This paper presents a Nonlinear Model Predictive Controller(NMPC)for the path following of autonomous vehicles and an algorithm to adaptively adjust the preview distance.The prediction model includes vehicle dynamics,...This paper presents a Nonlinear Model Predictive Controller(NMPC)for the path following of autonomous vehicles and an algorithm to adaptively adjust the preview distance.The prediction model includes vehicle dynamics,path following dynamics,and system input dynamics.The single-track vehicle model considers the vehicle’s coupled lateral and longitudinal dynamics,as well as nonlinear tire forces.The tracking error dynamics are derived based on the curvilinear coordinates.The cost function is designed to minimize path tracking errors and control effort while considering constraints such as actuator bounds and tire grip limits.An algorithm that utilizes the optimal preview distance vector to query the corresponding reference curvature and reference speed.The length of the preview path is adaptively adjusted based on the vehicle speed,heading error,and path curvature.We validate the controller performance in a simulation environment with the autonomous racing scenario.The simulation results show that the vehicle accurately follows the highly dynamic path with small tracking errors.The maximum preview distance can be prior estimated and guidance the selection of the prediction horizon for NMPC.展开更多
In order to enhance the control performance of piezo-positioning system,the influence of hysteresis characteristics and its compensation method are studied.Hammerstein model is used to represent the dynamic hysteresis...In order to enhance the control performance of piezo-positioning system,the influence of hysteresis characteristics and its compensation method are studied.Hammerstein model is used to represent the dynamic hysteresis nonlinear characteristics of piezo-positioning actuator.The static nonlinear part and dynamic linear part of the Hammerstein model are represented by models obtained through the Prandtl-Ishlinskii(PI)model and Hankel matrix system identification method,respectively.This model demonstrates good generalization capability for typical input frequencies below 200 Hz.A sliding mode inverse compensation tracking control strategy based on P-I inverse model and integral augmentation is proposed.Experimental results show that compared with PID inverse compensation control and sliding mode control without inverse compensation,the sliding mode inverse compensation control has a more ideal step response and no overshoot,moreover,the settling time is only 6.2 ms.In the frequency domain,the system closed-loop tracking bandwidth reaches 119.9 Hz,and the disturbance rejection bandwidth reaches 86.2 Hz.The proposed control strategy can effectively compensate the hysteresis nonlinearity,and improve the tracking accuracy and antidisturbance capability of piezo-positioning system.展开更多
A constrained generalized predictive control （GPC） algorithm based on the T-S fuzzy model is presented for the nonlinear system. First, a Takagi-Sugeno （T-S） fuzzy model based on the fuzzy cluster algorithm and th...A constrained generalized predictive control （GPC） algorithm based on the T-S fuzzy model is presented for the nonlinear system. First, a Takagi-Sugeno （T-S） fuzzy model based on the fuzzy cluster algorithm and the orthogonalleast square method is constructed to approach the nonlinear system. Since its consequence is linear, it can divide the nonlinear system into a number of linear or nearly linear subsystems. For this T-S fuzzy model, a GPC algorithm with input constraints is presented. This strategy takes into account all the constraints of the control signal and its increment, and does not require the calculation of the Diophantine equations. So it needs only a small computer memory and the computational speed is high. The simulation results show a good performance for the nonlinear systems.展开更多
A support vector machine with guadratic polynomial kernel function based nonlinear model multi-step-ahead optimizing predictive controller was presented. A support vector machine based predictive model was established...A support vector machine with guadratic polynomial kernel function based nonlinear model multi-step-ahead optimizing predictive controller was presented. A support vector machine based predictive model was established by black-box identification. And a quadratic objective function with receding horizon was selected to obtain the controller output. By solving a nonlinear optimization problem with equality constraint of model output and boundary constraint of controller output using Nelder-Mead simplex direct search method, a sub-optimal control law was achieved in feature space. The effect of the controller was demonstrated on a recognized benchmark problem and a continuous-stirred tank reactor. The simulation results show that the multi-step-ahead predictive controller can be well applied to nonlinear system, with better performance in following reference trajectory and disturbance-rejection.展开更多
By adopting cyclic increment loading and unloading method, time-independent and time-dependent strains can be separated. It is more reasonable to describe the reversible and the irreversible deformations of sample sep...By adopting cyclic increment loading and unloading method, time-independent and time-dependent strains can be separated. It is more reasonable to describe the reversible and the irreversible deformations of sample separately during creep process. A nonlinear elastic-visco-plastic rheological model is presented to characterize the time-based deformational behavior of hard rock. Specifically, a spring element is used to describe reversible instantaneous elastic deformation. A reversible nonlinear visco-elastic （RNVE） model is developed to characterize recoverable visco-elastic response. A combined model, which contains a fractional derivative dashpot in series with another Hook’s body, and a St. Venant body in parallel with them, is proposed to describe irreversible visco-plastic deformation. Furthermore, a three-stage damage equation based on strain energy is developed in the visco-plastic portion and then nonlinear elastic-visco-plastic rheological damage model is established to explain the trimodal creep response of hard rock. Finally, the proposed model is validated by a laboratory triaxial rheological experiment. Comparing with theoretical and experimental results, this rheological damage model characterizes well the reversible and irreversible deformations of the sample, especially the tertiary creep behavior.展开更多
The performance of the model algorithm control method is partially based on the accuracy of the system＇s model. It is difficult to obtain a good model of a nonlinear system, especially when the nonlinearity is high. ...The performance of the model algorithm control method is partially based on the accuracy of the system＇s model. It is difficult to obtain a good model of a nonlinear system, especially when the nonlinearity is high. Neural networks have the ability to ＂learn＂the characteristics of a system through nonlinear mapping to represent nonlinear functions as well as their inverse functions. This paper presents a model algorithm control method using neural networks for nonlinear time delay systems. Two neural networks are used in the control scheme. One neural network is trained as the model of the nonlinear time delay system, and the other one produces the control inputs. The neural networks are combined with the model algorithm control method to control the nonlinear time delay systems. Three examples are used to illustrate the proposed control method. The simulation results show that the proposed control method has a good control performance for nonlinear time delay systems.展开更多
Platform planning is one of the important problems in the command and control(C2) field. Hereto, we analyze the platform planning problem and present nonlinear optimal model aiming at maximizing the task completion qu...Platform planning is one of the important problems in the command and control(C2) field. Hereto, we analyze the platform planning problem and present nonlinear optimal model aiming at maximizing the task completion qualities. Firstly, we take into account the relation among tasks and build the single task nonlinear optimal model with a set of platform constraints. The Lagrange relaxation method and the pruning strategy are used to solve the model. Secondly, this paper presents optimization-based planning algorithms for efficiently allocating platforms to multiple tasks. To achieve the balance of the resource assignments among tasks, the m-best assignment algorithm and the pair-wise exchange(PWE)method are used to maximize multiple tasks completion qualities.Finally, a series of experiments are designed to verify the superiority and effectiveness of the proposed model and algorithms.展开更多
An improved model predictive control algorithm is proposed for Hammerstein-Wiener nonlinear systems.The proposed synthesis algorithm contains two parts:offline design the polytopic invariant sets,and online solve the ...An improved model predictive control algorithm is proposed for Hammerstein-Wiener nonlinear systems.The proposed synthesis algorithm contains two parts:offline design the polytopic invariant sets,and online solve the min-max optimization problem.The polytopic invariant set is adopted to replace the traditional ellipsoid invariant set.And the parameter-correlation nonlinear control law is designed to replace the traditional linear control law.Consequently,the terminal region is enlarged and the control effect is improved.Simulation and experiment are used to verify the validity of the wind tunnel flow field control algorithm.展开更多
Based on the multi-model principle, the fuzzy identification for nonlinear systems with multirate sampled data is studied.Firstly, the nonlinear system with multirate sampled data can be shown as the nonlinear weighte...Based on the multi-model principle, the fuzzy identification for nonlinear systems with multirate sampled data is studied.Firstly, the nonlinear system with multirate sampled data can be shown as the nonlinear weighted combination of some linear models at multiple local working points. On this basis, the fuzzy model of the multirate sampled nonlinear system is built. The premise structure of the fuzzy model is confirmed by using fuzzy competitive learning, and the conclusion parameters of the fuzzy model are estimated by the random gradient descent algorithm. The convergence of the proposed identification algorithm is given by using the martingale theorem and lemmas. The fuzzy model of the PH neutralization process of acid-base titration for hair quality detection is constructed to demonstrate the effectiveness of the proposed method.展开更多
This paper provides an introduction to a support vector machine, a new kernel-based technique introduced in statistical learning theory and structural risk minimization, then presents a modeling-control framework base...This paper provides an introduction to a support vector machine, a new kernel-based technique introduced in statistical learning theory and structural risk minimization, then presents a modeling-control framework based on SVM. At last a numerical experiment is taken to demonstrate the proposed approach's correctness and effectiveness.展开更多
Remaining useful life(RUL) prediction is one of the most crucial elements in prognostics and health management(PHM). Aiming at the imperfect prior information, this paper proposes an RUL prediction method based on a n...Remaining useful life(RUL) prediction is one of the most crucial elements in prognostics and health management(PHM). Aiming at the imperfect prior information, this paper proposes an RUL prediction method based on a nonlinear random coefficient regression(RCR) model with fusing failure time data.Firstly, some interesting natures of parameters estimation based on the nonlinear RCR model are given. Based on these natures,the failure time data can be fused as the prior information reasonably. Specifically, the fixed parameters are calculated by the field degradation data of the evaluated equipment and the prior information of random coefficient is estimated with fusing the failure time data of congeneric equipment. Then, the prior information of the random coefficient is updated online under the Bayesian framework, the probability density function(PDF) of the RUL with considering the limitation of the failure threshold is performed. Finally, two case studies are used for experimental verification. Compared with the traditional Bayesian method, the proposed method can effectively reduce the influence of imperfect prior information and improve the accuracy of RUL prediction.展开更多
Aiming at a class of nonlinear systems with multiple equilibrium points, we present a dual-mode model predictive control algorithm with extended terminal constraint set combined with control invariant set and gain sch...Aiming at a class of nonlinear systems with multiple equilibrium points, we present a dual-mode model predictive control algorithm with extended terminal constraint set combined with control invariant set and gain schedule. Local LQR control laws and the corresponding maximum control invariant sets can be designed for finite equilibrium points. It is guaranteed that control invariant sets are overlapped each other. The union of the control invariant sets is treated as the terminal constraint set of predictive control. The feasibility and stability of the novel dual-mode model predictive control are investigated with both variable and fixed horizon. Because of the introduction of extended terminal constrained set, the feasibility of optimization can be guaranteed with short prediction horizon. In this way, the size of the optimization problem is reduced so it is computationally efficient. Finally, a simulation example illustrating the algorithm is presented.展开更多
Many industry processes can be described as Hammerstein-Wiener nonlinear systems. In this work, an improved constrained model predictive control algorithm is presented for Hammerstein-Wiener systems. In the new approa...Many industry processes can be described as Hammerstein-Wiener nonlinear systems. In this work, an improved constrained model predictive control algorithm is presented for Hammerstein-Wiener systems. In the new approach, the maximum and minimum of partial derivative for input and output nonlinearities are solved in the neighbourhood of the equilibrium. And several parameter-dependent Lyapunov functions, each one corresponding to a different vertex of polytopic descriptions models, are introduced to analyze the stability of Hammerstein-Wiener systems, but only one Lyapunov function is utilized to analyze system stability like the traditional method. Consequently, the conservation of the traditional quadratic stability is removed, and the terminal regions are enlarged. Simulation and field trial results show that the proposed algorithm is valid. It has higher control precision and shorter blowing time than the traditional approach.展开更多
Nonlinear model predictive controllers(NMPC)can predict the future behavior of the under-controlled system using a nonlinear predictive model.Here,an array of hyper chaotic diagonal recurrent neural network(HCDRNN)was...Nonlinear model predictive controllers(NMPC)can predict the future behavior of the under-controlled system using a nonlinear predictive model.Here,an array of hyper chaotic diagonal recurrent neural network(HCDRNN)was proposed for modeling and predicting the behavior of the under-controller nonlinear system in a moving forward window.In order to improve the convergence of the parameters of the HCDRNN to improve system’s modeling,the extent of chaos is adjusted using a logistic map in the hidden layer.A novel NMPC based on the HCDRNN array(HCDRNN-NMPC)was proposed that the control signal with the help of an improved gradient descent method was obtained.The controller was used to control a continuous stirred tank reactor(CSTR)with hard-nonlinearities and input constraints,in the presence of uncertainties including external disturbance.The results of the simulations show the superior performance of the proposed method in trajectory tracking and disturbance rejection.Parameter convergence and neglectable prediction error of the neural network(NN),guaranteed stability and high tracking performance are the most significant advantages of the proposed scheme.展开更多
A feedforword neural network of multi-layer topologies for systems with hysteretic nonlinearity is constructed based on Bouce Wen differential model. It not only reflects the hysteresis force characteristics of the Bo...A feedforword neural network of multi-layer topologies for systems with hysteretic nonlinearity is constructed based on Bouce Wen differential model. It not only reflects the hysteresis force characteristics of the Bouce Wen model, but also determines its corresponding parameters. The simulation results show that restoring forceedisplacement curve hysteresis loop is very close to the real curve. The model trained can accurately predict the time response of system. The model is checked under the noise level. The result shows that the model has higher modeling precision, good generalization capability and a certain anti-interference ability.展开更多
In this paper, we develope a timodependent, nonlinear, photochemical-dynamical 2-D model which is composed of 3 models: dynamical gravity wave model, middle atmospheric photochemical model, and airglow layer photochem...In this paper, we develope a timodependent, nonlinear, photochemical-dynamical 2-D model which is composed of 3 models: dynamical gravity wave model, middle atmospheric photochemical model, and airglow layer photochemical model. We use the model to study the effect of the gravity wave propagation on the airglow layer. The comparison between the effects of the different wavelength gravity wave on the airglow emission distributions is made. When the vertical wavelength of the gravity wave is close to or is shorter than the thickness of the airglow layer, the gravity wave can make complex structure of the airglow layer, such as the double and multi-peak structures of the airglow layer. However, the gravity wave that has long vertical wavelength can make large scale perturbation of the airglow emission distribution.展开更多
The paper presents some problems of lightning overvoltage modeling in transmission lines with nonlinear elements.The presented results were obtained mostly for fast front transients of subsequent lightning return stro...The paper presents some problems of lightning overvoltage modeling in transmission lines with nonlinear elements.The presented results were obtained mostly for fast front transients of subsequent lightning return stroke currents.The effectiveness of numerical algorithms of nonlinear models and possibilities of their development for such transients are analyzed.Computer simulations carried out by application of EMTP show that nonlinear models of back-flashover and ZnO arresters work properly,while the implemented corona model can not be used for relatively large peak values of subsequent lightning return-stroke currents.展开更多
The necessity of the use of the block and parallel modeling of the nonlinear continuous mappings with NN is firstly expounded quantitatively. Then, a practical approach for the block and parallel modeling of the nonli...The necessity of the use of the block and parallel modeling of the nonlinear continuous mappings with NN is firstly expounded quantitatively. Then, a practical approach for the block and parallel modeling of the nonlinear continuous mappings with NN is proposed. Finally, an example indicating that the method raised in this paper can be realized by suitable existed software is given. The results of the experiment of the model discussed on the 3-D Mexican straw hat indicate that the block and parallel modeling based on NN is more precise and faster in computation than the direct ones and it is obviously a concrete example and the development of the large-scale general model established by Tu Xuyan.展开更多
基金“National Science and Technology Council”(NSTC 111-2221-E-027-088)。
文摘This paper presents a Nonlinear Model Predictive Controller(NMPC)for the path following of autonomous vehicles and an algorithm to adaptively adjust the preview distance.The prediction model includes vehicle dynamics,path following dynamics,and system input dynamics.The single-track vehicle model considers the vehicle’s coupled lateral and longitudinal dynamics,as well as nonlinear tire forces.The tracking error dynamics are derived based on the curvilinear coordinates.The cost function is designed to minimize path tracking errors and control effort while considering constraints such as actuator bounds and tire grip limits.An algorithm that utilizes the optimal preview distance vector to query the corresponding reference curvature and reference speed.The length of the preview path is adaptively adjusted based on the vehicle speed,heading error,and path curvature.We validate the controller performance in a simulation environment with the autonomous racing scenario.The simulation results show that the vehicle accurately follows the highly dynamic path with small tracking errors.The maximum preview distance can be prior estimated and guidance the selection of the prediction horizon for NMPC.
文摘In order to enhance the control performance of piezo-positioning system,the influence of hysteresis characteristics and its compensation method are studied.Hammerstein model is used to represent the dynamic hysteresis nonlinear characteristics of piezo-positioning actuator.The static nonlinear part and dynamic linear part of the Hammerstein model are represented by models obtained through the Prandtl-Ishlinskii(PI)model and Hankel matrix system identification method,respectively.This model demonstrates good generalization capability for typical input frequencies below 200 Hz.A sliding mode inverse compensation tracking control strategy based on P-I inverse model and integral augmentation is proposed.Experimental results show that compared with PID inverse compensation control and sliding mode control without inverse compensation,the sliding mode inverse compensation control has a more ideal step response and no overshoot,moreover,the settling time is only 6.2 ms.In the frequency domain,the system closed-loop tracking bandwidth reaches 119.9 Hz,and the disturbance rejection bandwidth reaches 86.2 Hz.The proposed control strategy can effectively compensate the hysteresis nonlinearity,and improve the tracking accuracy and antidisturbance capability of piezo-positioning system.
基金This Project was supported by the National Natural Science Foundation of China (60374037 and 60574036)the Opening Project Foundation of National Lab of Industrial Control Technology (0708008).
文摘A constrained generalized predictive control （GPC） algorithm based on the T-S fuzzy model is presented for the nonlinear system. First, a Takagi-Sugeno （T-S） fuzzy model based on the fuzzy cluster algorithm and the orthogonalleast square method is constructed to approach the nonlinear system. Since its consequence is linear, it can divide the nonlinear system into a number of linear or nearly linear subsystems. For this T-S fuzzy model, a GPC algorithm with input constraints is presented. This strategy takes into account all the constraints of the control signal and its increment, and does not require the calculation of the Diophantine equations. So it needs only a small computer memory and the computational speed is high. The simulation results show a good performance for the nonlinear systems.
文摘A support vector machine with guadratic polynomial kernel function based nonlinear model multi-step-ahead optimizing predictive controller was presented. A support vector machine based predictive model was established by black-box identification. And a quadratic objective function with receding horizon was selected to obtain the controller output. By solving a nonlinear optimization problem with equality constraint of model output and boundary constraint of controller output using Nelder-Mead simplex direct search method, a sub-optimal control law was achieved in feature space. The effect of the controller was demonstrated on a recognized benchmark problem and a continuous-stirred tank reactor. The simulation results show that the multi-step-ahead predictive controller can be well applied to nonlinear system, with better performance in following reference trajectory and disturbance-rejection.
基金Project(BK20150005)supported by the Natural Science Foundation of Jiangsu Province for Distinguished Young Scholars,ChinaProject(2015XKZD05)supported by the Fundamental Research Funds for the Central Universities,China
文摘By adopting cyclic increment loading and unloading method, time-independent and time-dependent strains can be separated. It is more reasonable to describe the reversible and the irreversible deformations of sample separately during creep process. A nonlinear elastic-visco-plastic rheological model is presented to characterize the time-based deformational behavior of hard rock. Specifically, a spring element is used to describe reversible instantaneous elastic deformation. A reversible nonlinear visco-elastic （RNVE） model is developed to characterize recoverable visco-elastic response. A combined model, which contains a fractional derivative dashpot in series with another Hook’s body, and a St. Venant body in parallel with them, is proposed to describe irreversible visco-plastic deformation. Furthermore, a three-stage damage equation based on strain energy is developed in the visco-plastic portion and then nonlinear elastic-visco-plastic rheological damage model is established to explain the trimodal creep response of hard rock. Finally, the proposed model is validated by a laboratory triaxial rheological experiment. Comparing with theoretical and experimental results, this rheological damage model characterizes well the reversible and irreversible deformations of the sample, especially the tertiary creep behavior.
基金supported by the Brain Korea 21 PLUS Project,National Research Foundation of Korea(NRF-2013R1A2A2A01068127NRF-2013R1A1A2A10009458)Jiangsu Province University Natural Science Research Project(13KJB510003)
文摘The performance of the model algorithm control method is partially based on the accuracy of the system＇s model. It is difficult to obtain a good model of a nonlinear system, especially when the nonlinearity is high. Neural networks have the ability to ＂learn＂the characteristics of a system through nonlinear mapping to represent nonlinear functions as well as their inverse functions. This paper presents a model algorithm control method using neural networks for nonlinear time delay systems. Two neural networks are used in the control scheme. One neural network is trained as the model of the nonlinear time delay system, and the other one produces the control inputs. The neural networks are combined with the model algorithm control method to control the nonlinear time delay systems. Three examples are used to illustrate the proposed control method. The simulation results show that the proposed control method has a good control performance for nonlinear time delay systems.
基金supported by the National Natural Science Foundation of China(61573017 61703425)+2 种基金the Aeronautical Science Fund(20175796014)the Shaanxi Province Natural Science Foundation Research Project(2016JQ6062 2017JM6062)
文摘Platform planning is one of the important problems in the command and control(C2) field. Hereto, we analyze the platform planning problem and present nonlinear optimal model aiming at maximizing the task completion qualities. Firstly, we take into account the relation among tasks and build the single task nonlinear optimal model with a set of platform constraints. The Lagrange relaxation method and the pruning strategy are used to solve the model. Secondly, this paper presents optimization-based planning algorithms for efficiently allocating platforms to multiple tasks. To achieve the balance of the resource assignments among tasks, the m-best assignment algorithm and the pair-wise exchange(PWE)method are used to maximize multiple tasks completion qualities.Finally, a series of experiments are designed to verify the superiority and effectiveness of the proposed model and algorithms.
基金Project(61074074)supported by the National Natural Science Foundation,ChinaProject(KT2012C01J0401)supported by the Group Innovation Fund,China
文摘An improved model predictive control algorithm is proposed for Hammerstein-Wiener nonlinear systems.The proposed synthesis algorithm contains two parts:offline design the polytopic invariant sets,and online solve the min-max optimization problem.The polytopic invariant set is adopted to replace the traditional ellipsoid invariant set.And the parameter-correlation nonlinear control law is designed to replace the traditional linear control law.Consequently,the terminal region is enlarged and the control effect is improved.Simulation and experiment are used to verify the validity of the wind tunnel flow field control algorithm.
基金supported by the National Natural Science Foundation of China(61863034)。
文摘Based on the multi-model principle, the fuzzy identification for nonlinear systems with multirate sampled data is studied.Firstly, the nonlinear system with multirate sampled data can be shown as the nonlinear weighted combination of some linear models at multiple local working points. On this basis, the fuzzy model of the multirate sampled nonlinear system is built. The premise structure of the fuzzy model is confirmed by using fuzzy competitive learning, and the conclusion parameters of the fuzzy model are estimated by the random gradient descent algorithm. The convergence of the proposed identification algorithm is given by using the martingale theorem and lemmas. The fuzzy model of the PH neutralization process of acid-base titration for hair quality detection is constructed to demonstrate the effectiveness of the proposed method.
文摘This paper provides an introduction to a support vector machine, a new kernel-based technique introduced in statistical learning theory and structural risk minimization, then presents a modeling-control framework based on SVM. At last a numerical experiment is taken to demonstrate the proposed approach's correctness and effectiveness.
基金supported by National Natural Science Foundation of China (61703410,61873175,62073336,61873273,61773386,61922089)。
文摘Remaining useful life(RUL) prediction is one of the most crucial elements in prognostics and health management(PHM). Aiming at the imperfect prior information, this paper proposes an RUL prediction method based on a nonlinear random coefficient regression(RCR) model with fusing failure time data.Firstly, some interesting natures of parameters estimation based on the nonlinear RCR model are given. Based on these natures,the failure time data can be fused as the prior information reasonably. Specifically, the fixed parameters are calculated by the field degradation data of the evaluated equipment and the prior information of random coefficient is estimated with fusing the failure time data of congeneric equipment. Then, the prior information of the random coefficient is updated online under the Bayesian framework, the probability density function(PDF) of the RUL with considering the limitation of the failure threshold is performed. Finally, two case studies are used for experimental verification. Compared with the traditional Bayesian method, the proposed method can effectively reduce the influence of imperfect prior information and improve the accuracy of RUL prediction.
基金Supported by National Natural Science Foundation of P. R. China (60474051, 60534020)Development Program of Shanghai Science and Technology Department (04DZ11008)the Program for New Century Excellent Talents in Universities of P. R. China (NCET)
文摘Aiming at a class of nonlinear systems with multiple equilibrium points, we present a dual-mode model predictive control algorithm with extended terminal constraint set combined with control invariant set and gain schedule. Local LQR control laws and the corresponding maximum control invariant sets can be designed for finite equilibrium points. It is guaranteed that control invariant sets are overlapped each other. The union of the control invariant sets is treated as the terminal constraint set of predictive control. The feasibility and stability of the novel dual-mode model predictive control are investigated with both variable and fixed horizon. Because of the introduction of extended terminal constrained set, the feasibility of optimization can be guaranteed with short prediction horizon. In this way, the size of the optimization problem is reduced so it is computationally efficient. Finally, a simulation example illustrating the algorithm is presented.
基金Project(61074074) supported by the National Natural Science Foundation,ChinaProject(KT2012C01J0401) supported by the Group Innovative Fund,China
文摘Many industry processes can be described as Hammerstein-Wiener nonlinear systems. In this work, an improved constrained model predictive control algorithm is presented for Hammerstein-Wiener systems. In the new approach, the maximum and minimum of partial derivative for input and output nonlinearities are solved in the neighbourhood of the equilibrium. And several parameter-dependent Lyapunov functions, each one corresponding to a different vertex of polytopic descriptions models, are introduced to analyze the stability of Hammerstein-Wiener systems, but only one Lyapunov function is utilized to analyze system stability like the traditional method. Consequently, the conservation of the traditional quadratic stability is removed, and the terminal regions are enlarged. Simulation and field trial results show that the proposed algorithm is valid. It has higher control precision and shorter blowing time than the traditional approach.
文摘Nonlinear model predictive controllers(NMPC)can predict the future behavior of the under-controlled system using a nonlinear predictive model.Here,an array of hyper chaotic diagonal recurrent neural network(HCDRNN)was proposed for modeling and predicting the behavior of the under-controller nonlinear system in a moving forward window.In order to improve the convergence of the parameters of the HCDRNN to improve system’s modeling,the extent of chaos is adjusted using a logistic map in the hidden layer.A novel NMPC based on the HCDRNN array(HCDRNN-NMPC)was proposed that the control signal with the help of an improved gradient descent method was obtained.The controller was used to control a continuous stirred tank reactor(CSTR)with hard-nonlinearities and input constraints,in the presence of uncertainties including external disturbance.The results of the simulations show the superior performance of the proposed method in trajectory tracking and disturbance rejection.Parameter convergence and neglectable prediction error of the neural network(NN),guaranteed stability and high tracking performance are the most significant advantages of the proposed scheme.
文摘A feedforword neural network of multi-layer topologies for systems with hysteretic nonlinearity is constructed based on Bouce Wen differential model. It not only reflects the hysteresis force characteristics of the Bouce Wen model, but also determines its corresponding parameters. The simulation results show that restoring forceedisplacement curve hysteresis loop is very close to the real curve. The model trained can accurately predict the time response of system. The model is checked under the noise level. The result shows that the model has higher modeling precision, good generalization capability and a certain anti-interference ability.
基金Supported by the National Science Foundation of China (40225011, 40336054)National Research Project (G2000078407)project of CAS (KZCX3-SW-217)International Collaboration Research Team Program of the Chinese Academy of SciencesChina-Russia Joint Research Center on Space Weather,Chinese Academy of Sciences
文摘In this paper, we develope a timodependent, nonlinear, photochemical-dynamical 2-D model which is composed of 3 models: dynamical gravity wave model, middle atmospheric photochemical model, and airglow layer photochemical model. We use the model to study the effect of the gravity wave propagation on the airglow layer. The comparison between the effects of the different wavelength gravity wave on the airglow emission distributions is made. When the vertical wavelength of the gravity wave is close to or is shorter than the thickness of the airglow layer, the gravity wave can make complex structure of the airglow layer, such as the double and multi-peak structures of the airglow layer. However, the gravity wave that has long vertical wavelength can make large scale perturbation of the airglow emission distribution.
文摘The paper presents some problems of lightning overvoltage modeling in transmission lines with nonlinear elements.The presented results were obtained mostly for fast front transients of subsequent lightning return stroke currents.The effectiveness of numerical algorithms of nonlinear models and possibilities of their development for such transients are analyzed.Computer simulations carried out by application of EMTP show that nonlinear models of back-flashover and ZnO arresters work properly,while the implemented corona model can not be used for relatively large peak values of subsequent lightning return-stroke currents.
基金The project was supported by the National Natural Science Foundation of China (60375014) and the Postdoctoral Sci-ence Foundation of China
文摘The necessity of the use of the block and parallel modeling of the nonlinear continuous mappings with NN is firstly expounded quantitatively. Then, a practical approach for the block and parallel modeling of the nonlinear continuous mappings with NN is proposed. Finally, an example indicating that the method raised in this paper can be realized by suitable existed software is given. The results of the experiment of the model discussed on the 3-D Mexican straw hat indicate that the block and parallel modeling based on NN is more precise and faster in computation than the direct ones and it is obviously a concrete example and the development of the large-scale general model established by Tu Xuyan.
