A global approximation based adaptive radial basis function（RBF） neural network control strategy is proposed for the trajectory tracking control of supercavitating vehicles（SV）.A nominal model is built firstly wit...A global approximation based adaptive radial basis function（RBF） neural network control strategy is proposed for the trajectory tracking control of supercavitating vehicles（SV）.A nominal model is built firstly with the unknown disturbance.Next, the control scheme is established consisting of a computed torque controller（CTC） for the practical vehicle and an RBF neural network controller to estimate model error between the practical vehicle and the nominal model. The network weights are adapted by employing a Lyapunov-based design. Then it is shown by the Lyapunov theory that the trajectory tracking errors asymptotically converge to a small neighborhood of zero. The control performance of the proposed controller is illustrated by simulation.展开更多
Target maneuver trajectory prediction plays an important role in air combat situation awareness and threat assessment.To solve the problem of low prediction accuracy of the traditional prediction method and model,a ta...Target maneuver trajectory prediction plays an important role in air combat situation awareness and threat assessment.To solve the problem of low prediction accuracy of the traditional prediction method and model,a target maneuver trajectory prediction model based on phase space reconstruction-radial basis function(PSR-RBF)neural network is established by combining the characteristics of trajectory with time continuity.In order to further improve the prediction performance of the model,the rival penalized competitive learning(RPCL)algorithm is introduced to determine the structure of RBF,the Levenberg-Marquardt(LM)and the hybrid algorithm of the improved particle swarm optimization(IPSO)algorithm and the k-means are introduced to optimize the parameter of RBF,and a PSR-RBF neural network is constructed.An independent method of 3D coordinates of the target maneuver trajectory is proposed,and the target manuver trajectory sample data is constructed by using the training data selected in the air combat maneuver instrument(ACMI),and the maneuver trajectory prediction model based on the PSR-RBF neural network is established.In order to verify the precision and real-time performance of the trajectory prediction model,the simulation experiment of target maneuver trajectory is performed.The results show that the prediction performance of the independent method is better,and the accuracy of the PSR-RBF prediction model proposed is better.The prediction confirms the effectiveness and applicability of the proposed method and model.展开更多
Based on immune clustering and evolutionary programming(EP), a hybrid algorithm to train the RBF network is proposed. An immune fuzzy C-means clustering algorithm (IFCM) is used to adaptively specify the amount and in...Based on immune clustering and evolutionary programming(EP), a hybrid algorithm to train the RBF network is proposed. An immune fuzzy C-means clustering algorithm (IFCM) is used to adaptively specify the amount and initial positions of the RBF centers according to input data set; then the RBF network is trained with EP that tends to global optima. The application of the hybrid algorithm in multiuser detection problem demonstrates that the RBF network trained with the algorithm has simple network structure with good generalization ability.展开更多
To ensure the control of the precision of air-fuel ratio(AFR)of port fuel injection(PFI)spark ignition(SI)engines,a chaos radial basis function(RBF)neural network is used to predict the air intake flow of the engine.T...To ensure the control of the precision of air-fuel ratio(AFR)of port fuel injection(PFI)spark ignition(SI)engines,a chaos radial basis function(RBF)neural network is used to predict the air intake flow of the engine.The data of air intake flow is proved to be multidimensionally nonlinear and chaotic.The RBF neural network is used to train the reconstructed phase space of the data.The chaos algorithm is employed to optimize the weights of output layer connection and the radial basis center of Gaussian function in hidden layer.The simulation results obtained from Matlab/Simulink illustrate that the model has higher accuracy compared to the conventional RBF model.The mean absolute error and the mean relative error of the chaos RBF model can reach 0.0017 and 0.48,respectively.展开更多
In this paper,a model reference adaptive control(MRAC)augmentation method of a linear controller is proposed for air-breathing hypersonic vehicle(AHV)during inlet unstart.With the development of hypersonic flight tech...In this paper,a model reference adaptive control(MRAC)augmentation method of a linear controller is proposed for air-breathing hypersonic vehicle(AHV)during inlet unstart.With the development of hypersonic flight technology,hypersonic vehicles have been gradually moving to the stage of weaponization.During the maneuvers,changes of attitude,Mach number and the back pressure can cause the inlet unstart phenomenon of scramjet.Inlet unstart causes significant changes in the aerodynamics of AHV,which may lead to deterioration of the tracking performance or instability of the control system.Therefore,we firstly establish the model of hypersonic vehicle considering inlet unstart,in which the changes of aerodynamics caused by inlet unstart is described as nonlinear uncertainty.Then,an MRAC augmentation method of a linear controller is proposed and the radial basis function(RBF)neural network is used to schedule the adaptive parameters of MRAC.Furthermore,the Lyapunov function is constructed to prove the stability of the proposed method.Finally,numerical simulations show that compared with the linear control method,the proposed method can stabilize the attitude of the hypersonic vehicle more quickly after the inlet unstart,which provides favorable conditions for inlet restart,thus verifying the effectiveness of the augmentation method proposed in the paper.展开更多
In this research,crashworthiness of polyurethane foam-filled tapered decagonal structures with different ratios of a/b=0,0.25,0.5,0.75 and 1 was evaluated under axial and oblique impacts.These new designed structures ...In this research,crashworthiness of polyurethane foam-filled tapered decagonal structures with different ratios of a/b=0,0.25,0.5,0.75 and 1 was evaluated under axial and oblique impacts.These new designed structures contained inner and outer tapered tubes,and four stiffening plates connected them together.The parameter a/b corresponds to the inner tube side length to the outer tube one.In addition,the space between the inner and outer tubes was filled with polyurethane foam.After validating the finite element model generated in LS-DYNA using the results of experimental tests,crashworthiness indicators of SEA(specific energy absorption)and Fmax(peak crushing force)were obtained for the studied structures.Based on the TOPSIS calculations,the semi-foam filled decagonal structure with the ratio of a/b=0.5 demonstrated the best crashworthiness capability among the studied ratios of a/b.Finally,optimum thicknesses(t1(thickness of the outer tube),t2(thickness of the inner tube),t3(thickness of the stiffening plates))of the selected decagonal structure were obtained by adopting RBF(radial basis function)neural network and genetic algorithm.展开更多
A self-organizing radial basis function(RBF) neural network(SODM-RBFNN) was presented for predicting the production yields and operating optimization. Gradient descent algorithm was used to optimize the widths of RBF ...A self-organizing radial basis function(RBF) neural network(SODM-RBFNN) was presented for predicting the production yields and operating optimization. Gradient descent algorithm was used to optimize the widths of RBF neural network with the initial parameters obtained by k-means learning method. During the iteration procedure of the algorithm, the centers of the neural network were optimized by using the gradient method with these optimized width values. The computational efficiency was maintained by using the multi-threading technique. SODM-RBFNN consists of two RBF neural network models: one is a running model used to predict the product yields of fluid catalytic cracking unit(FCCU) and optimize its operating parameters; the other is a learning model applied to construct or correct a RBF neural network. The running model can be updated by the learning model according to an accuracy criterion. The simulation results of a five-lump kinetic model exhibit its accuracy and generalization capabilities, and practical application in FCCU illustrates its effectiveness.展开更多
Artificial neural networks (ANNs) have become important tools in many fields, such as pattern recognition, signal processing and artificial intelligence. But many ANNs have their defects. For example, the results of R...Artificial neural networks (ANNs) have become important tools in many fields, such as pattern recognition, signal processing and artificial intelligence. But many ANNs have their defects. For example, the results of RBF networks greatly rely on the parameters of the basic functions and the squared errors often disperse if the parameters are not properly selected when the gradient descent algorithm is used. This paper describes a new algorithm for training an RBF network. In function approximation, this algorithm has two main advantages: high accuracy and stable learning process. In addition, it can be used as a good classifier in pattern recognition.展开更多
基金supported by the National Natural Science Foundation of China(5167920161473233)
文摘A global approximation based adaptive radial basis function（RBF） neural network control strategy is proposed for the trajectory tracking control of supercavitating vehicles（SV）.A nominal model is built firstly with the unknown disturbance.Next, the control scheme is established consisting of a computed torque controller（CTC） for the practical vehicle and an RBF neural network controller to estimate model error between the practical vehicle and the nominal model. The network weights are adapted by employing a Lyapunov-based design. Then it is shown by the Lyapunov theory that the trajectory tracking errors asymptotically converge to a small neighborhood of zero. The control performance of the proposed controller is illustrated by simulation.
文摘Target maneuver trajectory prediction plays an important role in air combat situation awareness and threat assessment.To solve the problem of low prediction accuracy of the traditional prediction method and model,a target maneuver trajectory prediction model based on phase space reconstruction-radial basis function(PSR-RBF)neural network is established by combining the characteristics of trajectory with time continuity.In order to further improve the prediction performance of the model,the rival penalized competitive learning(RPCL)algorithm is introduced to determine the structure of RBF,the Levenberg-Marquardt(LM)and the hybrid algorithm of the improved particle swarm optimization(IPSO)algorithm and the k-means are introduced to optimize the parameter of RBF,and a PSR-RBF neural network is constructed.An independent method of 3D coordinates of the target maneuver trajectory is proposed,and the target manuver trajectory sample data is constructed by using the training data selected in the air combat maneuver instrument(ACMI),and the maneuver trajectory prediction model based on the PSR-RBF neural network is established.In order to verify the precision and real-time performance of the trajectory prediction model,the simulation experiment of target maneuver trajectory is performed.The results show that the prediction performance of the independent method is better,and the accuracy of the PSR-RBF prediction model proposed is better.The prediction confirms the effectiveness and applicability of the proposed method and model.
文摘Based on immune clustering and evolutionary programming(EP), a hybrid algorithm to train the RBF network is proposed. An immune fuzzy C-means clustering algorithm (IFCM) is used to adaptively specify the amount and initial positions of the RBF centers according to input data set; then the RBF network is trained with EP that tends to global optima. The application of the hybrid algorithm in multiuser detection problem demonstrates that the RBF network trained with the algorithm has simple network structure with good generalization ability.
基金Project(51176014)supported by the National Natural Science Foundation of ChinaProject(2016JJ2003)supported by Natural Scienceof Hunan Province,ChinaProject(KF1605)supported by Key Laboratory of Safety Design and Reliability Technology of Engineering Vehicle in Hunan Province,China。
文摘To ensure the control of the precision of air-fuel ratio(AFR)of port fuel injection(PFI)spark ignition(SI)engines,a chaos radial basis function(RBF)neural network is used to predict the air intake flow of the engine.The data of air intake flow is proved to be multidimensionally nonlinear and chaotic.The RBF neural network is used to train the reconstructed phase space of the data.The chaos algorithm is employed to optimize the weights of output layer connection and the radial basis center of Gaussian function in hidden layer.The simulation results obtained from Matlab/Simulink illustrate that the model has higher accuracy compared to the conventional RBF model.The mean absolute error and the mean relative error of the chaos RBF model can reach 0.0017 and 0.48,respectively.
基金supported by the Foundation of Shanghai Aerospace Science and Technology(SAST2016077)。
文摘In this paper,a model reference adaptive control(MRAC)augmentation method of a linear controller is proposed for air-breathing hypersonic vehicle(AHV)during inlet unstart.With the development of hypersonic flight technology,hypersonic vehicles have been gradually moving to the stage of weaponization.During the maneuvers,changes of attitude,Mach number and the back pressure can cause the inlet unstart phenomenon of scramjet.Inlet unstart causes significant changes in the aerodynamics of AHV,which may lead to deterioration of the tracking performance or instability of the control system.Therefore,we firstly establish the model of hypersonic vehicle considering inlet unstart,in which the changes of aerodynamics caused by inlet unstart is described as nonlinear uncertainty.Then,an MRAC augmentation method of a linear controller is proposed and the radial basis function(RBF)neural network is used to schedule the adaptive parameters of MRAC.Furthermore,the Lyapunov function is constructed to prove the stability of the proposed method.Finally,numerical simulations show that compared with the linear control method,the proposed method can stabilize the attitude of the hypersonic vehicle more quickly after the inlet unstart,which provides favorable conditions for inlet restart,thus verifying the effectiveness of the augmentation method proposed in the paper.
基金Project(1365-96/7/22) supported by University of Mohaghegh Ardabili,Iran
文摘In this research,crashworthiness of polyurethane foam-filled tapered decagonal structures with different ratios of a/b=0,0.25,0.5,0.75 and 1 was evaluated under axial and oblique impacts.These new designed structures contained inner and outer tapered tubes,and four stiffening plates connected them together.The parameter a/b corresponds to the inner tube side length to the outer tube one.In addition,the space between the inner and outer tubes was filled with polyurethane foam.After validating the finite element model generated in LS-DYNA using the results of experimental tests,crashworthiness indicators of SEA(specific energy absorption)and Fmax(peak crushing force)were obtained for the studied structures.Based on the TOPSIS calculations,the semi-foam filled decagonal structure with the ratio of a/b=0.5 demonstrated the best crashworthiness capability among the studied ratios of a/b.Finally,optimum thicknesses(t1(thickness of the outer tube),t2(thickness of the inner tube),t3(thickness of the stiffening plates))of the selected decagonal structure were obtained by adopting RBF(radial basis function)neural network and genetic algorithm.
基金Projects(60974031,60704011,61174128)supported by the National Natural Science Foundation of China
文摘A self-organizing radial basis function(RBF) neural network(SODM-RBFNN) was presented for predicting the production yields and operating optimization. Gradient descent algorithm was used to optimize the widths of RBF neural network with the initial parameters obtained by k-means learning method. During the iteration procedure of the algorithm, the centers of the neural network were optimized by using the gradient method with these optimized width values. The computational efficiency was maintained by using the multi-threading technique. SODM-RBFNN consists of two RBF neural network models: one is a running model used to predict the product yields of fluid catalytic cracking unit(FCCU) and optimize its operating parameters; the other is a learning model applied to construct or correct a RBF neural network. The running model can be updated by the learning model according to an accuracy criterion. The simulation results of a five-lump kinetic model exhibit its accuracy and generalization capabilities, and practical application in FCCU illustrates its effectiveness.
文摘Artificial neural networks (ANNs) have become important tools in many fields, such as pattern recognition, signal processing and artificial intelligence. But many ANNs have their defects. For example, the results of RBF networks greatly rely on the parameters of the basic functions and the squared errors often disperse if the parameters are not properly selected when the gradient descent algorithm is used. This paper describes a new algorithm for training an RBF network. In function approximation, this algorithm has two main advantages: high accuracy and stable learning process. In addition, it can be used as a good classifier in pattern recognition.
