We design three kinds of photonic crystal fibres (PCF) with two zero-dispersion wavelengths (ZDWs) using the improved full vector index method (FVIM) and finite-difference frequency domain (FDFD} techniques. Ba...We design three kinds of photonic crystal fibres (PCF) with two zero-dispersion wavelengths (ZDWs) using the improved full vector index method (FVIM) and finite-difference frequency domain (FDFD} techniques. Based on these designed fibres, the effect of fibre structure, pump power and wavelength on the modulation instability (MI) gain in the anomalous dispersion region close to the second ZDW of the PCFs is comprehensively analysed in this paper. The analytical results show that an optimal MI gain can be obtained when the optimal pump wavelength (1530 nm) is slightly shorter than the second ZDW (1538 nm) and the optimal pump power is 250 W. Importantly, the total MI gain bandwidth has been increased to 260 nm for the first time, so far as we know, for an optimally-designed fibre with ∧ = 1.4 nm and d/∧ = 0.676, and the gain profile became much smoother. The optimal pump wavelength relies on the second ZDW of the PCF whereas the optimal pump power depends on the corporate operation of the optimal fibre structure and optimal pump wavelength, which is important in designing the most appropriate PCF to attain higher broadband and gain amplification.展开更多
The combination of structural health monitoring and vibration control is of great importance to provide components of smart structures.While synthetic algorithms have been proposed,adaptive control that is compatible ...The combination of structural health monitoring and vibration control is of great importance to provide components of smart structures.While synthetic algorithms have been proposed,adaptive control that is compatible with changing conditions still needs to be used,and time-varying systems are required to be simultaneously estimated with the application of adaptive control.In this research,the identification of structural time-varying dynamic characteristics and optimized simple adaptive control are integrated.First,reduced variations of physical parameters are estimated online using the multiple forgetting factor recursive least squares(MFRLS)method.Then,the energy from the structural vibration is simultaneously specified to optimize the control force with the identified parameters to be operational.Optimization is also performed based on the probability density function of the energy under the seismic excitation at any time.Finally,the optimal control force is obtained by the simple adaptive control(SAC)algorithm and energy coefficient.A numerical example and benchmark structure are employed to investigate the efficiency of the proposed approach.The simulation results revealed the effectiveness of the integrated online identification and optimal adaptive control in systems.展开更多
Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and ...Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.展开更多
The reduction of fuel consumption in engines is always considered of vital importance.Along these lines,in this work,this goal was attained by optimizing the heavy-duty commercial vehicle engine control strategy.More ...The reduction of fuel consumption in engines is always considered of vital importance.Along these lines,in this work,this goal was attained by optimizing the heavy-duty commercial vehicle engine control strategy.More specifically,at first,a general first principles model for heavy-duty commercial vehicles and a transient fuel consumptionmodel for heavy-duty commercial vehicles were developed and the parameters were adjusted to fit the empirical data.The accuracy of the proposed modelwas demonstrated fromthe stage and the final results.Next,the control optimization problem resulting in low fuel consumption in heavy commercial vehicles was described,with minimal fuel usage as the optimization goal and throttle opening as the control variable.Then,a time-continuous engine management approach was assessed.Next,the factors that influence low fuel consumption in heavy-duty commercial vehicles were systematically examined.To reduce the computing complexity,the control strategies related to the time constraints of the engine were parametrized using three different methods.The most effective solution was obtained by applying a global optimization strategy because the constrained optimization problem was nonlinear.Finally,the effectiveness of the low-fuel consumption engine control strategy was demonstrated by comparing the simulated and field test results.展开更多
Axial flux permanent magnet synchronous motors(AFPMSMs)have been widely used in wind-power generation,electric vehicles,aircraft,and other renewable-energy applications owing to their high power density,operating effi...Axial flux permanent magnet synchronous motors(AFPMSMs)have been widely used in wind-power generation,electric vehicles,aircraft,and other renewable-energy applications owing to their high power density,operating efficiency,and integrability.To facilitate comprehensive research on AFPMSM,this article reviews the developments in the research on the design and control optimization of AFPMSMs.First,the basic topologies of AFPMSMs are introduced and classified.Second,the key points of the design optimization of core and coreless AFPMSMs are summarized from the aspects of parameter design,structure design,and material optimization.Third,because efficiency improvement is an issue that needs to be addressed when AFPMSMs are applied to electric or other vehicles,the development status of efficiency-optimization control strategies is reviewed.Moreover,control strategies proposed to suppress torque ripple caused by the small inductance of disc coreless permanent magnet synchronous motors(DCPMSMs)are summarized.An overview of the rotor-synchronization control strategies for disc contra-rotating permanent magnet synchronous motors(CRPMSMs)is presented.Finally,the current difficulties and development trends revealed in this review are discussed.展开更多
In this paper we study a bilinear optimal control problem for a diffusive Lotka-Volterra competition model with chemo-repulsion in a bounded domain of ℝ^(ℕ),N=2,3.This model describes the competition of two species in...In this paper we study a bilinear optimal control problem for a diffusive Lotka-Volterra competition model with chemo-repulsion in a bounded domain of ℝ^(ℕ),N=2,3.This model describes the competition of two species in which one of them avoid encounters with rivals through a chemo-repulsion mechanism.We prove the existence and uniqueness of weak-strong solutions,and then we analyze the existence of a global optimal solution for a related bilinear optimal control problem,where the control is acting on the chemical signal.Posteriorly,we derive first-order optimality conditions for local optimal solutions using the Lagrange multipliers theory.Finally,we propose a discrete approximation scheme of the optimality system based on the gradient method,which is validated with some computational experiments.展开更多
We present an optimal and robust quantum control method for efficient population transfer in asymmetric double quantum-dot molecules.We derive a long-duration control scheme that allows for highly efficient population...We present an optimal and robust quantum control method for efficient population transfer in asymmetric double quantum-dot molecules.We derive a long-duration control scheme that allows for highly efficient population transfer by accurately controlling the amplitude of a narrow-bandwidth pulse.To overcome fluctuations in control field parameters,we employ a frequency-domain quantum optimal control theory method to optimize the spectral phase of a single pulse with broad bandwidth while preserving the spectral amplitude.It is shown that this spectral-phase-only optimization approach can successfully identify robust and optimal control fields,leading to efficient population transfer to the target state while concurrently suppressing population transfer to undesired states.The method demonstrates resilience to fluctuations in control field parameters,making it a promising approach for reliable and efficient population transfer in practical applications.展开更多
This paper presents a novel sequential inverse optimal control(SIOC)method for discrete-time systems,which calculates the unknown weight vectors of the cost function in real time using the input and output of an optim...This paper presents a novel sequential inverse optimal control(SIOC)method for discrete-time systems,which calculates the unknown weight vectors of the cost function in real time using the input and output of an optimally controlled discrete-time system.The proposed method overcomes the limitations of previous approaches by eliminating the need for the invertible Jacobian assumption.It calculates the possible-solution spaces and their intersections sequentially until the dimension of the intersection space decreases to one.The remaining one-dimensional vector of the possible-solution space’s intersection represents the SIOC solution.The paper presents clear conditions for convergence and addresses the issue of noisy data by clarifying the conditions for the singular values of the matrices that relate to the possible-solution space.The effectiveness of the proposed method is demonstrated through simulation results.展开更多
In this paper,a new optimal adaptive backstepping control approach for nonlinear systems under deception attacks via reinforcement learning is presented in this paper.The existence of nonlinear terms in the studied sy...In this paper,a new optimal adaptive backstepping control approach for nonlinear systems under deception attacks via reinforcement learning is presented in this paper.The existence of nonlinear terms in the studied system makes it very difficult to design the optimal controller using traditional methods.To achieve optimal control,RL algorithm based on critic–actor architecture is considered for the nonlinear system.Due to the significant security risks of network transmission,the system is vulnerable to deception attacks,which can make all the system state unavailable.By using the attacked states to design coordinate transformation,the harm brought by unknown deception attacks has been overcome.The presented control strategy can ensure that all signals in the closed-loop system are semi-globally ultimately bounded.Finally,the simulation experiment is shown to prove the effectiveness of the strategy.展开更多
In this paper, the matrix Riccati equation is considered. There is no general way for solving the matrix Riccati equation despite the many fields to which it applies. While scalar Riccati equation has been studied tho...In this paper, the matrix Riccati equation is considered. There is no general way for solving the matrix Riccati equation despite the many fields to which it applies. While scalar Riccati equation has been studied thoroughly, matrix Riccati equation of which scalar Riccati equations is a particular case, is much less investigated. This article proposes a change of variable that allows to find explicit solution of the Matrix Riccati equation. We then apply this solution to Optimal Control.展开更多
In this paper we study optimal advertising problems that model the introduction of a new product into the market in the presence of carryover effects of the advertisement and with memory effects in the level of goodwi...In this paper we study optimal advertising problems that model the introduction of a new product into the market in the presence of carryover effects of the advertisement and with memory effects in the level of goodwill. In particular, we let the dynamics of the product goodwill to depend on the past, and also on past advertising efforts. We treat the problem by means of the stochastic Pontryagin maximum principle, that here is considered for a class of problems where in the state equation either the state or the control depend on the past. Moreover the control acts on the martingale term and the space of controls U can be chosen to be non-convex but now the space of controls U can be chosen to be non-convex. The maximum principle is thus formulated using a first-order adjoint Backward Stochastic Differential Equations (BSDEs), which can be explicitly computed due to the specific characteristics of the model, and a second-order adjoint relation.展开更多
In this article, the transmission dynamics of a Hand-Foot-Mouth disease model with treatment and vaccination interventions are studied. We calculated the basic reproduction number and proved the global stability of di...In this article, the transmission dynamics of a Hand-Foot-Mouth disease model with treatment and vaccination interventions are studied. We calculated the basic reproduction number and proved the global stability of disease-free equilibrium when R0 R0 > 1. Meanwhile, we obtained the optimal control strategies minimizing the cost of intervention and minimizing the infected person. We also give some numerical simulations to verify our theoretical results.展开更多
In this paper, we propose the nonconforming virtual element method (NCVEM) discretization for the pointwise control constraint optimal control problem governed by elliptic equations. Based on the NCVEM approximation o...In this paper, we propose the nonconforming virtual element method (NCVEM) discretization for the pointwise control constraint optimal control problem governed by elliptic equations. Based on the NCVEM approximation of state equation and the variational discretization of control variables, we construct a virtual element discrete scheme. For the state, adjoint state and control variable, we obtain the corresponding prior estimate in H<sup>1</sup> and L<sup>2</sup> norms. Finally, some numerical experiments are carried out to support the theoretical results.展开更多
Currently, the power electronics-based devices, includinglarge-scale non-synchronized generators and reactivepower compensators, are widely used in power grids. This helpsintroduce the coupling interactions between th...Currently, the power electronics-based devices, includinglarge-scale non-synchronized generators and reactivepower compensators, are widely used in power grids. This helpsintroduce the coupling interactions between the devices andthe power grid, resulting in a new sub-synchronous oscillationphenomenon. It is a critical element for the stability operation ofthe power grid and its devices. In this paper, the sub-synchronousoscillation phenomenon of the power grid connected with largescalewind power generation is analyzed in detail. Then, inorder to damp the sub-synchronous oscillation, a coordinateddamping optimization control strategy for wind power generatorsand their reactive power compensators is proposed. The proposedcoordinated control strategy tracks the sub-synchronousoscillation current signal to correct the corresponding controlsignal, which increases the damping of power electronics. Theresponse characteristics of the proposed control strategy areanalyzed, and a self-optimization parameter tuning method basedon sensitivity analysis is proposed. The simulation results validatethe effectiveness and the availability of the proposed controlstrategy.展开更多
Power flow optimization control,which governs the energy flow among engine,battery,and motor,plays a very important role in plug-in hybrid electric vehicles(PHEVs).Its performance directly affects the fuel economy of ...Power flow optimization control,which governs the energy flow among engine,battery,and motor,plays a very important role in plug-in hybrid electric vehicles(PHEVs).Its performance directly affects the fuel economy of PHEVs.For the purpose of improving fuel economy,the electric system including battery and motor will be frequently scheduled,which would affect battery life.Therefore,a multi-objective optimization mechanism taking fuel economy and battery life into account is necessary,which is also a research focus in field of hybrid vehicles.Motivated by this issue,this paper proposes a multi-objective power flow optimization control strategy for a power split PHEV using game theory.Firstly,since the demand power of driver which is necessary for the power flow optimization control,cannot be known in advance,the demand power of driver can be modelled using a Markov chain to obtain predicted demand power.Secondly,based on the predicted demand power,the multi-objective optimization control problem is transformed into a game problem.A novel non-cooperative game model between engine and battery is established,and the benefit function with fuel economy and battery life as the optimization objective is proposed.Thirdly,under the premise of satisfying various constraints,the participants of the above game maximize their own benefit function to obtain the Nash equilibrium,which comprises of optimal power split scheme.Finally,the proposed strategy is verified compared with two baseline strategies,and results show that the proposed strategy can reduce equivalent fuel consumption by about 15%compared with baseline strategy 1,and achieve similar fuel economy while greatly extend battery life simultaneously compared with baseline strategy 2.展开更多
The control of slurry pressure aiming to be consistent with the external water and earth pressure during shield tunnelling has great significance for face stability,especially in urban areas or underwater where the su...The control of slurry pressure aiming to be consistent with the external water and earth pressure during shield tunnelling has great significance for face stability,especially in urban areas or underwater where the surrounding environment is very sensitive to the fluctuation of slurry pressure.In this study,an optimal control method for slurry pressure during shield tunnelling is developed,which is composed of an identifier and a controller.The established identifier based on the random forest(RF)can describe the complex non-linear relationship between slurry pressure and its influencing factors.The proposed controller based on particle swarm optimization(PSO)can optimize the key factor to precisely control the slurry pressure at the normal state of advancement.A data set from Tsinghua Yuan Tunnel in China was used to train the RF model and several performance measures like R2,RMSE,etc.,were employed to evaluate.Then,the hybrid RF-PSO control method is adopted to optimize the control of slurry pressure.The good agreement between optimized slurry pressure and expected values demonstrates a high identifying and control precision.展开更多
A pseudospectral method is presented for direct trajectory optimization and costate estimation of infinite-horizon optimal control problems using global collocation at flipped Legendre-Gauss-Radau points which include...A pseudospectral method is presented for direct trajectory optimization and costate estimation of infinite-horizon optimal control problems using global collocation at flipped Legendre-Gauss-Radau points which include the end point +1.A distinctive feature of the method is that it uses a new smooth,strictly monotonically decreasing transformation to map the scaled left half-open interval τ∈(-1, +1] to the descending time interval t ∈(+∞, 0]. As a result, the singularity of collocation at point +1 associated with the commonly used transformation,which maps the scaled right half-open interval τ∈ [-1, +1) to the increasing time interval [0, +∞), is avoided. The costate and constraint multiplier estimates for the proposed method are rigorously derived by comparing the discretized necessary optimality conditions of a finite-horizon optimal control problem with the Karush-Kuhn-Tucker conditions of the resulting nonlinear programming problem from collocation. Another key feature of the proposed method is that it provides highly accurate approximation to the state and costate on the entire horizon, including approximation at t = +∞, with good numerical stability.Numerical results show that the method presented in this paper leads to the ability to determine highly accurate solutions to infinite-horizon optimal control problems.展开更多
The optimal control of multibody spacecraft during the stretching process of solar arrays is investigated,and a hybrid optimization strategy based on Gauss pseudospectral method(GPM) and direct shooting method(DSM...The optimal control of multibody spacecraft during the stretching process of solar arrays is investigated,and a hybrid optimization strategy based on Gauss pseudospectral method(GPM) and direct shooting method(DSM) is presented. First, the elastic deformation of flexible solar arrays was described approximately by the assumed mode method, and a dynamic model was established by the second Lagrangian equation. Then, the nonholonomic motion planning problem is transformed into a nonlinear programming problem by using GPM. By giving fewer LG points, initial values of the state variables and control variables were obtained. A serial optimization framework was adopted to obtain the approximate optimal solution from a feasible solution. Finally, the control variables were discretized at LG points, and the precise optimal control inputs were obtained by DSM. The optimal trajectory of the system can be obtained through numerical integration. Through numerical simulation, the stretching process of solar arrays is stable with no detours, and the control inputs match the various constraints of actual conditions.The results indicate that the method is effective with good robustness.展开更多
This paper studies a novel distributed optimization problem that aims to minimize the sum of the non-convex objective functionals of the multi-agent network under privacy protection, which means that the local objecti...This paper studies a novel distributed optimization problem that aims to minimize the sum of the non-convex objective functionals of the multi-agent network under privacy protection, which means that the local objective of each agent is unknown to others. The above problem involves complexity simultaneously in the time and space aspects. Yet existing works about distributed optimization mainly consider privacy protection in the space aspect where the decision variable is a vector with finite dimensions. In contrast, when the time aspect is considered in this paper, the decision variable is a continuous function concerning time. Hence, the minimization of the overall functional belongs to the calculus of variations. Traditional works usually aim to seek the optimal decision function. Due to privacy protection and non-convexity, the Euler-Lagrange equation of the proposed problem is a complicated partial differential equation.Hence, we seek the optimal decision derivative function rather than the decision function. This manner can be regarded as seeking the control input for an optimal control problem, for which we propose a centralized reinforcement learning(RL) framework. In the space aspect, we further present a distributed reinforcement learning framework to deal with the impact of privacy protection. Finally, rigorous theoretical analysis and simulation validate the effectiveness of our framework.展开更多
Aimed at infinite horizon optimal control problems of discrete time-varying nonlinear systems,in this paper,a new iterative adaptive dynamic programming algorithm,which is the discrete-time time-varying policy iterati...Aimed at infinite horizon optimal control problems of discrete time-varying nonlinear systems,in this paper,a new iterative adaptive dynamic programming algorithm,which is the discrete-time time-varying policy iteration(DTTV)algorithm,is developed.The iterative control law is designed to update the iterative value function which approximates the index function of optimal performance.The admissibility of the iterative control law is analyzed.The results show that the iterative value function is non-increasingly convergent to the Bellman-equation optimal solution.To implement the algorithm,neural networks are employed and a new implementation structure is established,which avoids solving the generalized Bellman equation in each iteration.Finally,the optimal control laws for torsional pendulum and inverted pendulum systems are obtained by using the DTTV policy iteration algorithm,where the mass and pendulum bar length are permitted to be time-varying parameters.The effectiveness of the developed method is illustrated by numerical results and comparisons.展开更多
基金Project supported by the National Key Basic Research Program of China (Grant No 2006CB806001)the Knowledge Innovation Program of the Chinese Academy of Sciences (Grant No KGCX-YW-417-2)Shanghai Commission of Science and Technology,China (Grant No 07JC14055)
文摘We design three kinds of photonic crystal fibres (PCF) with two zero-dispersion wavelengths (ZDWs) using the improved full vector index method (FVIM) and finite-difference frequency domain (FDFD} techniques. Based on these designed fibres, the effect of fibre structure, pump power and wavelength on the modulation instability (MI) gain in the anomalous dispersion region close to the second ZDW of the PCFs is comprehensively analysed in this paper. The analytical results show that an optimal MI gain can be obtained when the optimal pump wavelength (1530 nm) is slightly shorter than the second ZDW (1538 nm) and the optimal pump power is 250 W. Importantly, the total MI gain bandwidth has been increased to 260 nm for the first time, so far as we know, for an optimally-designed fibre with ∧ = 1.4 nm and d/∧ = 0.676, and the gain profile became much smoother. The optimal pump wavelength relies on the second ZDW of the PCF whereas the optimal pump power depends on the corporate operation of the optimal fibre structure and optimal pump wavelength, which is important in designing the most appropriate PCF to attain higher broadband and gain amplification.
文摘The combination of structural health monitoring and vibration control is of great importance to provide components of smart structures.While synthetic algorithms have been proposed,adaptive control that is compatible with changing conditions still needs to be used,and time-varying systems are required to be simultaneously estimated with the application of adaptive control.In this research,the identification of structural time-varying dynamic characteristics and optimized simple adaptive control are integrated.First,reduced variations of physical parameters are estimated online using the multiple forgetting factor recursive least squares(MFRLS)method.Then,the energy from the structural vibration is simultaneously specified to optimize the control force with the identified parameters to be operational.Optimization is also performed based on the probability density function of the energy under the seismic excitation at any time.Finally,the optimal control force is obtained by the simple adaptive control(SAC)algorithm and energy coefficient.A numerical example and benchmark structure are employed to investigate the efficiency of the proposed approach.The simulation results revealed the effectiveness of the integrated online identification and optimal adaptive control in systems.
基金supported in part by the National Natural Science Foundation of China(62222301, 62073085, 62073158, 61890930-5, 62021003)the National Key Research and Development Program of China (2021ZD0112302, 2021ZD0112301, 2018YFC1900800-5)Beijing Natural Science Foundation (JQ19013)。
文摘Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.
基金This work was supported in part by the Science and Technology Major Project of Guangxi under Grant AA22068001in part by the Key Research and Development Program of Guangxi AB21196029+3 种基金in part by the Project of National Natural Science Foundation of China 51965012in part by the Scientific Research and TechnologyDevelopment in Liuzhou 2022AAA0102,2021AAA0104 and 2021AAA0112in part by Agricultural Science and Technology Innovation and Extension Special Project of Jiangsu Province NJ2021-21,in part by the Guangxi Key Laboratory of Manufacturing System and Advanced Manufacturing Technology,in part by the Guilin University of Electronic Technology 20-065-40-004Zin part by the Innovation Project of GUET Graduate Education 2022YCXS017.
文摘The reduction of fuel consumption in engines is always considered of vital importance.Along these lines,in this work,this goal was attained by optimizing the heavy-duty commercial vehicle engine control strategy.More specifically,at first,a general first principles model for heavy-duty commercial vehicles and a transient fuel consumptionmodel for heavy-duty commercial vehicles were developed and the parameters were adjusted to fit the empirical data.The accuracy of the proposed modelwas demonstrated fromthe stage and the final results.Next,the control optimization problem resulting in low fuel consumption in heavy commercial vehicles was described,with minimal fuel usage as the optimization goal and throttle opening as the control variable.Then,a time-continuous engine management approach was assessed.Next,the factors that influence low fuel consumption in heavy-duty commercial vehicles were systematically examined.To reduce the computing complexity,the control strategies related to the time constraints of the engine were parametrized using three different methods.The most effective solution was obtained by applying a global optimization strategy because the constrained optimization problem was nonlinear.Finally,the effectiveness of the low-fuel consumption engine control strategy was demonstrated by comparing the simulated and field test results.
基金Supported by Shanghai Municipal Natural Science Foundation of China (Grant No.19ZR1418600)。
文摘Axial flux permanent magnet synchronous motors(AFPMSMs)have been widely used in wind-power generation,electric vehicles,aircraft,and other renewable-energy applications owing to their high power density,operating efficiency,and integrability.To facilitate comprehensive research on AFPMSM,this article reviews the developments in the research on the design and control optimization of AFPMSMs.First,the basic topologies of AFPMSMs are introduced and classified.Second,the key points of the design optimization of core and coreless AFPMSMs are summarized from the aspects of parameter design,structure design,and material optimization.Third,because efficiency improvement is an issue that needs to be addressed when AFPMSMs are applied to electric or other vehicles,the development status of efficiency-optimization control strategies is reviewed.Moreover,control strategies proposed to suppress torque ripple caused by the small inductance of disc coreless permanent magnet synchronous motors(DCPMSMs)are summarized.An overview of the rotor-synchronization control strategies for disc contra-rotating permanent magnet synchronous motors(CRPMSMs)is presented.Finally,the current difficulties and development trends revealed in this review are discussed.
基金supported by Vicerrectoría de Investigación y Extensión of Universidad Industrial de Santander,Colombia,project 3704.
文摘In this paper we study a bilinear optimal control problem for a diffusive Lotka-Volterra competition model with chemo-repulsion in a bounded domain of ℝ^(ℕ),N=2,3.This model describes the competition of two species in which one of them avoid encounters with rivals through a chemo-repulsion mechanism.We prove the existence and uniqueness of weak-strong solutions,and then we analyze the existence of a global optimal solution for a related bilinear optimal control problem,where the control is acting on the chemical signal.Posteriorly,we derive first-order optimality conditions for local optimal solutions using the Lagrange multipliers theory.Finally,we propose a discrete approximation scheme of the optimality system based on the gradient method,which is validated with some computational experiments.
基金This work was supported by the National Natural Science Foundations of China(Grant Nos.12275033,61973317,and 12274470)the Natural Science Foundation of Hunan Province for Distinguished Young Scholars(Grant No.2022JJ10070)+1 种基金the Natural Science Foundation of Hunan Province(Grant No.2022JJ30582)the Scientific Research Fund of Hunan Provincial Education Department(Grant No.20A025).
文摘We present an optimal and robust quantum control method for efficient population transfer in asymmetric double quantum-dot molecules.We derive a long-duration control scheme that allows for highly efficient population transfer by accurately controlling the amplitude of a narrow-bandwidth pulse.To overcome fluctuations in control field parameters,we employ a frequency-domain quantum optimal control theory method to optimize the spectral phase of a single pulse with broad bandwidth while preserving the spectral amplitude.It is shown that this spectral-phase-only optimization approach can successfully identify robust and optimal control fields,leading to efficient population transfer to the target state while concurrently suppressing population transfer to undesired states.The method demonstrates resilience to fluctuations in control field parameters,making it a promising approach for reliable and efficient population transfer in practical applications.
文摘This paper presents a novel sequential inverse optimal control(SIOC)method for discrete-time systems,which calculates the unknown weight vectors of the cost function in real time using the input and output of an optimally controlled discrete-time system.The proposed method overcomes the limitations of previous approaches by eliminating the need for the invertible Jacobian assumption.It calculates the possible-solution spaces and their intersections sequentially until the dimension of the intersection space decreases to one.The remaining one-dimensional vector of the possible-solution space’s intersection represents the SIOC solution.The paper presents clear conditions for convergence and addresses the issue of noisy data by clarifying the conditions for the singular values of the matrices that relate to the possible-solution space.The effectiveness of the proposed method is demonstrated through simulation results.
基金supported in part by the National Key R&D Program of China under Grants 2021YFE0206100in part by the National Natural Science Foundation of China under Grant 62073321+2 种基金in part by National Defense Basic Scientific Research Program JCKY2019203C029in part by the Science and Technology Development Fund,Macao SAR under Grants FDCT-22-009-MISE,0060/2021/A2 and 0015/2020/AMJin part by the financial support from the National Defense Basic Scientific Research Project(JCKY2020130C025).
文摘In this paper,a new optimal adaptive backstepping control approach for nonlinear systems under deception attacks via reinforcement learning is presented in this paper.The existence of nonlinear terms in the studied system makes it very difficult to design the optimal controller using traditional methods.To achieve optimal control,RL algorithm based on critic–actor architecture is considered for the nonlinear system.Due to the significant security risks of network transmission,the system is vulnerable to deception attacks,which can make all the system state unavailable.By using the attacked states to design coordinate transformation,the harm brought by unknown deception attacks has been overcome.The presented control strategy can ensure that all signals in the closed-loop system are semi-globally ultimately bounded.Finally,the simulation experiment is shown to prove the effectiveness of the strategy.
文摘In this paper, the matrix Riccati equation is considered. There is no general way for solving the matrix Riccati equation despite the many fields to which it applies. While scalar Riccati equation has been studied thoroughly, matrix Riccati equation of which scalar Riccati equations is a particular case, is much less investigated. This article proposes a change of variable that allows to find explicit solution of the Matrix Riccati equation. We then apply this solution to Optimal Control.
文摘In this paper we study optimal advertising problems that model the introduction of a new product into the market in the presence of carryover effects of the advertisement and with memory effects in the level of goodwill. In particular, we let the dynamics of the product goodwill to depend on the past, and also on past advertising efforts. We treat the problem by means of the stochastic Pontryagin maximum principle, that here is considered for a class of problems where in the state equation either the state or the control depend on the past. Moreover the control acts on the martingale term and the space of controls U can be chosen to be non-convex but now the space of controls U can be chosen to be non-convex. The maximum principle is thus formulated using a first-order adjoint Backward Stochastic Differential Equations (BSDEs), which can be explicitly computed due to the specific characteristics of the model, and a second-order adjoint relation.
文摘In this article, the transmission dynamics of a Hand-Foot-Mouth disease model with treatment and vaccination interventions are studied. We calculated the basic reproduction number and proved the global stability of disease-free equilibrium when R0 R0 > 1. Meanwhile, we obtained the optimal control strategies minimizing the cost of intervention and minimizing the infected person. We also give some numerical simulations to verify our theoretical results.
文摘In this paper, we propose the nonconforming virtual element method (NCVEM) discretization for the pointwise control constraint optimal control problem governed by elliptic equations. Based on the NCVEM approximation of state equation and the variational discretization of control variables, we construct a virtual element discrete scheme. For the state, adjoint state and control variable, we obtain the corresponding prior estimate in H<sup>1</sup> and L<sup>2</sup> norms. Finally, some numerical experiments are carried out to support the theoretical results.
基金the NationalNatural Science Foundation of China under Grant No.51577174.
文摘Currently, the power electronics-based devices, includinglarge-scale non-synchronized generators and reactivepower compensators, are widely used in power grids. This helpsintroduce the coupling interactions between the devices andthe power grid, resulting in a new sub-synchronous oscillationphenomenon. It is a critical element for the stability operation ofthe power grid and its devices. In this paper, the sub-synchronousoscillation phenomenon of the power grid connected with largescalewind power generation is analyzed in detail. Then, inorder to damp the sub-synchronous oscillation, a coordinateddamping optimization control strategy for wind power generatorsand their reactive power compensators is proposed. The proposedcoordinated control strategy tracks the sub-synchronousoscillation current signal to correct the corresponding controlsignal, which increases the damping of power electronics. Theresponse characteristics of the proposed control strategy areanalyzed, and a self-optimization parameter tuning method basedon sensitivity analysis is proposed. The simulation results validatethe effectiveness and the availability of the proposed controlstrategy.
基金the National Natural Science Foundation of China(Grant Nos.51975048,U1764257 and 51705480)the Beijing Institute of Technology Research Fund Program for Young Scholars。
文摘Power flow optimization control,which governs the energy flow among engine,battery,and motor,plays a very important role in plug-in hybrid electric vehicles(PHEVs).Its performance directly affects the fuel economy of PHEVs.For the purpose of improving fuel economy,the electric system including battery and motor will be frequently scheduled,which would affect battery life.Therefore,a multi-objective optimization mechanism taking fuel economy and battery life into account is necessary,which is also a research focus in field of hybrid vehicles.Motivated by this issue,this paper proposes a multi-objective power flow optimization control strategy for a power split PHEV using game theory.Firstly,since the demand power of driver which is necessary for the power flow optimization control,cannot be known in advance,the demand power of driver can be modelled using a Markov chain to obtain predicted demand power.Secondly,based on the predicted demand power,the multi-objective optimization control problem is transformed into a game problem.A novel non-cooperative game model between engine and battery is established,and the benefit function with fuel economy and battery life as the optimization objective is proposed.Thirdly,under the premise of satisfying various constraints,the participants of the above game maximize their own benefit function to obtain the Nash equilibrium,which comprises of optimal power split scheme.Finally,the proposed strategy is verified compared with two baseline strategies,and results show that the proposed strategy can reduce equivalent fuel consumption by about 15%compared with baseline strategy 1,and achieve similar fuel economy while greatly extend battery life simultaneously compared with baseline strategy 2.
基金This work was supported by the Fundamental Research Funds for the Central Universities(2020YJS141)the Key Project of High-speed Rail Joint Fund of National Natural Science Foundation of China under Grant No.U1834208.
文摘The control of slurry pressure aiming to be consistent with the external water and earth pressure during shield tunnelling has great significance for face stability,especially in urban areas or underwater where the surrounding environment is very sensitive to the fluctuation of slurry pressure.In this study,an optimal control method for slurry pressure during shield tunnelling is developed,which is composed of an identifier and a controller.The established identifier based on the random forest(RF)can describe the complex non-linear relationship between slurry pressure and its influencing factors.The proposed controller based on particle swarm optimization(PSO)can optimize the key factor to precisely control the slurry pressure at the normal state of advancement.A data set from Tsinghua Yuan Tunnel in China was used to train the RF model and several performance measures like R2,RMSE,etc.,were employed to evaluate.Then,the hybrid RF-PSO control method is adopted to optimize the control of slurry pressure.The good agreement between optimized slurry pressure and expected values demonstrates a high identifying and control precision.
基金supported by Natural Science Basic Research Plan in Shaanxi Province of China(2014JQ8366)Aeronautical Science Foundation of China(20120853007)
文摘A pseudospectral method is presented for direct trajectory optimization and costate estimation of infinite-horizon optimal control problems using global collocation at flipped Legendre-Gauss-Radau points which include the end point +1.A distinctive feature of the method is that it uses a new smooth,strictly monotonically decreasing transformation to map the scaled left half-open interval τ∈(-1, +1] to the descending time interval t ∈(+∞, 0]. As a result, the singularity of collocation at point +1 associated with the commonly used transformation,which maps the scaled right half-open interval τ∈ [-1, +1) to the increasing time interval [0, +∞), is avoided. The costate and constraint multiplier estimates for the proposed method are rigorously derived by comparing the discretized necessary optimality conditions of a finite-horizon optimal control problem with the Karush-Kuhn-Tucker conditions of the resulting nonlinear programming problem from collocation. Another key feature of the proposed method is that it provides highly accurate approximation to the state and costate on the entire horizon, including approximation at t = +∞, with good numerical stability.Numerical results show that the method presented in this paper leads to the ability to determine highly accurate solutions to infinite-horizon optimal control problems.
基金supported by the National Natural Science Foundation of China (11472058)
文摘The optimal control of multibody spacecraft during the stretching process of solar arrays is investigated,and a hybrid optimization strategy based on Gauss pseudospectral method(GPM) and direct shooting method(DSM) is presented. First, the elastic deformation of flexible solar arrays was described approximately by the assumed mode method, and a dynamic model was established by the second Lagrangian equation. Then, the nonholonomic motion planning problem is transformed into a nonlinear programming problem by using GPM. By giving fewer LG points, initial values of the state variables and control variables were obtained. A serial optimization framework was adopted to obtain the approximate optimal solution from a feasible solution. Finally, the control variables were discretized at LG points, and the precise optimal control inputs were obtained by DSM. The optimal trajectory of the system can be obtained through numerical integration. Through numerical simulation, the stretching process of solar arrays is stable with no detours, and the control inputs match the various constraints of actual conditions.The results indicate that the method is effective with good robustness.
基金supported in part by the National Natural Science Foundation of China(NSFC)(61773260)the Ministry of Science and Technology (2018YFB130590)。
文摘This paper studies a novel distributed optimization problem that aims to minimize the sum of the non-convex objective functionals of the multi-agent network under privacy protection, which means that the local objective of each agent is unknown to others. The above problem involves complexity simultaneously in the time and space aspects. Yet existing works about distributed optimization mainly consider privacy protection in the space aspect where the decision variable is a vector with finite dimensions. In contrast, when the time aspect is considered in this paper, the decision variable is a continuous function concerning time. Hence, the minimization of the overall functional belongs to the calculus of variations. Traditional works usually aim to seek the optimal decision function. Due to privacy protection and non-convexity, the Euler-Lagrange equation of the proposed problem is a complicated partial differential equation.Hence, we seek the optimal decision derivative function rather than the decision function. This manner can be regarded as seeking the control input for an optimal control problem, for which we propose a centralized reinforcement learning(RL) framework. In the space aspect, we further present a distributed reinforcement learning framework to deal with the impact of privacy protection. Finally, rigorous theoretical analysis and simulation validate the effectiveness of our framework.
基金supported in part by Fundamental Research Funds for the Central Universities(2022JBZX024)in part by the National Natural Science Foundation of China(61872037,61273167)。
文摘Aimed at infinite horizon optimal control problems of discrete time-varying nonlinear systems,in this paper,a new iterative adaptive dynamic programming algorithm,which is the discrete-time time-varying policy iteration(DTTV)algorithm,is developed.The iterative control law is designed to update the iterative value function which approximates the index function of optimal performance.The admissibility of the iterative control law is analyzed.The results show that the iterative value function is non-increasingly convergent to the Bellman-equation optimal solution.To implement the algorithm,neural networks are employed and a new implementation structure is established,which avoids solving the generalized Bellman equation in each iteration.Finally,the optimal control laws for torsional pendulum and inverted pendulum systems are obtained by using the DTTV policy iteration algorithm,where the mass and pendulum bar length are permitted to be time-varying parameters.The effectiveness of the developed method is illustrated by numerical results and comparisons.