In modern wireless communication systems,the accurate acquisition of channel state information(CSI)is critical to the performance of beamforming,non-orthogonal multiple access(NOMA),etc.However,with the application of...In modern wireless communication systems,the accurate acquisition of channel state information(CSI)is critical to the performance of beamforming,non-orthogonal multiple access(NOMA),etc.However,with the application of massive MIMO in 5G,the number of antennas increases by hundreds or even thousands times,which leads to excessive feedback overhead and poses a huge challenge to the conventional channel state information feedback scheme.In this paper,by using deep learning technology,we develop a system framework for CSI feedback based on fully connected feedforward neural networks(FCFNN),named CF-FCFNN.Through learning the training set composed of CSI,CF-FCFNN is able to recover the original CSI from the compressed CSI more accurately compared with the existing method based on deep learning without increasing the algorithm complexity.展开更多
The optimal control is investigated for linear systems affected by external harmonic disturbance and applied to vibration control systems of offshore steel jacket platforms. The wave-induced force is the dominant load...The optimal control is investigated for linear systems affected by external harmonic disturbance and applied to vibration control systems of offshore steel jacket platforms. The wave-induced force is the dominant load that offshore structures are subjected to, and it can be taken as harmonic excitation for the system. The linearized Morison equation is employed to estimate the wave loading. The main result concerns the existence and design of a realizable optimal regulator, which is proposed to damp the forced oscillation in an optimal fashion. For demonstration of the effectiveness of the control scheme, the platform performance is investigated for different wave states. The simulations are based on the tuned mass damper and the active mass damper control devices. It is demonstrated that the control scheme is useful in reducing the displacement response of jacket-type offshore platforms.展开更多
The linear systems affected by additive external sinusoidal disturbances is studied. The problem is to damp this forced oscillation in an optimal fashion. The main result of this paper is a new design approach is prop...The linear systems affected by additive external sinusoidal disturbances is studied. The problem is to damp this forced oscillation in an optimal fashion. The main result of this paper is a new design approach is proposed of realizable feedforward and feedback optimal control law for a linear time invariant system with sinusoidal disturbances. The algorithm of solving the optimal control law is given. It is shown that the control law is easily realized and is robust with respect to errors produced by the external sinusoidal disturbances through simulation results.展开更多
The feedforward and feedback control strategy of water flowrate based on the analysis of thermal process in water cooling box was proposed, and the control strategy was applied to wire rod hot rolling at Baosteel Co. ...The feedforward and feedback control strategy of water flowrate based on the analysis of thermal process in water cooling box was proposed, and the control strategy was applied to wire rod hot rolling at Baosteel Co. The operation has proved that the strategy can control water flowrate in the cooling water box reasonably to ensure the temperature requirement of the wire discharged from the cooling water box.展开更多
This paper investigates the problem of global output feedback stabilization for a class of feedforward nonlinear systems via linear sampled-data control. To solve the problem, we first construct a linear sampled-data ...This paper investigates the problem of global output feedback stabilization for a class of feedforward nonlinear systems via linear sampled-data control. To solve the problem, we first construct a linear sampled-data observer and controller. Then, a scaling gain is introduced into the proposed observer and controller. Finally, we use the sampled-data output feedback domination approach to find the explicit formula for choosing the scaling gain and the sampling period which renders the closed-loop system globally asymptotically stable. A simulation example is given to demonstrate the effectiveness of the proposed design procedure.展开更多
This paper discusses the problem of global state regulation via output feedback for a class of feedforward nonlinear time-delay systems with unknown measurement sensitivity. Different from previous works, the nonlinea...This paper discusses the problem of global state regulation via output feedback for a class of feedforward nonlinear time-delay systems with unknown measurement sensitivity. Different from previous works, the nonlinear terms are dominated by upper triangular linear unmeasured (delayed) states multiplied by unknown growth rate. The unknown growth rate is composed of an unknown constant, a power function of output, and an input function. Furthermore, due to the measurement uncertainty of the system output, it is more difficult to solve this problem. It is proved that the presented output feedback controller can globally regulate all states of the nonlinear systems using the dynamic gain scaling technique and choosing the appropriate Lyapunov–Krasovskii functionals.展开更多
The optimal control problem was studied for linear time-varying systems,which was affected by external persistent disturbances with known dynamic characteristics but unknown initial conditions. To damp the effect of d...The optimal control problem was studied for linear time-varying systems,which was affected by external persistent disturbances with known dynamic characteristics but unknown initial conditions. To damp the effect of disturbances in an optimal fashion,we obtained a new feedforward and feedback optimal control law and gave the control algorithm by solving a Riccati differential equation and a matrix differential equation. Simulation results showed that the achieved optimal control law was realizable,efficient and robust to reject the external disturbances.展开更多
Recurrent Neural Networks(RNNs)have been widely applied to deal with temporal problems,such as flood forecasting and financial data processing.On the one hand,traditional RNNs models amplify the gradient issue due to ...Recurrent Neural Networks(RNNs)have been widely applied to deal with temporal problems,such as flood forecasting and financial data processing.On the one hand,traditional RNNs models amplify the gradient issue due to the strict time serial dependency,making it difficult to realize a long-term memory function.On the other hand,RNNs cells are highly complex,which will signifi-cantly increase computational complexity and cause waste of computational resources during model training.In this paper,an improved Time Feedforward Connections Recurrent Neural Networks(TFC-RNNs)model was first proposed to address the gradient issue.A parallel branch was introduced for the hidden state at time t−2 to be directly transferred to time t without the nonlinear transforma-tion at time t−1.This is effective in improving the long-term dependence of RNNs.Then,a novel cell structure named Single Gate Recurrent Unit(SGRU)was presented.This cell structure can reduce the number of parameters for RNNs cell,consequently reducing the computational complexity.Next,applying SGRU to TFC-RNNs as a new TFC-SGRU model solves the above two difficulties.Finally,the performance of our proposed TFC-SGRU was verified through sev-eral experiments in terms of long-term memory and anti-interference capabilities.Experimental results demonstrated that our proposed TFC-SGRU model can cap-ture helpful information with time step 1500 and effectively filter out the noise.The TFC-SGRU model accuracy is better than the LSTM and GRU models regarding language processing ability.展开更多
Neurons in the nervous system make connections with ascending feedforward projections and descending feedback projections,as well as projections from neural structures at the identical hierarchical level.These neurons...Neurons in the nervous system make connections with ascending feedforward projections and descending feedback projections,as well as projections from neural structures at the identical hierarchical level.These neurons form extremely complicated neural networks and pathways.Compared with the role of the feedforward projection,much less is known concerning the functional roles of the feedback projection.Visual cortex is a good model for studying functional roles of cortical feedback projections which involve many high functions,such as attention,searching and cognition.The present review mainly focused on the functional roles of feedback projections in the visual system.展开更多
Obvious resonance peak will be generated when parallel photovoltaic grid-connected inverters are connected to the weak grid with high grid impedance, which seriously affects the stability of grid-connected operation o...Obvious resonance peak will be generated when parallel photovoltaic grid-connected inverters are connected to the weak grid with high grid impedance, which seriously affects the stability of grid-connected operation of the photovoltaic system. To overcome the problems mentioned above, the mathematical model of the parallel photovoltaic inverters is established. Several factors including the impact of the reference current of the grid-connected inverter, the grid voltage interference and the current disturbance between the photovoltaic inverters in parallel with the grid-connected inverters are analyzed. The grid impedance and the LCL filter of the photovoltaic inverter system are found to be the key elements which lead to existence of resonance peak. This paper presents the branch voltage and current double feedback suppression method under the premise of not changing the topological structure of the photovoltaic inverter, which effectively handles the resonance peak, weakens the harmonic content of the grid current of the photovoltaic grid-connected inverter and the voltage at the point of common coupling, and improves the stability of the parallel operation of the photovoltaic grid-connected inverters in weak grid. At last, the simulation model is established to verify the reliability of this suppression method.展开更多
基金This work was supported by the Key Research and Development Project of Shaanxi Province under Grant no.2019ZDLGY07-07.
文摘In modern wireless communication systems,the accurate acquisition of channel state information(CSI)is critical to the performance of beamforming,non-orthogonal multiple access(NOMA),etc.However,with the application of massive MIMO in 5G,the number of antennas increases by hundreds or even thousands times,which leads to excessive feedback overhead and poses a huge challenge to the conventional channel state information feedback scheme.In this paper,by using deep learning technology,we develop a system framework for CSI feedback based on fully connected feedforward neural networks(FCFNN),named CF-FCFNN.Through learning the training set composed of CSI,CF-FCFNN is able to recover the original CSI from the compressed CSI more accurately compared with the existing method based on deep learning without increasing the algorithm complexity.
文摘The optimal control is investigated for linear systems affected by external harmonic disturbance and applied to vibration control systems of offshore steel jacket platforms. The wave-induced force is the dominant load that offshore structures are subjected to, and it can be taken as harmonic excitation for the system. The linearized Morison equation is employed to estimate the wave loading. The main result concerns the existence and design of a realizable optimal regulator, which is proposed to damp the forced oscillation in an optimal fashion. For demonstration of the effectiveness of the control scheme, the platform performance is investigated for different wave states. The simulations are based on the tuned mass damper and the active mass damper control devices. It is demonstrated that the control scheme is useful in reducing the displacement response of jacket-type offshore platforms.
文摘The linear systems affected by additive external sinusoidal disturbances is studied. The problem is to damp this forced oscillation in an optimal fashion. The main result of this paper is a new design approach is proposed of realizable feedforward and feedback optimal control law for a linear time invariant system with sinusoidal disturbances. The algorithm of solving the optimal control law is given. It is shown that the control law is easily realized and is robust with respect to errors produced by the external sinusoidal disturbances through simulation results.
基金Supported by National Natural Science Foundation of P.R.China(60474038)Science Research Foundation of Beijing Jiaotong University(2005SM005)Specialized Research Fund for the Doctoral Program of Higher Education(20060004002)
文摘The feedforward and feedback control strategy of water flowrate based on the analysis of thermal process in water cooling box was proposed, and the control strategy was applied to wire rod hot rolling at Baosteel Co. The operation has proved that the strategy can control water flowrate in the cooling water box reasonably to ensure the temperature requirement of the wire discharged from the cooling water box.
基金This work was supported by the National Natural Science Foundation of China （Nos. 61104068, 61273119） Natural Science Foundation of Jiangsu Province （No. BK2010200）+1 种基金 China Postdoctoral Science Foundation Founded Project （No. 2012M511176） the Fundamental Research Funds for the Central Universities （No. 2242013R30006）.
文摘This paper investigates the problem of global output feedback stabilization for a class of feedforward nonlinear systems via linear sampled-data control. To solve the problem, we first construct a linear sampled-data observer and controller. Then, a scaling gain is introduced into the proposed observer and controller. Finally, we use the sampled-data output feedback domination approach to find the explicit formula for choosing the scaling gain and the sampling period which renders the closed-loop system globally asymptotically stable. A simulation example is given to demonstrate the effectiveness of the proposed design procedure.
基金supported by the fund of Beijing Municipal Commission of Education(Nos.22019821001 and KM202210017001)the Natural Science Foundation of Henan Province(No.222300420253).
文摘This paper discusses the problem of global state regulation via output feedback for a class of feedforward nonlinear time-delay systems with unknown measurement sensitivity. Different from previous works, the nonlinear terms are dominated by upper triangular linear unmeasured (delayed) states multiplied by unknown growth rate. The unknown growth rate is composed of an unknown constant, a power function of output, and an input function. Furthermore, due to the measurement uncertainty of the system output, it is more difficult to solve this problem. It is proved that the presented output feedback controller can globally regulate all states of the nonlinear systems using the dynamic gain scaling technique and choosing the appropriate Lyapunov–Krasovskii functionals.
基金the National Natural Science Foundation of China (Grant No.60074001)the Natural Science Foundation of Shandong Province (Grant No.Y2000G02).
文摘The optimal control problem was studied for linear time-varying systems,which was affected by external persistent disturbances with known dynamic characteristics but unknown initial conditions. To damp the effect of disturbances in an optimal fashion,we obtained a new feedforward and feedback optimal control law and gave the control algorithm by solving a Riccati differential equation and a matrix differential equation. Simulation results showed that the achieved optimal control law was realizable,efficient and robust to reject the external disturbances.
基金This work was funded by the National Science Foundation of Hunan Province(2020JJ2029)。
文摘Recurrent Neural Networks(RNNs)have been widely applied to deal with temporal problems,such as flood forecasting and financial data processing.On the one hand,traditional RNNs models amplify the gradient issue due to the strict time serial dependency,making it difficult to realize a long-term memory function.On the other hand,RNNs cells are highly complex,which will signifi-cantly increase computational complexity and cause waste of computational resources during model training.In this paper,an improved Time Feedforward Connections Recurrent Neural Networks(TFC-RNNs)model was first proposed to address the gradient issue.A parallel branch was introduced for the hidden state at time t−2 to be directly transferred to time t without the nonlinear transforma-tion at time t−1.This is effective in improving the long-term dependence of RNNs.Then,a novel cell structure named Single Gate Recurrent Unit(SGRU)was presented.This cell structure can reduce the number of parameters for RNNs cell,consequently reducing the computational complexity.Next,applying SGRU to TFC-RNNs as a new TFC-SGRU model solves the above two difficulties.Finally,the performance of our proposed TFC-SGRU was verified through sev-eral experiments in terms of long-term memory and anti-interference capabilities.Experimental results demonstrated that our proposed TFC-SGRU model can cap-ture helpful information with time step 1500 and effectively filter out the noise.The TFC-SGRU model accuracy is better than the LSTM and GRU models regarding language processing ability.
基金supported by the National Natural Science Foundation of China(No.90208013)Shanghai Leading Academic Project B111 and"211"Projectof Ministry of Education of China
文摘Neurons in the nervous system make connections with ascending feedforward projections and descending feedback projections,as well as projections from neural structures at the identical hierarchical level.These neurons form extremely complicated neural networks and pathways.Compared with the role of the feedforward projection,much less is known concerning the functional roles of the feedback projection.Visual cortex is a good model for studying functional roles of cortical feedback projections which involve many high functions,such as attention,searching and cognition.The present review mainly focused on the functional roles of feedback projections in the visual system.
基金supported by National Natural Science Foundation of China (No. 61573303)Natural Science Foundation of Hebei Province (No. E2016203092)
文摘Obvious resonance peak will be generated when parallel photovoltaic grid-connected inverters are connected to the weak grid with high grid impedance, which seriously affects the stability of grid-connected operation of the photovoltaic system. To overcome the problems mentioned above, the mathematical model of the parallel photovoltaic inverters is established. Several factors including the impact of the reference current of the grid-connected inverter, the grid voltage interference and the current disturbance between the photovoltaic inverters in parallel with the grid-connected inverters are analyzed. The grid impedance and the LCL filter of the photovoltaic inverter system are found to be the key elements which lead to existence of resonance peak. This paper presents the branch voltage and current double feedback suppression method under the premise of not changing the topological structure of the photovoltaic inverter, which effectively handles the resonance peak, weakens the harmonic content of the grid current of the photovoltaic grid-connected inverter and the voltage at the point of common coupling, and improves the stability of the parallel operation of the photovoltaic grid-connected inverters in weak grid. At last, the simulation model is established to verify the reliability of this suppression method.
