In the goal optimization and control optimization process the problems with common artificial neural network algorithm are unsure convergence, insufficient post-training network precision, and slow training speed, in ...In the goal optimization and control optimization process the problems with common artificial neural network algorithm are unsure convergence, insufficient post-training network precision, and slow training speed, in which partial minimum value question tends to occur. This paper conducted an in-depth study on the causes of the limi-tations of the algorithm, presented a rapid artificial neural network algorithm, which is characterized by integrating multiple algorithms and by using their complementary advan-tages. The salient feature of the method is self-organization, which can effectively prevent the optimized results from tending to be partial minimum values. Overall optimization can be achieved with this method, goal function can be searched for in overall scope. With op-timization control of coal mine ventilator as a practical application, the paper proves that by integrating multiple artificial neural network algorithms, best control optimization and goal optimized can be achieved.展开更多
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
This study aims to optimize the inbound traffic flow on on-ramps by considering low time costs,good speed stability,and high driving safety for mixed traffic flow.The optimal inlet gap is identified in advance,and tra...This study aims to optimize the inbound traffic flow on on-ramps by considering low time costs,good speed stability,and high driving safety for mixed traffic flow.The optimal inlet gap is identified in advance,and trajectory guidance for vehicles entering the gap is determined under safety constraints.Based on the initial state and sequence of vehicles entering the merging area,individual vehicle trajectories are optimized sequentially.An optimization model and method for ramp entry trajectories in mixed traffic flow are developed,incorporating on-ramp vehicle entry sequencing and ordinary vehicle trajectory prediction.Key performance indicators,including driving safety,total travel time,parking wait probability,and trajectory smoothness,are compared and analyzed to evaluate the proposed approach.展开更多
Compared to other energy sources,nuclear reactors offer several advantages as a spacecraft power source,including compact size,high power density,and long operating life.These qualities make nuclear power an ideal ene...Compared to other energy sources,nuclear reactors offer several advantages as a spacecraft power source,including compact size,high power density,and long operating life.These qualities make nuclear power an ideal energy source for future deep space exploration.A whole system model of the space nuclear reactor consisting of the reactor neutron kinetics,reactivity control,reactor heat transfer,heat exchanger,and thermoelectric converter was developed.In addition,an electrical power control system was designed based on the developed dynamic model.The GRS method was used to quantitatively calculate the uncertainty of coupling parameters of the neutronics,thermal-hydraulics,and control system for the space reactor.The Spearman correlation coefficient was applied in the sensitivity analysis of system input parameters to output parameters.The calculation results showed that the uncertainty of the output parameters caused by coupling parameters had the most considerable variation,with a relative standard deviation<2.01%.Effective delayed neutron fraction was most sensitive to electrical power.To obtain optimal control performance,the non-dominated sorting genetic algorithm method was employed to optimize the controller parameters based on the uncertainty quantification calculation.Two typical transient simulations were conducted to test the adaptive ability of the optimized controller in the uncertainty dynamic system,including 100%full power(FP)to 90%FP step load reduction transient and 5%FP/min linear variable load transient.The results showed that,considering the influence of system uncertainty,the optimized controller could improve the response speed and load following accuracy of electrical power control,in which the effectiveness and superiority have been verified.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
At present, large centrifugal compressors are widely used in domestic petrochemical plants. This paper describes the control status of compressor control system in domestic petrochemical plants, the optimization schem...At present, large centrifugal compressors are widely used in domestic petrochemical plants. This paper describes the control status of compressor control system in domestic petrochemical plants, the optimization scheme to realize energy-saving control and the economic benefits brought to enterprises after control optimization.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
PartⅠhas illustrated the procedures to apply the Linkage Learning Genetic Algorithm(LLGA)in Gas Turbine Engine(GTE)controller gains tuning and generated the optimization results for runway conditions from idle to tak...PartⅠhas illustrated the procedures to apply the Linkage Learning Genetic Algorithm(LLGA)in Gas Turbine Engine(GTE)controller gains tuning and generated the optimization results for runway conditions from idle to takeoff.However,the total pressure and temperature of the engine inlet vary as the changing of altitude and Mach number,which would lead to the variation in fuel flow supply regulation.As a result,the optimized gains in runway might not be suitable for other flight conditions.In order to maintain the optimal control performance,the GTE controller gains should be adjusted according to the flight conditions.This paper extends the application of the LLGA method to other flight conditions and then simulates a complete flight mission with different gains and weather condition configurations.For this purpose,the control parameters in the Simulink model of the GTE controller are first corrected by the weather condition in altitude.Then,a typical flight mission is defined and divided into different flight segments based on the altitude and Mach number configuration.One representative point is selected from each segment as the datum point for optimization process.After this step,the LLGA method is used to find the best gains combinations for different flight conditions and the differences in optimization effects for different flight conditions are analyzed subsequently.The simulation results show that the optimization effect of the control performance of each flight condition is dependent on the value of(θδ)~(1/2)and the optimal K_(pla)in some flight conditions is approximately equal to p hd times of the Kplavalue in sea level standard condition.Finally,the complete flight mission is simulated with different gains and weather condition configurations.The simulation results show that the engine performance has been greatly improved after optimization by LLGA in the transient state and the high altitude conditions.In other steady states,the optimization effect is not very obvious.展开更多
The methylotrophic yeast Pichia pastoris is a highly successful system for production of a variety of heterologous proteins due to its unique features/abilities for effective protein expression, and tremendous efforts...The methylotrophic yeast Pichia pastoris is a highly successful system for production of a variety of heterologous proteins due to its unique features/abilities for effective protein expression, and tremendous efforts have been made to increase heterologous protein productivity by P. pastoris in recent years. When new engineered yeast strains are constructed and are ready to use tot industrial protein production, process control and optimization techniques should be applied to improve the fermentation performance in the following aspects： （1） increase recombinant cell concentrations in fermentor to high density during growth phase; （2） effectively induce heterologous proteins by enhancing/stabilizing titers or concentrations of the proteins during induction phase; （3） decrease operation costs by relieving the working loads of heat-exchange and oxygen supply. This article reviews and discusses the key and commonly used techniques in heterologous protein production by P. pastoris, with the focus on optimizations of fermentation media and basic operation conditions, development of optimal glycerol feeding strategies for achieving high density cultivation of P. pastoris and effective heterologous protein induction methods by regulating specific growth rate, methanol concentration, temperatures, mixture ratio of multi-carbon substrates, etc. Metabolic analysis for recombinant protein production by P. pastoris is also introduced to interpret the mechanism of sub-optimal heterologous protein production and to explore further optimal expression methods.展开更多
The goal of this paper is to design a model-free optimal controller for the multirate system based on reinforcement learning.Sampled-data control systems are widely used in the industrial production process and multir...The goal of this paper is to design a model-free optimal controller for the multirate system based on reinforcement learning.Sampled-data control systems are widely used in the industrial production process and multirate sampling has attracted much attention in the study of the sampled-data control theory.In this paper,we assume the sampling periods for state variables are different from periods for system inputs.Under this condition,we can obtain an equivalent discrete-time system using the lifting technique.Then,we provide an algorithm to solve the linear quadratic regulator(LQR)control problem of multirate systems with the utilization of matrix substitutions.Based on a reinforcement learning method,we use online policy iteration and off-policy algorithms to optimize the controller for multirate systems.By using the least squares method,we convert the off-policy algorithm into a model-free reinforcement learning algorithm,which only requires the input and output data of the system.Finally,we use an example to illustrate the applicability and efficiency of the model-free algorithm above mentioned.展开更多
An approach for parameter estimation of proportional-integral-derivative(PID) control system using a new nonlinear programming(NLP) algorithm was proposed.SQP/IIPM algorithm is a sequential quadratic programming(SQP) ...An approach for parameter estimation of proportional-integral-derivative(PID) control system using a new nonlinear programming(NLP) algorithm was proposed.SQP/IIPM algorithm is a sequential quadratic programming(SQP) based algorithm that derives its search directions by solving quadratic programming(QP) subproblems via an infeasible interior point method(IIPM) and evaluates step length adaptively via a simple line search and/or a quadratic search algorithm depending on the termination of the IIPM solver.The task of tuning PI/PID parameters for the first-and second-order systems was modeled as constrained NLP problem. SQP/IIPM algorithm was applied to determining the optimum parameters for the PI/PID control systems.To assess the performance of the proposed method,a Matlab simulation of PID controller tuning was conducted to compare the proposed SQP/IIPM algorithm with the gain and phase margin(GPM) method and Ziegler-Nichols(ZN) method.The results reveal that,for both step and impulse response tests,the PI/PID controller using SQP/IIPM optimization algorithm consistently reduce rise time,settling-time and remarkably lower overshoot compared to GPM and ZN methods,and the proposed method improves the robustness and effectiveness of numerical optimization of PID control systems.展开更多
In order to effectively achieve torque demand in electric vehicles （EVs）, this paper presents a torque control strategy based on model predictive control （MPC） for permanent magnet synchronous motor （PMSM） drive...In order to effectively achieve torque demand in electric vehicles （EVs）, this paper presents a torque control strategy based on model predictive control （MPC） for permanent magnet synchronous motor （PMSM） driven by a two-level three-phase inverter. A centralized control strategy is established in the MPC framework to track the torque demand and reduce energy loss, by directly optimizing the switch states of inverter. To fast determine the optimal control sequence in predictive process, a searching tree is built to look for optimal inputs by dynamic programming （DP） algorithm on the basis of the principle of optimality. Then we design a pruning method to check the candidate inputs that can enter the next predictive loop in order to decrease the computational burden of evaluation of input sequences. Finally, the simulation results on different conditions indicate that the proposed strategy can achieve a tradeoff between control performance and computational efficiency.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
The operation variables,including feed rate of ore slurry,caustic solution and live steams in the double-stream alumina digestion process,determine the product quality,process costs and the environment pollution.Previ...The operation variables,including feed rate of ore slurry,caustic solution and live steams in the double-stream alumina digestion process,determine the product quality,process costs and the environment pollution.Previously,they were set by the technical workers according to the offline analysis results and an empirical formula,which leads to unstable process indices and high consumption frequently.So,a multi-objective optimization model is built to maintain the balance between resource consumptions and process indices by taking technical indices and energy efficiency as objectives,where the key technical indices are predicted based on the digestion kinetics of diaspore.A multi-objective state transition algorithm(MOSTA)is improved to solve the problem,in which a self-adaptive strategy is applied to dynamically adjust the operator factors of the MOSTA and dynamic infeasible threshold is used to handle constraints to enhance searching efficiency and ability of the algorithm.Then a rule based strategy is designed to make the final decision from the Pareto frontiers.The method is integrated into an optimal control system for the industrial digestion process and tested in the actual production.Results show that the proposed method can achieve the technical target while reducing the energy consumption.展开更多
The energy consumption of train operation occupies a large proportion of the total consumption of railway transportation.In order to improve the oper-ating energy utilization rate of trains,a multi-objective particle ...The energy consumption of train operation occupies a large proportion of the total consumption of railway transportation.In order to improve the oper-ating energy utilization rate of trains,a multi-objective particle swarm optimiza-tion(MPSO)algorithm with energy consumption,punctuality and parking accuracy as the objective and safety as the constraint is built.To accelerate its the convergence process,the train operation progression is divided into several modes according to the train speed-distance curve.A human-computer interactive particle swarm optimization algorithm is proposed,which presents the optimized results after a certain number of iterations to the decision maker,and the satisfac-tory outcomes can be obtained after a limited number of adjustments.The multi-objective particle swarm optimization(MPSO)algorithm is used to optimize the train operation process.An algorithm based on the important relationship between the objective and the preference information of the given reference points is sug-gested to overcome the shortcomings of the existing algorithms.These methods significantly increase the computational complexity and convergence of the algo-rithm.An adaptive fuzzy logic system that can simultaneously utilize experience information andfield data information is proposed to adjust the consequences of off-line optimization in real time,thereby eliminating the influence of uncertainty on train operation.After optimization and adjustment,the whole running time has been increased by 0.5 s,the energy consumption has been reduced by 12%,the parking accuracy has been increased by 8%,and the comprehensive performance has been enhanced.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
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
The unpowered high-speed vehicle experiences a significant coupling between the disciplines of aerodynamics and control due to its characteristics of high flight speed and extensive maneuverability within large airspa...The unpowered high-speed vehicle experiences a significant coupling between the disciplines of aerodynamics and control due to its characteristics of high flight speed and extensive maneuverability within large airspace.The conventional aircraft conceptual design process follows a sequential design approach,and there is an artificial separation between the disciplines of aerodynamics and control,neglecting the coupling effects arising from their interaction.As a result,this design process often requires extensive iterations over long periods when applied to high-speed vehicles,and may not be able to effectively achieve the desired design objectives.To enhance the overall performance and design efficiency of high-speed vehicles,this study integrates the concept of Active Control Technology(ACT)from modern aircraft into the philosophy of aerodynamic/control integrated optimization.Two integrated optimization strategies,with differences in coupling granularity,have been developed.Subsequently,these strategies are put into action on a biconical vehicle that operates at Mach 5.The results reveal that the comprehensive performance of the synthesis optimal model derived from the aerodynamic/control integrated optimization strategy is improved by 31.76%and 28.29%respectively compared to the base model under high-speed conditions,demonstrating the feasibility and effectiveness of the method and optimization strategies employed.Moreover,in comparison to the single-stage strategy,the multi-stage strategy takes into deeper consideration the impact of control capacity.As a result,the control performance of the synthesis opti-mal model derived from the multi-stage strategy improves by 13.99%,whereas the single-stage strategy only achieves a 5.79%improvement.This method enables a fruitful interaction between aerodynamic configuration design and control system design,leading to enhanced overall performance and design efficiency.Furthermore,it improves the controllability of high-speed vehicles,mitigating the risk of mission failure resulting from an ineffective control system.展开更多
A mathematical approach was proposed to investigate the impact of high penetration of large-scale photovoltaic park(LPP) on small-signal stability of a power network and design of hybrid controller for these units.A s...A mathematical approach was proposed to investigate the impact of high penetration of large-scale photovoltaic park(LPP) on small-signal stability of a power network and design of hybrid controller for these units.A systematic procedure was performed to obtain the complete model of a multi-machine power network including LPP.For damping of oscillations focusing on inter-area oscillatory modes,a hybrid controller for LPP was proposed.The performance of the suggested controller was tested using a 16-machine 5-area network.The results indicate that the proposed hybrid controller for LPP provides sufficient damping to the low-frequency modes of power system for a wide range of operating conditions.The method presented in this work effectively indentifies the impact of increased PV penetration and its controller on dynamic performance of multi-machine power network containing LPP.Simulation results demonstrate that the model presented can be used in designing of essential controllers for LPP.展开更多
基金Supported by the Science Foundation of the Liaoning Province(2004C011)
文摘In the goal optimization and control optimization process the problems with common artificial neural network algorithm are unsure convergence, insufficient post-training network precision, and slow training speed, in which partial minimum value question tends to occur. This paper conducted an in-depth study on the causes of the limi-tations of the algorithm, presented a rapid artificial neural network algorithm, which is characterized by integrating multiple algorithms and by using their complementary advan-tages. The salient feature of the method is self-organization, which can effectively prevent the optimized results from tending to be partial minimum values. Overall optimization can be achieved with this method, goal function can be searched for in overall scope. With op-timization control of coal mine ventilator as a practical application, the paper proves that by integrating multiple artificial neural network algorithms, best control optimization and goal optimized can be achieved.
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This study aims to optimize the inbound traffic flow on on-ramps by considering low time costs,good speed stability,and high driving safety for mixed traffic flow.The optimal inlet gap is identified in advance,and trajectory guidance for vehicles entering the gap is determined under safety constraints.Based on the initial state and sequence of vehicles entering the merging area,individual vehicle trajectories are optimized sequentially.An optimization model and method for ramp entry trajectories in mixed traffic flow are developed,incorporating on-ramp vehicle entry sequencing and ordinary vehicle trajectory prediction.Key performance indicators,including driving safety,total travel time,parking wait probability,and trajectory smoothness,are compared and analyzed to evaluate the proposed approach.
基金supported by the National Natural Science Foundation of China(12305185)Natural Science Foundation of Hunan Province,China(No.2023JJ50122)+1 种基金International Cooperative Research Project of the Ministry of Education,China(No.HZKY20220355)Scientific Research Foundation of the Education Department of Hunan Province,China(No.22A0307).
文摘Compared to other energy sources,nuclear reactors offer several advantages as a spacecraft power source,including compact size,high power density,and long operating life.These qualities make nuclear power an ideal energy source for future deep space exploration.A whole system model of the space nuclear reactor consisting of the reactor neutron kinetics,reactivity control,reactor heat transfer,heat exchanger,and thermoelectric converter was developed.In addition,an electrical power control system was designed based on the developed dynamic model.The GRS method was used to quantitatively calculate the uncertainty of coupling parameters of the neutronics,thermal-hydraulics,and control system for the space reactor.The Spearman correlation coefficient was applied in the sensitivity analysis of system input parameters to output parameters.The calculation results showed that the uncertainty of the output parameters caused by coupling parameters had the most considerable variation,with a relative standard deviation<2.01%.Effective delayed neutron fraction was most sensitive to electrical power.To obtain optimal control performance,the non-dominated sorting genetic algorithm method was employed to optimize the controller parameters based on the uncertainty quantification calculation.Two typical transient simulations were conducted to test the adaptive ability of the optimized controller in the uncertainty dynamic system,including 100%full power(FP)to 90%FP step load reduction transient and 5%FP/min linear variable load transient.The results showed that,considering the influence of system uncertainty,the optimized controller could improve the response speed and load following accuracy of electrical power control,in which the effectiveness and superiority have been verified.
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.
文摘At present, large centrifugal compressors are widely used in domestic petrochemical plants. This paper describes the control status of compressor control system in domestic petrochemical plants, the optimization scheme to realize energy-saving control and the economic benefits brought to enterprises after control optimization.
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.
文摘PartⅠhas illustrated the procedures to apply the Linkage Learning Genetic Algorithm(LLGA)in Gas Turbine Engine(GTE)controller gains tuning and generated the optimization results for runway conditions from idle to takeoff.However,the total pressure and temperature of the engine inlet vary as the changing of altitude and Mach number,which would lead to the variation in fuel flow supply regulation.As a result,the optimized gains in runway might not be suitable for other flight conditions.In order to maintain the optimal control performance,the GTE controller gains should be adjusted according to the flight conditions.This paper extends the application of the LLGA method to other flight conditions and then simulates a complete flight mission with different gains and weather condition configurations.For this purpose,the control parameters in the Simulink model of the GTE controller are first corrected by the weather condition in altitude.Then,a typical flight mission is defined and divided into different flight segments based on the altitude and Mach number configuration.One representative point is selected from each segment as the datum point for optimization process.After this step,the LLGA method is used to find the best gains combinations for different flight conditions and the differences in optimization effects for different flight conditions are analyzed subsequently.The simulation results show that the optimization effect of the control performance of each flight condition is dependent on the value of(θδ)~(1/2)and the optimal K_(pla)in some flight conditions is approximately equal to p hd times of the Kplavalue in sea level standard condition.Finally,the complete flight mission is simulated with different gains and weather condition configurations.The simulation results show that the engine performance has been greatly improved after optimization by LLGA in the transient state and the high altitude conditions.In other steady states,the optimization effect is not very obvious.
基金Supported by the Key Agricultral Technology Program of Shanghai Science & Technology Committee(073919108)MajorState Basic Research Development Program of China(2007CB714303)
文摘The methylotrophic yeast Pichia pastoris is a highly successful system for production of a variety of heterologous proteins due to its unique features/abilities for effective protein expression, and tremendous efforts have been made to increase heterologous protein productivity by P. pastoris in recent years. When new engineered yeast strains are constructed and are ready to use tot industrial protein production, process control and optimization techniques should be applied to improve the fermentation performance in the following aspects： （1） increase recombinant cell concentrations in fermentor to high density during growth phase; （2） effectively induce heterologous proteins by enhancing/stabilizing titers or concentrations of the proteins during induction phase; （3） decrease operation costs by relieving the working loads of heat-exchange and oxygen supply. This article reviews and discusses the key and commonly used techniques in heterologous protein production by P. pastoris, with the focus on optimizations of fermentation media and basic operation conditions, development of optimal glycerol feeding strategies for achieving high density cultivation of P. pastoris and effective heterologous protein induction methods by regulating specific growth rate, methanol concentration, temperatures, mixture ratio of multi-carbon substrates, etc. Metabolic analysis for recombinant protein production by P. pastoris is also introduced to interpret the mechanism of sub-optimal heterologous protein production and to explore further optimal expression methods.
基金This work was supported by National Key R&D Program of China(No.2018YFB1308404).
文摘The goal of this paper is to design a model-free optimal controller for the multirate system based on reinforcement learning.Sampled-data control systems are widely used in the industrial production process and multirate sampling has attracted much attention in the study of the sampled-data control theory.In this paper,we assume the sampling periods for state variables are different from periods for system inputs.Under this condition,we can obtain an equivalent discrete-time system using the lifting technique.Then,we provide an algorithm to solve the linear quadratic regulator(LQR)control problem of multirate systems with the utilization of matrix substitutions.Based on a reinforcement learning method,we use online policy iteration and off-policy algorithms to optimize the controller for multirate systems.By using the least squares method,we convert the off-policy algorithm into a model-free reinforcement learning algorithm,which only requires the input and output data of the system.Finally,we use an example to illustrate the applicability and efficiency of the model-free algorithm above mentioned.
基金Project(60874070) supported by the National Natural Science Foundation of ChinaProject(20070533131) supported by the National Research Foundation for the Doctoral Program of Higher Education of ChinaProject supported by the Scientific Research Foundation for the Returned Overseas Chinese Scholars,Ministry of Education of China
文摘An approach for parameter estimation of proportional-integral-derivative(PID) control system using a new nonlinear programming(NLP) algorithm was proposed.SQP/IIPM algorithm is a sequential quadratic programming(SQP) based algorithm that derives its search directions by solving quadratic programming(QP) subproblems via an infeasible interior point method(IIPM) and evaluates step length adaptively via a simple line search and/or a quadratic search algorithm depending on the termination of the IIPM solver.The task of tuning PI/PID parameters for the first-and second-order systems was modeled as constrained NLP problem. SQP/IIPM algorithm was applied to determining the optimum parameters for the PI/PID control systems.To assess the performance of the proposed method,a Matlab simulation of PID controller tuning was conducted to compare the proposed SQP/IIPM algorithm with the gain and phase margin(GPM) method and Ziegler-Nichols(ZN) method.The results reveal that,for both step and impulse response tests,the PI/PID controller using SQP/IIPM optimization algorithm consistently reduce rise time,settling-time and remarkably lower overshoot compared to GPM and ZN methods,and the proposed method improves the robustness and effectiveness of numerical optimization of PID control systems.
基金This work was supported by the NSFC Projects of International Cooperation and Exchanges （No. 61520106008）, the National Natural Science Foundation of China （Nos. 61503149, U1564207） and the Graduate Innovation Fund of Jilin University （No. 2016093）.
文摘In order to effectively achieve torque demand in electric vehicles （EVs）, this paper presents a torque control strategy based on model predictive control （MPC） for permanent magnet synchronous motor （PMSM） driven by a two-level three-phase inverter. A centralized control strategy is established in the MPC framework to track the torque demand and reduce energy loss, by directly optimizing the switch states of inverter. To fast determine the optimal control sequence in predictive process, a searching tree is built to look for optimal inputs by dynamic programming （DP） algorithm on the basis of the principle of optimality. Then we design a pruning method to check the candidate inputs that can enter the next predictive loop in order to decrease the computational burden of evaluation of input sequences. Finally, the simulation results on different conditions indicate that the proposed strategy can achieve a tradeoff between control performance and computational efficiency.
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Project(62073342)supported by the National Natural Science Foundation of ChinaProject(2014 AA 041803)supported by the Hi-tech Research and Development Program of China。
文摘The operation variables,including feed rate of ore slurry,caustic solution and live steams in the double-stream alumina digestion process,determine the product quality,process costs and the environment pollution.Previously,they were set by the technical workers according to the offline analysis results and an empirical formula,which leads to unstable process indices and high consumption frequently.So,a multi-objective optimization model is built to maintain the balance between resource consumptions and process indices by taking technical indices and energy efficiency as objectives,where the key technical indices are predicted based on the digestion kinetics of diaspore.A multi-objective state transition algorithm(MOSTA)is improved to solve the problem,in which a self-adaptive strategy is applied to dynamically adjust the operator factors of the MOSTA and dynamic infeasible threshold is used to handle constraints to enhance searching efficiency and ability of the algorithm.Then a rule based strategy is designed to make the final decision from the Pareto frontiers.The method is integrated into an optimal control system for the industrial digestion process and tested in the actual production.Results show that the proposed method can achieve the technical target while reducing the energy consumption.
基金supported by the project of science and technology of Henan province under Grant No.202102210134.
文摘The energy consumption of train operation occupies a large proportion of the total consumption of railway transportation.In order to improve the oper-ating energy utilization rate of trains,a multi-objective particle swarm optimiza-tion(MPSO)algorithm with energy consumption,punctuality and parking accuracy as the objective and safety as the constraint is built.To accelerate its the convergence process,the train operation progression is divided into several modes according to the train speed-distance curve.A human-computer interactive particle swarm optimization algorithm is proposed,which presents the optimized results after a certain number of iterations to the decision maker,and the satisfac-tory outcomes can be obtained after a limited number of adjustments.The multi-objective particle swarm optimization(MPSO)algorithm is used to optimize the train operation process.An algorithm based on the important relationship between the objective and the preference information of the given reference points is sug-gested to overcome the shortcomings of the existing algorithms.These methods significantly increase the computational complexity and convergence of the algo-rithm.An adaptive fuzzy logic system that can simultaneously utilize experience information andfield data information is proposed to adjust the consequences of off-line optimization in real time,thereby eliminating the influence of uncertainty on train operation.After optimization and adjustment,the whole running time has been increased by 0.5 s,the energy consumption has been reduced by 12%,the parking accuracy has been increased by 8%,and the comprehensive performance has been enhanced.
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 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 by the National Natural Science Foundation of China(Nos.92371201,52192633)the Natural Science Foundation of Shaanxi Province(No.2022JC-03)Chinese Aeronautical Foundation(No.ASFC-20220019070002)。
文摘The unpowered high-speed vehicle experiences a significant coupling between the disciplines of aerodynamics and control due to its characteristics of high flight speed and extensive maneuverability within large airspace.The conventional aircraft conceptual design process follows a sequential design approach,and there is an artificial separation between the disciplines of aerodynamics and control,neglecting the coupling effects arising from their interaction.As a result,this design process often requires extensive iterations over long periods when applied to high-speed vehicles,and may not be able to effectively achieve the desired design objectives.To enhance the overall performance and design efficiency of high-speed vehicles,this study integrates the concept of Active Control Technology(ACT)from modern aircraft into the philosophy of aerodynamic/control integrated optimization.Two integrated optimization strategies,with differences in coupling granularity,have been developed.Subsequently,these strategies are put into action on a biconical vehicle that operates at Mach 5.The results reveal that the comprehensive performance of the synthesis optimal model derived from the aerodynamic/control integrated optimization strategy is improved by 31.76%and 28.29%respectively compared to the base model under high-speed conditions,demonstrating the feasibility and effectiveness of the method and optimization strategies employed.Moreover,in comparison to the single-stage strategy,the multi-stage strategy takes into deeper consideration the impact of control capacity.As a result,the control performance of the synthesis opti-mal model derived from the multi-stage strategy improves by 13.99%,whereas the single-stage strategy only achieves a 5.79%improvement.This method enables a fruitful interaction between aerodynamic configuration design and control system design,leading to enhanced overall performance and design efficiency.Furthermore,it improves the controllability of high-speed vehicles,mitigating the risk of mission failure resulting from an ineffective control system.
文摘A mathematical approach was proposed to investigate the impact of high penetration of large-scale photovoltaic park(LPP) on small-signal stability of a power network and design of hybrid controller for these units.A systematic procedure was performed to obtain the complete model of a multi-machine power network including LPP.For damping of oscillations focusing on inter-area oscillatory modes,a hybrid controller for LPP was proposed.The performance of the suggested controller was tested using a 16-machine 5-area network.The results indicate that the proposed hybrid controller for LPP provides sufficient damping to the low-frequency modes of power system for a wide range of operating conditions.The method presented in this work effectively indentifies the impact of increased PV penetration and its controller on dynamic performance of multi-machine power network containing LPP.Simulation results demonstrate that the model presented can be used in designing of essential controllers for LPP.
