. In addition, in the proposed method, the Learning Parameter optimization is introduced. The effectiveness of the proposed method is confirmed on the incorrectness and the coverage of maps through numerical experiments.
Takashi Okamoto - One of the best experts on this subject based on the ideXlab platform.
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a pareto optimal solution visualization method using som ng with Learning Parameter optimization
Systems Man and Cybernetics, 2016Co-Authors: Yusuke Kobayashi, Takashi Okamoto, Seiichi KoakutsuAbstract:The visualization of the Pareto optimal solution set is one of important issues of the decision-making process on the multi-objective optimization problem. The Pareto optimal solution visualization method using the self-organizing maps (SOM) is one of promising visualization methods. This method has two shortcomings in the Pareto optimal solution representation capability. One is that the maps have incorrect points that represent non-Pareto optimal solutions. The other is that the coverage of the maps for the edge region of the Pareto optimal solution set is not good. This study proposes a Pareto optimal solution visualization method using SOM-NG. In SOM, winner nodes affect neighbor nodes on the map space irrespective of similarity on the input data space. This causes the above-mentioned shortcomings. SOM-NG can form maps with considering similarity on the input space; hence, the shortcomings are expected to be overcome. In addition, in the proposed method, the Learning Parameter optimization is introduced. The effectiveness of the proposed method is confirmed on the incorrectness and the coverage of maps through numerical experiments.
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SMC - A Pareto optimal solution visualization method using SOM-NG with Learning Parameter optimization
2016 IEEE International Conference on Systems Man and Cybernetics (SMC), 2016Co-Authors: Yusuke Kobayashi, Takashi Okamoto, Seiichi KoakutsuAbstract:The visualization of the Pareto optimal solution set is one of important issues of the decision-making process on the multi-objective optimization problem. The Pareto optimal solution visualization method using the self-organizing maps (SOM) is one of promising visualization methods. This method has two shortcomings in the Pareto optimal solution representation capability. One is that the maps have incorrect points that represent non-Pareto optimal solutions. The other is that the coverage of the maps for the edge region of the Pareto optimal solution set is not good. This study proposes a Pareto optimal solution visualization method using SOM-NG. In SOM, winner nodes affect neighbor nodes on the map space irrespective of similarity on the input data space. This causes the above-mentioned shortcomings. SOM-NG can form maps with considering similarity on the input space; hence, the shortcomings are expected to be overcome. In addition, in the proposed method, the Learning Parameter optimization is introduced. The effectiveness of the proposed method is confirmed on the incorrectness and the coverage of maps through numerical experiments.