Abstract--The time-optimal control design of the double integrator is extended to the finite-time stabilization design that compensates both input saturation and input delay. With the aid of the Artstein＇s transforma...Abstract--The time-optimal control design of the double integrator is extended to the finite-time stabilization design that compensates both input saturation and input delay. With the aid of the Artstein＇s transformation, the problem is reduced to assigning a saturated finite-time stabilizer. Index Terms--Finite-time stabilization, input delay, saturated design.展开更多
Dear Editor,This letter addresses distributed optimization for resource allocation problems with time-varying objective functions and time-varying constraints.Inspired by the distributed average tracking(DAT)approach,...Dear Editor,This letter addresses distributed optimization for resource allocation problems with time-varying objective functions and time-varying constraints.Inspired by the distributed average tracking(DAT)approach,a distributed control protocol is proposed for optimal resource allocation.The convergence to a time-varying optimal solution within a predefined time is proved.Two numerical examples are given to illustrate the effectiveness of the proposed approach.展开更多
Most enterprises rely on railway transportation to deliver their products to customers,particularly in the salt lake chemical industry.Notably,allocating products to freight spaces and their assembly on transport vehi...Most enterprises rely on railway transportation to deliver their products to customers,particularly in the salt lake chemical industry.Notably,allocating products to freight spaces and their assembly on transport vehicles are critical pre-transportation processes.However,due to demand fluctuations from changing product orders and unforeseen railway scheduling delays,manually adjusted allocation and loading may lead to excessive loading and unloading distances and times,ultimately increasing transportation costs for enterprises.To address these issues,this paper proposes a data-driven two-stage robust optimization(TSRO)framework embedding with the gated stacked temporal autoencoder clustering based on the attention mechanism(GSTAC-AM),which aims to overcome demand uncertainty and enhance the efficiency of freight allocation and loading.Specifically,GSTAC-AM is developed to help predict the deviation level of demand uncertainty and mitigate the impact of potential outliers.Then,a robust counterpart model is formulated to ensure computational tractability.In addition,a multi-stage hybrid heuristic algorithm is designed to handle the large scale and complexity inherent in the freight space allocation and loading processes.Finally,the effectiveness and applicability of the proposed framework are validated through a real case study conducted in a large salt lake chemical enterprise.展开更多
In the industrial roller kiln,the time-delay characteristic in heat transfer causes the temperature field to be affected by both the current and historical temperature states.It presents a poor control performance and...In the industrial roller kiln,the time-delay characteristic in heat transfer causes the temperature field to be affected by both the current and historical temperature states.It presents a poor control performance and brings a significant challenge to the process precise control.Considering high complexity of precise modeling,a data-driven time-delay optimal control method for temperature field of roller kiln is proposed based on a large amount of process data.First,the control challenges and problem description brought by time-delay are demonstrated,where the cost function for the time-delay partial differential equation system is constructed.To obtain the optimal control law,the policy iteration in adaptive dynamic programming is adopted to design the time-delay temperature field controller,and neural network is used for the critic network in policy iteration to approximate the optimal time-delay cost function.The closed-loop system stability is proved by designing the Lyapunov function which contains the time-delay information.Finally,through establishing the time-delay temperature field model for roller kiln,the effectiveness and convergence of the proposed method is verified and proved.展开更多
With the intelligent transformation of process manufacturing,accurate and comprehensive perception information is fundamental for application of artificial intelligence methods.In zinc smelting,the fluidized bed roast...With the intelligent transformation of process manufacturing,accurate and comprehensive perception information is fundamental for application of artificial intelligence methods.In zinc smelting,the fluidized bed roaster is a key piece of large-scale equipment and plays a critical role in the manufacturing industry;its internal temperature field directly determines the quality of zinc calcine and other related products.However,due to its vast spatial dimensions,the limited observation methods,and the complex multiphase,multifield coupled reaction atmosphere inside it,accurately and timely perceiving its temperature field remains a significant challenge.To address these challenges,a spatial-temporal reduced-order model(STROM)is proposed,which can realize fast and accurate temperature field perception based on sparse observation data.Specifically,to address the difficulty in matching the initial physical field with the sparse observation data,an initial field construction based on data assimilation(IFCDA)method is proposed to ensure that the initial conditions of the model can be matched with the actual operation state,which provides a basis for constructing a high-precision computational fluid dynamics(CFD)model.Then,to address the high simulation cost of high-precision CFD models under full working conditions,a high uniformity(HU)-orthogonal test design(OTD)method with the centered L2 deviation is innovatively proposed to ensure high information coverage of the temperature field dataset under typical working conditions in terms of multiple factors and levels of the component,feed,and blast parameters.Finally,to address the difficulty in real-time and accurate temperature field prediction,considering the spatial correlation between the observed temperature and the temperature field,as well as the dynamic correlation of the observed temperature in the time dimension,a spatial-temporal predictive model(STPM)is established,which realizes rapid prediction of the temperature field through sparse observa-tion data.To verify the accuracy and validity of the proposed method,CFD model validation and reduced-order model prediction experiments are designed,and the results show that the proposed method can realize high-precision and fast prediction of the roaster temperature field under different working conditions through sparse observation data.Compared with the CFD model,the prediction root-mean-square error(RMSE)of STROM is less than 0.038,and the computational efficiency is improved by 3.4184×10^(4)times.In particular,STROM also has a good prediction ability for unmodeled conditions,with a prediction RMSE of less than 0.1089.展开更多
Today,a well-devised charging operation scheme is urgently needed by on-site workmen and is critical for building an intelligent blast furnace(BF).Previous research on charging operations always focused on the two-dim...Today,a well-devised charging operation scheme is urgently needed by on-site workmen and is critical for building an intelligent blast furnace(BF).Previous research on charging operations always focused on the two-dimensional shape of the burden surface(i.e.,a single radial profile)while neglecting the unique feature of global dissymmetry,severely restricting the development of precise charging.For this reason,this study proposes an innovative optimization strategy for the charging operation under the three-dimensional burden surface,which is the first attempt in this field.First,a practicable region partitioning scheme is introduced,and the partitioning results are then integrated with the charging mechanism to construct a three-dimensional burden surface prediction model.Next,the intrinsic relationship between the operational parameters and charging volume is revealed based on the law of mass conservation,which forms the basis for defining a novel operational parameter with variable-speed utility,referred to as the neotype charging matrix(NCM).To find the best NCM,a customized NCM optimization strategy,involving a dual constraint handling technique in conjunction with a two-stage hybrid variable differential evolution algorithm,is further developed.The industrial experiment results manifest that the partitioning scheme significantly enhances the accuracy of burden surface description.Moreover,the NCM optimization strategy offers greater flexibility and higher accuracy than current mainstream optimization strategies for the charging matrix(CM).展开更多
Choosing optimal parameters for support vector regression （SVR） is an important step in SVR. design, which strongly affects the pefformance of SVR. In this paper, based on the analysis of influence of SVR parameters...Choosing optimal parameters for support vector regression （SVR） is an important step in SVR. design, which strongly affects the pefformance of SVR. In this paper, based on the analysis of influence of SVR parameters on generalization error, a new approach with two steps is proposed for selecting SVR parameters, First the kernel function and SVM parameters are optimized roughly through genetic algorithm, then the kernel parameter is finely adjusted by local linear search, This approach has been successfully applied to the prediction model of the sulfur content in hot metal. The experiment results show that the proposed approach can yield better generalization performance of SVR than other methods,展开更多
The solution purification process is an essential step in zinc hydrometallurgy. The performance of solution purification directly affects the normal functioning and economical benefits of zinc hydrometallurgy. This pa...The solution purification process is an essential step in zinc hydrometallurgy. The performance of solution purification directly affects the normal functioning and economical benefits of zinc hydrometallurgy. This paper summarizes the authors' recent work on the modeling, optimization, and control of solution purification process. The online measurable property of the oxidation reduction potential(ORP) and the multiple reactors, multiple running statuses characteristic of the solution purification process are extensively utilized in this research. The absence of reliable online equipment for detecting the impurity ion concentration is circumvented by introducing the oxidationreduction potential into the kinetic model. A steady-state multiple reactors gradient optimization, unsteady-state operationalpattern adjustment strategy, and a process evaluation strategy based on the oxidation-reduction potential are proposed. The effectiveness of the proposed research is demonstrated by its industrial experiment.展开更多
The nonferrous metallurgical(NFM)industry is a cornerstone industry for a nation’s economy.With the development of artificial technologies and high requirements on environment protection,product quality,and productio...The nonferrous metallurgical(NFM)industry is a cornerstone industry for a nation’s economy.With the development of artificial technologies and high requirements on environment protection,product quality,and production efficiency,the importance of applying smart manufacturing technologies to comprehensively percept production states and intelligently optimize process operations is becoming widely recognized by the industry.As a brief summary of the smart and optimal manufacturing of the NFM industry,this paper first reviews the research progress on some key facets of the operational optimization of NFM processes,including production and management,blending optimization,modeling,process monitoring,optimization,and control.Then,it illustrates the perspectives of smart and optimal manufacturing of the NFM industry.Finally,it discusses the major research directions and challenges of smart and optimal manufacturing for the NFM industry.This paper will lay a foundation for the realization of smart and optimal manufacturing in nonferrous metallurgy in the future.展开更多
For a class of value-bounded uncertain descriptor large-scale interconnected systems,the decentralized robust H∞descriptor output feedback control problem is investigated.A design method based on the bounded real lem...For a class of value-bounded uncertain descriptor large-scale interconnected systems,the decentralized robust H∞descriptor output feedback control problem is investigated.A design method based on the bounded real lemma is developed for a decentralized descriptor dynamic output feedback controller,which is reduced to a feasibility problem for a nonlinear matrix inequality(NLMI).It is proposed to solve the NLMI iteratively by the idea of homotopy,where some of the variables are fixed alternately at each iteration to reduce the NLMI to a linear matrix inequality(LMI).A given example shows the efficiency of this method.展开更多
In the aluminum reduction process, aluminum uoride (AlF3) is added to lower the liquidus temperature of the electrolyte and increase the electrolytic ef ciency. Making the decision on the amount of AlF3 addi- tion (re...In the aluminum reduction process, aluminum uoride (AlF3) is added to lower the liquidus temperature of the electrolyte and increase the electrolytic ef ciency. Making the decision on the amount of AlF3 addi- tion (referred to in this work as MDAAA) is a complex and knowledge-based task that must take into con- sideration a variety of interrelated functions;in practice, this decision-making step is performed manually. Due to technician subjectivity and the complexity of the aluminum reduction cell, it is dif cult to guarantee the accuracy of MDAAA based on knowledge-driven or data-driven methods alone. Existing strategies for MDAAA have dif culty covering these complex causalities. In this work, a data and knowl- edge collaboration strategy for MDAAA based on augmented fuzzy cognitive maps (FCMs) is proposed. In the proposed strategy, the fuzzy rules are extracted by extended fuzzy k-means (EFKM) and fuzzy deci- sion trees, which are used to amend the initial structure provided by experts. The state transition algo- rithm (STA) is introduced to detect weight matrices that lead the FCMs to desired steady states. This study then experimentally compares the proposed strategy with some existing research. The results of the comparison show that the speed of FCMs convergence into a stable region based on the STA using the proposed strategy is faster than when using the differential Hebbian learning (DHL), particle swarm optimization (PSO), or genetic algorithm (GA) strategies. In addition, the accuracy of MDAAA based on the proposed method is better than those based on other methods. Accordingly, this paper provides a feasible and effective strategy for MDAAA.展开更多
This article proposes an integral-based event-triggered attack-resilient control method for the aircraft-on-ground(AoG) synergistic turning system with uncertain tire cornering stiffness under stochastic deception att...This article proposes an integral-based event-triggered attack-resilient control method for the aircraft-on-ground(AoG) synergistic turning system with uncertain tire cornering stiffness under stochastic deception attacks. First, a novel AoG synergistic turning model is established with synergistic reverse steering of the front and main wheels to decrease the steering angle of the AoG fuselage, thus reducing the steady-state error when it follows a path with some large curvature. Considering that the tire cornering stiffness of the front and main wheels vary during steering, a dynamical observer is designed to adaptively identify them and estimate the system state at the same time.Then, an integral-based event-triggered mechanism(I-ETM) is synthesized to reduce the transmission frequency at the observerto-controller end, where stochastic deception attacks may occur at any time with a stochastic probability. Moreover, an attackresilient controller is designed to guarantee that the closed-loop system is robust L2-stable under stochastic attacks and external disturbances. A co-design method is provided to get feasible solutions for the observer, controller, and I-ETM simultaneously. An optimization program is further presented to make a tradeoff between the robustness of the control scheme and the saving of communication resources. Finally, the low-and high-probability stochastic deception attacks are considered in the simulations. The results have illustrated that the AoG synergistic turning system with the proposed control method follows a path with some large curvature well under stochastic deception attacks. Furthermore,compared with the static event-triggered mechanisms, the proposed I-ETM has demonstrated its superiority in saving communication resources.展开更多
The zinc oxide rotary kiln,as an essential piece of equipment in the zinc smelting industrial process,is presenting new challenges in process control.China’s strategy of achieving a carbon peak and carbon neutrality ...The zinc oxide rotary kiln,as an essential piece of equipment in the zinc smelting industrial process,is presenting new challenges in process control.China’s strategy of achieving a carbon peak and carbon neutrality is putting new demands on the industry,including green production and the use of fewer resources;thus,traditional stability control is no longer suitable for multi-objective control tasks.Although researchers have revealed the principle of the rotary kiln and set up computational fluid dynamics(CFD)simulation models to study its dynamics,these models cannot be directly applied to process control due to their high computational complexity.To address these issues,this paper proposes a multi-objective adaptive optimization model predictive control(MAO-MPC)method based on sparse identification.More specifically,with a large amount of data collected from a CFD model,a sparse regression problem is first formulated and solved to obtain a reduction model.Then,a two-layered control framework including real-time optimization(RTO)and model predictive control(MPC)is designed.In the RTO layer,an optimization problem with the goal of achieving optimal operation performance and the lowest possible resource consumption is set up.By solving the optimization problem in real time,a suitable setting value is sent to the MPC layer to ensure that the zinc oxide rotary kiln always functions in an optimal state.Our experiments show the strength and reliability of the proposed method,which reduces the usage of coal while maintaining high profits.展开更多
This paper focuses on the problem of stability analysis and controller design for a class of delay systems based on networked control systems. By introducing some free matrix variables, some criteria for stability ana...This paper focuses on the problem of stability analysis and controller design for a class of delay systems based on networked control systems. By introducing some free matrix variables, some criteria for stability analysis and observer-based control law design can be obtained by the solving of linear matrix inequalities. A numerical example is also offered to prove the effectiveness of the proposed method.展开更多
Partial least squares(PLS)model is the most typical data-driven method for quality-related industrial tasks like soft sensor.However,only linear relations are captured between the input and output data in the PLS.It i...Partial least squares(PLS)model is the most typical data-driven method for quality-related industrial tasks like soft sensor.However,only linear relations are captured between the input and output data in the PLS.It is difficult to obtain the remaining nonlinear information in the residual subspaces,which may deteriorate the prediction performance in complex industrial processes.To fully utilize data information in PLS residual subspaces,a deep residual PLS(DRPLS)framework is proposed for quality prediction in this paper.Inspired by deep learning,DRPLS is designed by stacking a number of PLSs successively,in which the input residuals of the previous PLS are used as the layer connection.To enhance representation,nonlinear function is applied to the input residuals before using them for stacking highlevel PLS.For each PLS,the output parts are just the output residuals from its previous PLS.Finally,the output prediction is obtained by adding the results of each PLS.The effectiveness of the proposed DRPLS is validated on an industrial hydrocracking process.展开更多
The dust distribution law acting at the top of a blast fumace(BF)is of great significance for understanding gas flow distribution and mitigating the negative influence of dust particles on the accuracy and service lif...The dust distribution law acting at the top of a blast fumace(BF)is of great significance for understanding gas flow distribution and mitigating the negative influence of dust particles on the accuracy and service life of detection equipment.The harsh environment inside a BF makes it difficult to describe the dust disthibution.This paper adresses this problem by proposing a dust distribution k-Sε-u_(p)model based on interphase(gas-powder)coupling.The proposed model is coupled with a k-Sεmodel(which describes gas flow movement)and a u_(p)model(which depicts dust movement).First,the kinetic energy equation and turbulent dissipation rate equation in the k-Sεmodel are established based on the modeling theory and single Green-function two scale direct interaction approximation(SGF-TSDIA)theory.Second,a dust particle mnovement u_(p)model is built based on a force analysis of the dust and Newton's laws of motion.Finally,a coupling factor that descibes the interphase interaction is proposed,and the k-Sε-u_(p)model,with clear physical meaning.ligorous mathematical logic,and adequate generality,is dleveloped.Siumulation results and o-site verification show that the k-Sε-u_(p)model not only has high precision,but also reveals the aggregate distribution features of the dust,which are helpful in optimizing the installation position of the detection equipment and imnproving its accuracy and service life.展开更多
Cell voltage is a widely used signal that can be measured online from an industrial aluminum electrolysis cell.A variety of parameters for the analysis and control of industrial cells are calculated using the cell vol...Cell voltage is a widely used signal that can be measured online from an industrial aluminum electrolysis cell.A variety of parameters for the analysis and control of industrial cells are calculated using the cell voltage.In this paper,the frequency segmentation of cell voltage is used as the basis for designing filters to obtain these parameters.Based on the qualitative analysis of the cell voltage,the sub-band instantaneous energy spectrum(SIEP)is first proposed,which is then used to quantitatively represent the characteristics of the designated frequency bands of the cell voltage under various cell conditions.Ultimately,a cell condition-sensitive frequency segmentation method is given.The proposed frequency segmentation method divides the effective frequency band into the[0,0.001]Hz band of lowfrequency signals and the[0.001,0.050]Hz band of low-frequency noise,and subdivides the lowfrequency noise into the[0.001,0.010]Hz band of metal pad abnormal rolling and the[0.01,0.05]Hz band of sub-low-frequency noise.Compared with the instantaneous energy spectrum based on empirical mode decomposition,the SIEP more finely represents the law of energy change with time in any designated frequency band within the effective frequency band of the cell voltage.The proposed frequency segmentation method is more sensitive to cell condition changes and can obtain more elaborate details of online cell condition information,thus providing a more reliable and accurate online basis for cell condition monitoring and control decisions.展开更多
The problem of delay-dependent stability and passivity for linear neutral systems is discussed.By constructing a novel type Lyapunov-krasovskii functional,a new delay-dependent passivity criterion is presented in term...The problem of delay-dependent stability and passivity for linear neutral systems is discussed.By constructing a novel type Lyapunov-krasovskii functional,a new delay-dependent passivity criterion is presented in terms of linear matrix inequalities(LMIs).Model transformation,bounding for cross terms and selecting free weighting matrices[12-14]are not required in the arguments.Numerical examples show that the proposed criteria are available and less conservative than existing results.展开更多
Data-driven process-monitoring methods have been the mainstream for complex industrial systems due to their universality and the reduced need for reaction mechanisms and first-principles knowledge.However,most data-dr...Data-driven process-monitoring methods have been the mainstream for complex industrial systems due to their universality and the reduced need for reaction mechanisms and first-principles knowledge.However,most data-driven process-monitoring methods assume that historical training data and online testing data follow the same distribution.In fact,due to the harsh environment of industrial systems,the collected data from real industrial processes are always affected by many factors,such as the changeable operating environment,variation in the raw materials,and production indexes.These factors often cause the distributions of online monitoring data and historical training data to differ,which induces a model mismatch in the process-monitoring task.Thus,it is difficult to achieve accurate process monitoring when a model learned from training data is applied to actual online monitoring.In order to resolve the problem of the distribution divergence between historical training data and online testing data that is induced by changeable operation environments,a robust transfer dictionary learning(RTDL)algorithm is proposed in this paper for industrial process monitoring.The RTDL is a synergy of representative learning and domain adaptive transfer learning.The proposed method regards historical training data and online testing data as the source domain and the target domain,respectively,in the transfer learning problem.Maximum mean discrepancy regularization and linear discriminant analysis-like regularization are then incorporated into the dictionary learning framework,which can reduce the distribution divergence between the source domain and target domain.In this way,a robust dictionary can be learned even if the characteristics of the source domain and target domain are evidently different under the interference of a realistic and changeable operation environment.Such a dictionary can effectively improve the performance of process monitoring and mode classification.Extensive experiments including a numerical simulation and two industrial systems are conducted to verify the efficiency and superiority of the proposed method.展开更多
Dear Editor,The distributed generalized-Nash-equilibrium(GNE)seeking in noncooperative games with nonconvexity is the topic of this letter.Inspired by the sequential quadratic programming(SQP)method,a multi-timescale ...Dear Editor,The distributed generalized-Nash-equilibrium(GNE)seeking in noncooperative games with nonconvexity is the topic of this letter.Inspired by the sequential quadratic programming(SQP)method,a multi-timescale multi-agent system(MAS)is developed,and its convergence to a critical point of the game is proven.To illustrate the qualities and efficacy of the theoretical findings,a numerical example is elaborated.展开更多
基金partially supported by the National Natural Science Foundation of China(61374024,61321003,61325309)the Natural Science Foundation of Hunan Province(14JJ2016)the Teacher Research Foundation of Central South University(2013JSJJ023)
文摘Abstract--The time-optimal control design of the double integrator is extended to the finite-time stabilization design that compensates both input saturation and input delay. With the aid of the Artstein＇s transformation, the problem is reduced to assigning a saturated finite-time stabilizer. Index Terms--Finite-time stabilization, input delay, saturated design.
基金supported by National Key Research and Development Program of China(2024YFE0214000)National Natural Science Foundation of China(62173308)+3 种基金Natural Science Foundation of Zhejiang Province of China(LRG25F030002)Zhejiang Province Leading Geese Plan(2025C01056)Jinhua Science and Technology Project(2022-1-042)Natural Science Foundation of Jiangsu Province(BK20240009).
文摘Dear Editor,This letter addresses distributed optimization for resource allocation problems with time-varying objective functions and time-varying constraints.Inspired by the distributed average tracking(DAT)approach,a distributed control protocol is proposed for optimal resource allocation.The convergence to a time-varying optimal solution within a predefined time is proved.Two numerical examples are given to illustrate the effectiveness of the proposed approach.
基金supported in part by the National Natural Science Foundation of China(NSFC)(92267205)the Natural Science Foundation of Hunan Province(2025JJ10007,2025JJ60423)the Open Research Project of the State Key Laboratory of Industrial Control Technology,China(ICT2024 B66).
文摘Most enterprises rely on railway transportation to deliver their products to customers,particularly in the salt lake chemical industry.Notably,allocating products to freight spaces and their assembly on transport vehicles are critical pre-transportation processes.However,due to demand fluctuations from changing product orders and unforeseen railway scheduling delays,manually adjusted allocation and loading may lead to excessive loading and unloading distances and times,ultimately increasing transportation costs for enterprises.To address these issues,this paper proposes a data-driven two-stage robust optimization(TSRO)framework embedding with the gated stacked temporal autoencoder clustering based on the attention mechanism(GSTAC-AM),which aims to overcome demand uncertainty and enhance the efficiency of freight allocation and loading.Specifically,GSTAC-AM is developed to help predict the deviation level of demand uncertainty and mitigate the impact of potential outliers.Then,a robust counterpart model is formulated to ensure computational tractability.In addition,a multi-stage hybrid heuristic algorithm is designed to handle the large scale and complexity inherent in the freight space allocation and loading processes.Finally,the effectiveness and applicability of the proposed framework are validated through a real case study conducted in a large salt lake chemical enterprise.
基金supported in part by the Key Program of National Natural Science Foundation of China(62033014)the Application Projects of Integrated Standardization and New Paradigm for Intelligent Manufacturing from the Ministry of Industry and Information Technology of China in 2016the Fundamental Research Funds for the Central Universities of Central South University(2021zzts0700).
文摘In the industrial roller kiln,the time-delay characteristic in heat transfer causes the temperature field to be affected by both the current and historical temperature states.It presents a poor control performance and brings a significant challenge to the process precise control.Considering high complexity of precise modeling,a data-driven time-delay optimal control method for temperature field of roller kiln is proposed based on a large amount of process data.First,the control challenges and problem description brought by time-delay are demonstrated,where the cost function for the time-delay partial differential equation system is constructed.To obtain the optimal control law,the policy iteration in adaptive dynamic programming is adopted to design the time-delay temperature field controller,and neural network is used for the critic network in policy iteration to approximate the optimal time-delay cost function.The closed-loop system stability is proved by designing the Lyapunov function which contains the time-delay information.Finally,through establishing the time-delay temperature field model for roller kiln,the effectiveness and convergence of the proposed method is verified and proved.
基金supported in part by the National Key Research and Development Program of China(2022YFB3304900)in part by the National Natural Science Foundation of China(62394340 and 62073340)in part by the Science and Technology Innovation Program of Hunan Province(2022JJ10083).
文摘With the intelligent transformation of process manufacturing,accurate and comprehensive perception information is fundamental for application of artificial intelligence methods.In zinc smelting,the fluidized bed roaster is a key piece of large-scale equipment and plays a critical role in the manufacturing industry;its internal temperature field directly determines the quality of zinc calcine and other related products.However,due to its vast spatial dimensions,the limited observation methods,and the complex multiphase,multifield coupled reaction atmosphere inside it,accurately and timely perceiving its temperature field remains a significant challenge.To address these challenges,a spatial-temporal reduced-order model(STROM)is proposed,which can realize fast and accurate temperature field perception based on sparse observation data.Specifically,to address the difficulty in matching the initial physical field with the sparse observation data,an initial field construction based on data assimilation(IFCDA)method is proposed to ensure that the initial conditions of the model can be matched with the actual operation state,which provides a basis for constructing a high-precision computational fluid dynamics(CFD)model.Then,to address the high simulation cost of high-precision CFD models under full working conditions,a high uniformity(HU)-orthogonal test design(OTD)method with the centered L2 deviation is innovatively proposed to ensure high information coverage of the temperature field dataset under typical working conditions in terms of multiple factors and levels of the component,feed,and blast parameters.Finally,to address the difficulty in real-time and accurate temperature field prediction,considering the spatial correlation between the observed temperature and the temperature field,as well as the dynamic correlation of the observed temperature in the time dimension,a spatial-temporal predictive model(STPM)is established,which realizes rapid prediction of the temperature field through sparse observa-tion data.To verify the accuracy and validity of the proposed method,CFD model validation and reduced-order model prediction experiments are designed,and the results show that the proposed method can realize high-precision and fast prediction of the roaster temperature field under different working conditions through sparse observation data.Compared with the CFD model,the prediction root-mean-square error(RMSE)of STROM is less than 0.038,and the computational efficiency is improved by 3.4184×10^(4)times.In particular,STROM also has a good prediction ability for unmodeled conditions,with a prediction RMSE of less than 0.1089.
基金supported in part by the Science and Technology Innovation Program of Hunan Province(2024RC1007)the Young Scientists Fund of the National Natural Science Foundation of China(62303491)+2 种基金the Major Program of Xiangjiang Laboratory(22XJ01005)the Young Scientists Fund of the National Natural Science Foundation of China(62203473)Central South University Post-Graduate Independent Exploration and Innovation Project(2024ZZTS0451).
文摘Today,a well-devised charging operation scheme is urgently needed by on-site workmen and is critical for building an intelligent blast furnace(BF).Previous research on charging operations always focused on the two-dimensional shape of the burden surface(i.e.,a single radial profile)while neglecting the unique feature of global dissymmetry,severely restricting the development of precise charging.For this reason,this study proposes an innovative optimization strategy for the charging operation under the three-dimensional burden surface,which is the first attempt in this field.First,a practicable region partitioning scheme is introduced,and the partitioning results are then integrated with the charging mechanism to construct a three-dimensional burden surface prediction model.Next,the intrinsic relationship between the operational parameters and charging volume is revealed based on the law of mass conservation,which forms the basis for defining a novel operational parameter with variable-speed utility,referred to as the neotype charging matrix(NCM).To find the best NCM,a customized NCM optimization strategy,involving a dual constraint handling technique in conjunction with a two-stage hybrid variable differential evolution algorithm,is further developed.The industrial experiment results manifest that the partitioning scheme significantly enhances the accuracy of burden surface description.Moreover,the NCM optimization strategy offers greater flexibility and higher accuracy than current mainstream optimization strategies for the charging matrix(CM).
文摘Choosing optimal parameters for support vector regression （SVR） is an important step in SVR. design, which strongly affects the pefformance of SVR. In this paper, based on the analysis of influence of SVR parameters on generalization error, a new approach with two steps is proposed for selecting SVR parameters, First the kernel function and SVM parameters are optimized roughly through genetic algorithm, then the kernel parameter is finely adjusted by local linear search, This approach has been successfully applied to the prediction model of the sulfur content in hot metal. The experiment results show that the proposed approach can yield better generalization performance of SVR than other methods,
基金supported by the National Natural Science Foundation of China(61603418,61673400,61273185)the Foundation for Innovative Research Groups of the National Natural Science Foundation of China(61621062)the Innovation-driven Plan in Central South University(2015cx007)
文摘The solution purification process is an essential step in zinc hydrometallurgy. The performance of solution purification directly affects the normal functioning and economical benefits of zinc hydrometallurgy. This paper summarizes the authors' recent work on the modeling, optimization, and control of solution purification process. The online measurable property of the oxidation reduction potential(ORP) and the multiple reactors, multiple running statuses characteristic of the solution purification process are extensively utilized in this research. The absence of reliable online equipment for detecting the impurity ion concentration is circumvented by introducing the oxidationreduction potential into the kinetic model. A steady-state multiple reactors gradient optimization, unsteady-state operationalpattern adjustment strategy, and a process evaluation strategy based on the oxidation-reduction potential are proposed. The effectiveness of the proposed research is demonstrated by its industrial experiment.
基金financially supported by the Funds for International Cooperation and Exchange of the National Natural Science Foundation of China(No.61860206014)the Basic Science Research Center Program of National Natural Science Foundation of China(No.61988101)+2 种基金National Key Research and Development Program(No.2020YFB1713700)National Natural Science Foundation of China(Nos.61973321 and 62073342)Science and Technology Innovation Program of Hunan Province(No.2021RC4054).
文摘The nonferrous metallurgical(NFM)industry is a cornerstone industry for a nation’s economy.With the development of artificial technologies and high requirements on environment protection,product quality,and production efficiency,the importance of applying smart manufacturing technologies to comprehensively percept production states and intelligently optimize process operations is becoming widely recognized by the industry.As a brief summary of the smart and optimal manufacturing of the NFM industry,this paper first reviews the research progress on some key facets of the operational optimization of NFM processes,including production and management,blending optimization,modeling,process monitoring,optimization,and control.Then,it illustrates the perspectives of smart and optimal manufacturing of the NFM industry.Finally,it discusses the major research directions and challenges of smart and optimal manufacturing for the NFM industry.This paper will lay a foundation for the realization of smart and optimal manufacturing in nonferrous metallurgy in the future.
基金supported by the National Natural Science Foundation of China(No.60474003)the Doctor Subject Foundation of China(No.20050533028).
文摘For a class of value-bounded uncertain descriptor large-scale interconnected systems,the decentralized robust H∞descriptor output feedback control problem is investigated.A design method based on the bounded real lemma is developed for a decentralized descriptor dynamic output feedback controller,which is reduced to a feasibility problem for a nonlinear matrix inequality(NLMI).It is proposed to solve the NLMI iteratively by the idea of homotopy,where some of the variables are fixed alternately at each iteration to reduce the NLMI to a linear matrix inequality(LMI).A given example shows the efficiency of this method.
文摘In the aluminum reduction process, aluminum uoride (AlF3) is added to lower the liquidus temperature of the electrolyte and increase the electrolytic ef ciency. Making the decision on the amount of AlF3 addi- tion (referred to in this work as MDAAA) is a complex and knowledge-based task that must take into con- sideration a variety of interrelated functions;in practice, this decision-making step is performed manually. Due to technician subjectivity and the complexity of the aluminum reduction cell, it is dif cult to guarantee the accuracy of MDAAA based on knowledge-driven or data-driven methods alone. Existing strategies for MDAAA have dif culty covering these complex causalities. In this work, a data and knowl- edge collaboration strategy for MDAAA based on augmented fuzzy cognitive maps (FCMs) is proposed. In the proposed strategy, the fuzzy rules are extracted by extended fuzzy k-means (EFKM) and fuzzy deci- sion trees, which are used to amend the initial structure provided by experts. The state transition algo- rithm (STA) is introduced to detect weight matrices that lead the FCMs to desired steady states. This study then experimentally compares the proposed strategy with some existing research. The results of the comparison show that the speed of FCMs convergence into a stable region based on the STA using the proposed strategy is faster than when using the differential Hebbian learning (DHL), particle swarm optimization (PSO), or genetic algorithm (GA) strategies. In addition, the accuracy of MDAAA based on the proposed method is better than those based on other methods. Accordingly, this paper provides a feasible and effective strategy for MDAAA.
基金supported in part by the National Science Fund for Excellent Young Scholars of China (62222317)the National Natural Science Foundation of China (61973319)+4 种基金the Funds for International Cooperation and Exchange of the National Natural Science Foundation of China (61860206014)111 Project of China (B17048)Science and Technology Innovation Program of Hunan Province (2022WZ1001)the Natural Science Foundation of Changsha (kq2208287)the Postdoctoral Fund of Central South University (22022136)。
文摘This article proposes an integral-based event-triggered attack-resilient control method for the aircraft-on-ground(AoG) synergistic turning system with uncertain tire cornering stiffness under stochastic deception attacks. First, a novel AoG synergistic turning model is established with synergistic reverse steering of the front and main wheels to decrease the steering angle of the AoG fuselage, thus reducing the steady-state error when it follows a path with some large curvature. Considering that the tire cornering stiffness of the front and main wheels vary during steering, a dynamical observer is designed to adaptively identify them and estimate the system state at the same time.Then, an integral-based event-triggered mechanism(I-ETM) is synthesized to reduce the transmission frequency at the observerto-controller end, where stochastic deception attacks may occur at any time with a stochastic probability. Moreover, an attackresilient controller is designed to guarantee that the closed-loop system is robust L2-stable under stochastic attacks and external disturbances. A co-design method is provided to get feasible solutions for the observer, controller, and I-ETM simultaneously. An optimization program is further presented to make a tradeoff between the robustness of the control scheme and the saving of communication resources. Finally, the low-and high-probability stochastic deception attacks are considered in the simulations. The results have illustrated that the AoG synergistic turning system with the proposed control method follows a path with some large curvature well under stochastic deception attacks. Furthermore,compared with the static event-triggered mechanisms, the proposed I-ETM has demonstrated its superiority in saving communication resources.
基金supported in part by the National Key Research and Development Program of China(2022YFB3304900)in part by the National Natural Science Foundation of China(61988101,62073340,and 61860206014)+2 种基金in part by the Major Key Project of Peng Cheng Laboratory(PCL)(PCL2021A09)in part by the Science and Technology Innovation Program of Hunan Province(2022JJ10083,2021RC3018,and 2021RC4054)in part by the Innovation-Driven Project of Central South University,China(2019CX020)。
文摘The zinc oxide rotary kiln,as an essential piece of equipment in the zinc smelting industrial process,is presenting new challenges in process control.China’s strategy of achieving a carbon peak and carbon neutrality is putting new demands on the industry,including green production and the use of fewer resources;thus,traditional stability control is no longer suitable for multi-objective control tasks.Although researchers have revealed the principle of the rotary kiln and set up computational fluid dynamics(CFD)simulation models to study its dynamics,these models cannot be directly applied to process control due to their high computational complexity.To address these issues,this paper proposes a multi-objective adaptive optimization model predictive control(MAO-MPC)method based on sparse identification.More specifically,with a large amount of data collected from a CFD model,a sparse regression problem is first formulated and solved to obtain a reduction model.Then,a two-layered control framework including real-time optimization(RTO)and model predictive control(MPC)is designed.In the RTO layer,an optimization problem with the goal of achieving optimal operation performance and the lowest possible resource consumption is set up.By solving the optimization problem in real time,a suitable setting value is sent to the MPC layer to ensure that the zinc oxide rotary kiln always functions in an optimal state.Our experiments show the strength and reliability of the proposed method,which reduces the usage of coal while maintaining high profits.
基金supported by the National Science Foundation of China (No.60474003)Hunan Provincial Natural Science Foundation of China (07JJ6126)the Postdoctoral Science Foundation of Central South University
文摘This paper focuses on the problem of stability analysis and controller design for a class of delay systems based on networked control systems. By introducing some free matrix variables, some criteria for stability analysis and observer-based control law design can be obtained by the solving of linear matrix inequalities. A numerical example is also offered to prove the effectiveness of the proposed method.
基金supported in part by the National Natural Science Foundation of China(62173346,61988101,92267205,62103360,62303494)。
文摘Partial least squares(PLS)model is the most typical data-driven method for quality-related industrial tasks like soft sensor.However,only linear relations are captured between the input and output data in the PLS.It is difficult to obtain the remaining nonlinear information in the residual subspaces,which may deteriorate the prediction performance in complex industrial processes.To fully utilize data information in PLS residual subspaces,a deep residual PLS(DRPLS)framework is proposed for quality prediction in this paper.Inspired by deep learning,DRPLS is designed by stacking a number of PLSs successively,in which the input residuals of the previous PLS are used as the layer connection.To enhance representation,nonlinear function is applied to the input residuals before using them for stacking highlevel PLS.For each PLS,the output parts are just the output residuals from its previous PLS.Finally,the output prediction is obtained by adding the results of each PLS.The effectiveness of the proposed DRPLS is validated on an industrial hydrocracking process.
基金supported by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China(61621062)the National Major Scientific Research Equipment of China(61927803)+1 种基金the National Natural Science Foundation of China(61933015)National Natural Science Foundation for Young Scholars of China(61903325)。
文摘The dust distribution law acting at the top of a blast fumace(BF)is of great significance for understanding gas flow distribution and mitigating the negative influence of dust particles on the accuracy and service life of detection equipment.The harsh environment inside a BF makes it difficult to describe the dust disthibution.This paper adresses this problem by proposing a dust distribution k-Sε-u_(p)model based on interphase(gas-powder)coupling.The proposed model is coupled with a k-Sεmodel(which describes gas flow movement)and a u_(p)model(which depicts dust movement).First,the kinetic energy equation and turbulent dissipation rate equation in the k-Sεmodel are established based on the modeling theory and single Green-function two scale direct interaction approximation(SGF-TSDIA)theory.Second,a dust particle mnovement u_(p)model is built based on a force analysis of the dust and Newton's laws of motion.Finally,a coupling factor that descibes the interphase interaction is proposed,and the k-Sε-u_(p)model,with clear physical meaning.ligorous mathematical logic,and adequate generality,is dleveloped.Siumulation results and o-site verification show that the k-Sε-u_(p)model not only has high precision,but also reveals the aggregate distribution features of the dust,which are helpful in optimizing the installation position of the detection equipment and imnproving its accuracy and service life.
基金This work was supported by the Program of the National Natural Science Foundation of China(61988101,61773405,and 61751312).
文摘Cell voltage is a widely used signal that can be measured online from an industrial aluminum electrolysis cell.A variety of parameters for the analysis and control of industrial cells are calculated using the cell voltage.In this paper,the frequency segmentation of cell voltage is used as the basis for designing filters to obtain these parameters.Based on the qualitative analysis of the cell voltage,the sub-band instantaneous energy spectrum(SIEP)is first proposed,which is then used to quantitatively represent the characteristics of the designated frequency bands of the cell voltage under various cell conditions.Ultimately,a cell condition-sensitive frequency segmentation method is given.The proposed frequency segmentation method divides the effective frequency band into the[0,0.001]Hz band of lowfrequency signals and the[0.001,0.050]Hz band of low-frequency noise,and subdivides the lowfrequency noise into the[0.001,0.010]Hz band of metal pad abnormal rolling and the[0.01,0.05]Hz band of sub-low-frequency noise.Compared with the instantaneous energy spectrum based on empirical mode decomposition,the SIEP more finely represents the law of energy change with time in any designated frequency band within the effective frequency band of the cell voltage.The proposed frequency segmentation method is more sensitive to cell condition changes and can obtain more elaborate details of online cell condition information,thus providing a more reliable and accurate online basis for cell condition monitoring and control decisions.
基金This work was supported by the National Natural Science Foundation of China(No.60474003).
文摘The problem of delay-dependent stability and passivity for linear neutral systems is discussed.By constructing a novel type Lyapunov-krasovskii functional,a new delay-dependent passivity criterion is presented in terms of linear matrix inequalities(LMIs).Model transformation,bounding for cross terms and selecting free weighting matrices[12-14]are not required in the arguments.Numerical examples show that the proposed criteria are available and less conservative than existing results.
基金This work was supported in part by the National Natural Science Foundation of China(61988101)in part by the National Key R&D Program of China(2018YFB1701100).
文摘Data-driven process-monitoring methods have been the mainstream for complex industrial systems due to their universality and the reduced need for reaction mechanisms and first-principles knowledge.However,most data-driven process-monitoring methods assume that historical training data and online testing data follow the same distribution.In fact,due to the harsh environment of industrial systems,the collected data from real industrial processes are always affected by many factors,such as the changeable operating environment,variation in the raw materials,and production indexes.These factors often cause the distributions of online monitoring data and historical training data to differ,which induces a model mismatch in the process-monitoring task.Thus,it is difficult to achieve accurate process monitoring when a model learned from training data is applied to actual online monitoring.In order to resolve the problem of the distribution divergence between historical training data and online testing data that is induced by changeable operation environments,a robust transfer dictionary learning(RTDL)algorithm is proposed in this paper for industrial process monitoring.The RTDL is a synergy of representative learning and domain adaptive transfer learning.The proposed method regards historical training data and online testing data as the source domain and the target domain,respectively,in the transfer learning problem.Maximum mean discrepancy regularization and linear discriminant analysis-like regularization are then incorporated into the dictionary learning framework,which can reduce the distribution divergence between the source domain and target domain.In this way,a robust dictionary can be learned even if the characteristics of the source domain and target domain are evidently different under the interference of a realistic and changeable operation environment.Such a dictionary can effectively improve the performance of process monitoring and mode classification.Extensive experiments including a numerical simulation and two industrial systems are conducted to verify the efficiency and superiority of the proposed method.
基金partially supported by the National Natural Science Foundation of China(62173308)the Natural Science Foundation of Zhejiang Province of China(LR20F030001)+3 种基金the Jinhua Science and Technology Project(2022-1-042)University of Macao(MYRG2022-00108-FST,MYRG-CRG202200010-ICMS)the Science and Technology Development Fund,Macao S.A.R(0036/2021/AGJ)Chinese Guangdong’s S&T project(2022A0505020028)。
文摘Dear Editor,The distributed generalized-Nash-equilibrium(GNE)seeking in noncooperative games with nonconvexity is the topic of this letter.Inspired by the sequential quadratic programming(SQP)method,a multi-timescale multi-agent system(MAS)is developed,and its convergence to a critical point of the game is proven.To illustrate the qualities and efficacy of the theoretical findings,a numerical example is elaborated.
