In this paper, adaptive identification and control of nonlinear dynamical systems are investigated using radial basis function networks (RBF). Firstly, a novel approach to train the RBF is introduced, which employs an...In this paper, adaptive identification and control of nonlinear dynamical systems are investigated using radial basis function networks (RBF). Firstly, a novel approach to train the RBF is introduced, which employs an adaptive fuzzy generalized learning vector quantization (AFGLVQ) technique and recursive least squares algorithm with variable forgetting factor (VRLS). The AFGLVQ adjusts the centers of the RBF while the VRLS updates the connection weights of the network. The identification algorithm has the properties of rapid convergence and persistent adaptability that make it suitable for real-time control. Secondly, on the basis of the one-step ahead RBF predictor, the control law is optimized iteratively through a numerical stable Davidon's least squares-based (SDLS) minimization approach. Four nonlinear examples are simulated to demonstrate the effectiveness of the identification and control algorithms.展开更多
The Radial Basis Functions Neural Network （RBFNN） is used to establish the model of a response system through the input and output data of the system. The synchronization between a drive system and the response syst...The Radial Basis Functions Neural Network （RBFNN） is used to establish the model of a response system through the input and output data of the system. The synchronization between a drive system and the response system can be implemented by employing the RBFNN model and state feedback control. In this case, the exact mathematical model, which is the precondition for the conventional method, is unnecessary for implementing synchronization. The effect of the model error is investigated and a corresponding theorem is developed. The effect of the parameter perturbations and the measurement noise is investigated through simulations. The simulation results under different conditions show the effectiveness of the method.展开更多
An adaptive integral dynamic surface control approach based on fully tuned radial basis function neural network （FTRBFNN） is presented for a general class of strict-feedback nonlinear systems,which may possess a wid...An adaptive integral dynamic surface control approach based on fully tuned radial basis function neural network （FTRBFNN） is presented for a general class of strict-feedback nonlinear systems,which may possess a wide class of uncertainties that are not linearly parameterized and do not have any prior knowledge of the bounding functions.FTRBFNN is employed to approximate the uncertainty online,and a systematic framework for adaptive controller design is given by dynamic surface control. The control algorithm has two outstanding features,namely,the neural network regulates the weights,width and center of Gaussian function simultaneously,which ensures the control system has perfect ability of restraining different unknown uncertainties and the integral term of tracking error introduced in the control law can eliminate the static error of the closed loop system effectively. As a result,high control precision can be achieved.All signals in the closed loop system can be guaranteed bounded by Lyapunov approach.Finally,simulation results demonstrate the validity of the control approach.展开更多
An enhanced trajectory linearization control （TLC） structure based on radial basis function neural network （RBFNN） and its application on an aerospace vehicle （ASV） flight control system are presensted. The infl...An enhanced trajectory linearization control （TLC） structure based on radial basis function neural network （RBFNN） and its application on an aerospace vehicle （ASV） flight control system are presensted. The influence of unknown disturbances and uncertainties is reduced by RBFNN thanks to its approaching ability, and a robustifying itera is used to overcome the approximate error of RBFNN. The parameters adaptive adjusting laws are designed on the Lyapunov theory. The uniform ultimate boundedness of all signals of the composite closed-loop system is proved based on Lyapunov theory. Finally, the flight control system of an ASV is designed based on the proposed method. Simulation results demonstrate the effectiveness and robustness of the designed approach.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
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
To improve the global search ability and imaging quality of electrical resistivity imaging(ERI) inversion, a two-stage learning ICPSO algorithm of radial basis function neural network(RBFNN) based on information crite...To improve the global search ability and imaging quality of electrical resistivity imaging(ERI) inversion, a two-stage learning ICPSO algorithm of radial basis function neural network(RBFNN) based on information criterion(IC) and particle swarm optimization(PSO) is presented. In the proposed method, IC is applied to obtain the hidden layer structure by calculating the optimal IC value automatically and PSO algorithm is used to optimize the centers and widths of the radial basis functions in the hidden layer. Meanwhile, impacts of different information criteria to the inversion results are compared, and an implementation of the proposed ICPSO algorithm is given. The optimized neural network has one hidden layer with 261 nodes selected by AKAIKE's information criterion(AIC) and it is trained on 32 data sets and tested on another 8 synthetic data sets. Two complex synthetic examples are used to verify the feasibility and effectiveness of the proposed method with two learning stages. The results show that the proposed method has better performance and higher imaging quality than three-layer and four-layer back propagation neural networks(BPNNs) and traditional least square(LS) inversion.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
A new vision-based approach was presented for predicting the behavior of the ball carrier—shooting, passing and dribbling in basketball matches. It was proposed to recognize the ball carrier’s head pose by classifyi...A new vision-based approach was presented for predicting the behavior of the ball carrier—shooting, passing and dribbling in basketball matches. It was proposed to recognize the ball carrier’s head pose by classifying its yaw angle to determine his vision range and the court situation of the sportsman within his vision range can be further learned. In basketball match videos characterized by cluttered background, fast motion of the sportsmen and low resolution of their head images, and the covariance descriptor, were adopted to fuse multiple visual features of the head region, which can be seen as a point on the Riemannian manifold and then mapped to the tangent space. Then, the classification of head yaw angle was directly completed in this space through the trained multiclass LogitBoost. In order to describe the court situation of all sportsmen within the ball carrier’s vision range, artificial potential field (APF)-based information was introduced. Finally, the behavior of the ball carrier—shooting, passing and dribbling, was predicted using radial basis function (RBF) neural network as the classifier. Experimental results show that the average prediction accuracy of the proposed method can reach 80% on the video recorded in basketball matches, which validates its effectiveness.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
A neural network model and fuzzy neural network controller was designed to control the inner impedance of a proton exchange membrane fuel cell (PEMFC) stack. A radial basis function (RBF) neural network model was trai...A neural network model and fuzzy neural network controller was designed to control the inner impedance of a proton exchange membrane fuel cell (PEMFC) stack. A radial basis function (RBF) neural network model was trained by the input-output data of impedance. A fuzzy neural network controller was designed to control the impedance response. The RBF neural network model was used to test the fuzzy neural network controller. The results show that the RBF model output can imitate actual output well, the maximal error is not beyond 20 m-, the training time is about 1 s by using 20 neurons, and the mean squared errors is 141.9 m-2. The impedance of the PEMFC stack is controlled within the optimum range when the load changes, and the adjustive time is about 3 min.展开更多
The electrode regulator system is a complex system with many variables, strong coupling and strong nonlinearity, while conventional control methods such as proportional integral derivative （PID） can not meet the req...The electrode regulator system is a complex system with many variables, strong coupling and strong nonlinearity, while conventional control methods such as proportional integral derivative （PID） can not meet the requirements. A robust adaptive neural network controller （RANNC） for electrode regulator system was proposed. Artificial neural networks were established to learn the system dynamics. The nonlinear control law was derived directly based on an input-output approximating method via the Taylor expansion, which avoids complex control development and intensive computation. The stability of the closed-loop system was established by the Lyapunov method. The current fluctuation relative percentage is less than ±8% and heating rate is up to 6.32 ℃/min when the proposed controller is used. The experiment results show that the proposed control scheme is better than inverse neural network controller （INNC） and PID controller （PIDC）.展开更多
文摘In this paper, adaptive identification and control of nonlinear dynamical systems are investigated using radial basis function networks (RBF). Firstly, a novel approach to train the RBF is introduced, which employs an adaptive fuzzy generalized learning vector quantization (AFGLVQ) technique and recursive least squares algorithm with variable forgetting factor (VRLS). The AFGLVQ adjusts the centers of the RBF while the VRLS updates the connection weights of the network. The identification algorithm has the properties of rapid convergence and persistent adaptability that make it suitable for real-time control. Secondly, on the basis of the one-step ahead RBF predictor, the control law is optimized iteratively through a numerical stable Davidon's least squares-based (SDLS) minimization approach. Four nonlinear examples are simulated to demonstrate the effectiveness of the identification and control algorithms.
基金This project was supported in part by the Science Foundation of Shanxi Province (2003F028)China Postdoctoral Science Foundation (20060390318).
文摘The Radial Basis Functions Neural Network （RBFNN） is used to establish the model of a response system through the input and output data of the system. The synchronization between a drive system and the response system can be implemented by employing the RBFNN model and state feedback control. In this case, the exact mathematical model, which is the precondition for the conventional method, is unnecessary for implementing synchronization. The effect of the model error is investigated and a corresponding theorem is developed. The effect of the parameter perturbations and the measurement noise is investigated through simulations. The simulation results under different conditions show the effectiveness of the method.
基金supported by the China Postdoctoral Science Foundation (200904501035 201003548)+3 种基金the National Natural Science Foundation of China (60835001907160289101600460804017)
文摘An adaptive integral dynamic surface control approach based on fully tuned radial basis function neural network （FTRBFNN） is presented for a general class of strict-feedback nonlinear systems,which may possess a wide class of uncertainties that are not linearly parameterized and do not have any prior knowledge of the bounding functions.FTRBFNN is employed to approximate the uncertainty online,and a systematic framework for adaptive controller design is given by dynamic surface control. The control algorithm has two outstanding features,namely,the neural network regulates the weights,width and center of Gaussian function simultaneously,which ensures the control system has perfect ability of restraining different unknown uncertainties and the integral term of tracking error introduced in the control law can eliminate the static error of the closed loop system effectively. As a result,high control precision can be achieved.All signals in the closed loop system can be guaranteed bounded by Lyapunov approach.Finally,simulation results demonstrate the validity of the control approach.
基金the National Natural Science Foundation of China (90405011).
文摘An enhanced trajectory linearization control （TLC） structure based on radial basis function neural network （RBFNN） and its application on an aerospace vehicle （ASV） flight control system are presensted. The influence of unknown disturbances and uncertainties is reduced by RBFNN thanks to its approaching ability, and a robustifying itera is used to overcome the approximate error of RBFNN. The parameters adaptive adjusting laws are designed on the Lyapunov theory. The uniform ultimate boundedness of all signals of the composite closed-loop system is proved based on Lyapunov theory. Finally, the flight control system of an ASV is designed based on the proposed method. Simulation results demonstrate the effectiveness and robustness of the designed approach.
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.
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.
基金Project(41374118)supported by the National Natural Science Foundation,ChinaProject(20120162110015)supported by Research Fund for the Doctoral Program of Higher Education,China+3 种基金Project(2015M580700)supported by the China Postdoctoral Science Foundation,ChinaProject(2016JJ3086)supported by the Hunan Provincial Natural Science Foundation,ChinaProject(2015JC3067)supported by the Hunan Provincial Science and Technology Program,ChinaProject(15B138)supported by the Scientific Research Fund of Hunan Provincial Education Department,China
文摘To improve the global search ability and imaging quality of electrical resistivity imaging(ERI) inversion, a two-stage learning ICPSO algorithm of radial basis function neural network(RBFNN) based on information criterion(IC) and particle swarm optimization(PSO) is presented. In the proposed method, IC is applied to obtain the hidden layer structure by calculating the optimal IC value automatically and PSO algorithm is used to optimize the centers and widths of the radial basis functions in the hidden layer. Meanwhile, impacts of different information criteria to the inversion results are compared, and an implementation of the proposed ICPSO algorithm is given. The optimized neural network has one hidden layer with 261 nodes selected by AKAIKE's information criterion(AIC) and it is trained on 32 data sets and tested on another 8 synthetic data sets. Two complex synthetic examples are used to verify the feasibility and effectiveness of the proposed method with two learning stages. The results show that the proposed method has better performance and higher imaging quality than three-layer and four-layer back propagation neural networks(BPNNs) and traditional least square(LS) inversion.
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(50808025) supported by the National Natural Science Foundation of ChinaProject(20090162110057) supported by the Doctoral Fund of Ministry of Education, China
文摘A new vision-based approach was presented for predicting the behavior of the ball carrier—shooting, passing and dribbling in basketball matches. It was proposed to recognize the ball carrier’s head pose by classifying its yaw angle to determine his vision range and the court situation of the sportsman within his vision range can be further learned. In basketball match videos characterized by cluttered background, fast motion of the sportsmen and low resolution of their head images, and the covariance descriptor, were adopted to fuse multiple visual features of the head region, which can be seen as a point on the Riemannian manifold and then mapped to the tangent space. Then, the classification of head yaw angle was directly completed in this space through the trained multiclass LogitBoost. In order to describe the court situation of all sportsmen within the ball carrier’s vision range, artificial potential field (APF)-based information was introduced. Finally, the behavior of the ball carrier—shooting, passing and dribbling, was predicted using radial basis function (RBF) neural network as the classifier. Experimental results show that the average prediction accuracy of the proposed method can reach 80% on the video recorded in basketball matches, which validates its effectiveness.
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.
基金Project(2003AA517020) supported by the National High Technology Research and Development Program of China
文摘A neural network model and fuzzy neural network controller was designed to control the inner impedance of a proton exchange membrane fuel cell (PEMFC) stack. A radial basis function (RBF) neural network model was trained by the input-output data of impedance. A fuzzy neural network controller was designed to control the impedance response. The RBF neural network model was used to test the fuzzy neural network controller. The results show that the RBF model output can imitate actual output well, the maximal error is not beyond 20 m-, the training time is about 1 s by using 20 neurons, and the mean squared errors is 141.9 m-2. The impedance of the PEMFC stack is controlled within the optimum range when the load changes, and the adjustive time is about 3 min.
基金Project(N100604002) supported by the Fundamental Research Funds for Central Universities of ChinaProject(61074074) supported by the National Natural Science Foundation of China
文摘The electrode regulator system is a complex system with many variables, strong coupling and strong nonlinearity, while conventional control methods such as proportional integral derivative （PID） can not meet the requirements. A robust adaptive neural network controller （RANNC） for electrode regulator system was proposed. Artificial neural networks were established to learn the system dynamics. The nonlinear control law was derived directly based on an input-output approximating method via the Taylor expansion, which avoids complex control development and intensive computation. The stability of the closed-loop system was established by the Lyapunov method. The current fluctuation relative percentage is less than ±8% and heating rate is up to 6.32 ℃/min when the proposed controller is used. The experiment results show that the proposed control scheme is better than inverse neural network controller （INNC） and PID controller （PIDC）.
