Soft robotic manipulators represent a rapidly evolving field characterized by inherent compliance,adaptability,and safe interactions within unstructured environments.Over the past decade(2015-2025),significant advance...Soft robotic manipulators represent a rapidly evolving field characterized by inherent compliance,adaptability,and safe interactions within unstructured environments.Over the past decade(2015-2025),significant advancements have trans-formed their capabilities through novel designs inspired by biological systems,advanced modeling frameworks,sophisti-cated control strategies,and integration into diverse real-world applications.Recent innovations in multifunctional mate-rials and emerging actuation technologies have markedly expanded manipulator performance,reliability,and dexterity.Concurrently,developments in modeling have progressed from simplified geometric methods toward highly accurate physics-based and hybrid data-driven approaches,substantially improving real-time prediction and controllability.Coupled with these developments,adaptive and robust control strategies-including learning-based techniques-have enabled unprec-edented autonomy and precision in challenging application domains such as Minimally Invasive Surgery(MIS),precision agriculture,deep-sea exploration,disaster recovery,and space missions.Despite these remarkable strides,key challenges remain,notably regarding scalability,long-term material durability,robust integrated sensing,and standardized evaluation procedures.This review comprehensively synthesizes recent advances,critically evaluates state-of-the-art methodologies,and systematically identifies existing gaps to provide a clear roadmap and targeted research directions,guiding future developments toward the broader adoption and optimal utilization of soft robotic manipulators.展开更多
Fluctuating voltage levels in power grids necessitate automatic voltage regulators(AVRs)to ensure stability.This study examined the modeling and control of AVR in hydroelectric power plants using model predictive cont...Fluctuating voltage levels in power grids necessitate automatic voltage regulators(AVRs)to ensure stability.This study examined the modeling and control of AVR in hydroelectric power plants using model predictive control(MPC),which utilizes an extensive mathe-matical model of the voltage regulation system to optimize the control actions over a defined prediction horizon.This predictive feature enables MPC to minimize voltage deviations while accounting for operational constraints,thereby improving stability and performance under dynamic conditions.Thefindings were compared with those derived from an optimal proportional integral derivative(PID)con-troller designed using the artificial bee colony(ABC)algorithm.Although the ABC-PID method adjusts the PID parameters based on historical data,it may be difficult to adapt to real-time changes in system dynamics under constraints.Comprehensive simulations assessed both frameworks,emphasizing performance metrics such as disturbance rejection,response to load changes,and resilience to uncertainties.The results show that both MPC and ABC-PID methods effectively achieved accurate voltage regulation;however,MPC excelled in controlling overshoot and settling time—recording 0.0%and 0.25 s,respectively.This demonstrates greater robustness compared to conventional control methods that optimize PID parameters based on performance criteria derived from actual system behavior,which exhibited settling times and overshoots exceeding 0.41 s and 5.0%,respectively.The controllers were implemented using MATLAB/Simulink software,indicating a significant advancement for power plant engineers pursuing state-of-the-art automatic voltage regulations.展开更多
With the growing integration of renewable energy sources(RESs)and smart interconnected devices,conventional distribution networks have turned to active distribution networks(ADNs)with complex system model and power fl...With the growing integration of renewable energy sources(RESs)and smart interconnected devices,conventional distribution networks have turned to active distribution networks(ADNs)with complex system model and power flow dynamics.The rapid fluctuation of RES power may easily result in frequent voltage violation issues.Taking the flexible RES reactive power as control variables,this paper proposes a two-layer control scheme with Koopman wide neural network(WNN)based model predictive control(MPC)method for optimal voltage regulation and network loss reduction.Based on Koopman operator theory,a data-driven WNN method is presented to fit a high-dimensional linear model of power flow.With the model,voltage and network loss sensitivities are computed analytically,and utilized for ADN partition and control model formulation.In the lower level,a dual-mode adaptive switching MPC strategy is put forward for optimal voltage control and network loss optimization in each individual partition to decide the RES reactive power.The upper level is to calculate the adjustment coefficients of the RES reactive power given in the low level by taking the coupling effects of different partitions into account,and then the final reactive power dispatches of RESs are obtained to realize optimal control of voltage and network loss.Simulation results on two ADNs demonstrate that the proposed strategy can reliably maintain the voltage at each node within the secure range,reduce network power losses,and enhance the overall system security and economic efficiency.展开更多
In modern gas turbine engine control,Direct Thrust Control(DTC) is an effective method for achieving the desired thrust.Model Predictive Control(MPC) has the characteristics of handling constraints while accomplishing...In modern gas turbine engine control,Direct Thrust Control(DTC) is an effective method for achieving the desired thrust.Model Predictive Control(MPC) has the characteristics of handling constraints while accomplishing command tracking,making it a promising approach for implementing DTC.However,since the performance of DTC is highly sensitive to MPC's tuning parameters,developing an efficient optimization strategy for these parameters becomes imperative.Therefore,a Model Predictive Direct Thrust Control(MP-DTC) architecture is designed,and its asymptotic stability is proven.Additionally,the influence of the tuning parameters on control performance is analyzed.Then,a tuning strategy for MP-DTC architecture is proposed.This strategy combines the objectives of DTC to design a Multi-Objective Optimization(MOO) index function,and uses Multi-Objective Grey Wolf Optimizer(MOGWO) to solve its Pareto front and obtain the tuning parameters.In the Hardware-in-Loop(HIL) experiments,the proposed MPDTC architecture achieves the shortest settling time and smallest overshoot compared to the latest DTC scheme.Its MOGWO-based tuning strategy provides more Pareto-optimal solutions,ensuring optimal selection based on rapidity and stability,and maintains precise DTC even under component degradation and various operating conditions,thereby providing robustness,optimality,and generalizability.展开更多
Mode shift is a special mechanism for a power-split hybrid electric vehicle(HEV)to realise electrically variable transmission,but the sudden change of equivalent inertia caused by topological configuration recombinati...Mode shift is a special mechanism for a power-split hybrid electric vehicle(HEV)to realise electrically variable transmission,but the sudden change of equivalent inertia caused by topological configuration recombination during mode shift induces a significant torque shock.Therefore,a smooth transient process,among other concerns,typically associated with this category of vehicles,is of great importance.The present research aims to introduce a novel control strategy to manage the dynamic torque of multiple power sources and therefore im-prove ride comfort.To this end,a dynamic model of the objective power-split HEV is first built.To resolve the contention between vehicle jerk and clutch friction loss,a model predictive control(MPC)combined with control allocation(CA)is then designed for the clutch-engaged phase.To reduce the torque fluctuation caused by the inertia torques of multiple power sources,a dynamic compensation control strategy(DCcs)that coordinates motorgenerator torque to compensate for the transition torque is proposed for the brake-disengaged phase.Finally,the proposed control strategy is validated by simulation and bench test,and results show great potential in reducing shift duration,torque variation,vehicle jerk and friction loss(the simulation results show decreases of 22%,39%,83%and 53%,and the experimental results show decreases of 21%,74%,77%,and 59%,re-spectively),thereby improving shift quality.展开更多
This article proposes a Gaussian process(GP) based model predictive control(MPC) method to solve the tracking control of wheeled mobile robot( WMR) with uncertain model parameters.Firstly,a Gaussian process velocity p...This article proposes a Gaussian process(GP) based model predictive control(MPC) method to solve the tracking control of wheeled mobile robot( WMR) with uncertain model parameters.Firstly,a Gaussian process velocity prediction model is proposed to compensate for the unknown dynamic model,as the kinematic model cannot accurately characterize the motion characteristics of the robot.Then,by introducing the Lorentz function,the improved iterative linear quadratic regulator(iLQR) method is used to solve the nonlinear MPC(NMPC) controller with constraints.In addition,in order to reduce computational burden,a closed gradient calculation method is introduced to improve algorithm efficiency.Finally,the feasibility and effectiveness of this method are verified through simulation and experiment.展开更多
Dear Editor,This letter presents a model predictive control(MPC)scheme for human-robot interaction(HRI)in a multi-joint exoskeleton robot(ER)driven by series elastic actuator(SEA).The proposed scheme in robot-in-charg...Dear Editor,This letter presents a model predictive control(MPC)scheme for human-robot interaction(HRI)in a multi-joint exoskeleton robot(ER)driven by series elastic actuator(SEA).The proposed scheme in robot-in-charge(RIC)mode facilitates the ER driven by SEA to provide the required assistance and support for the subject.展开更多
Influenced by the subduction of the Paleo-Pacic plate,the eastern part of the North China Craton(NCC)underwent intense destruction during the Mesozoic,leading to complex magmatic activities and mineralization events t...Influenced by the subduction of the Paleo-Pacic plate,the eastern part of the North China Craton(NCC)underwent intense destruction during the Mesozoic,leading to complex magmatic activities and mineralization events that formed the Jiaodong and Liaodong gold districts.However,the currently gold proved reserves in two regions dier signicantly,sparking debate over the consistency of their controlling models.This study employs high-precision short-oset Controlled-Source Electromagnetic Method(CSEM)advanced detection technology,focusing on the Sanshandao fault in the Jiaodong district and the Jianshanzi fault in the Liaodong district,to reveal the ne-scale resistivity structures of deep controlling factors in both districts.A new structural-fluid joint controlling model for the NCC is proposed.In the Jiaodong district,the Sanshandao fault exhibits both ore-guiding and mineralization attributes,forming a structural-fluid joint step-like model.In contrast,the Qingchengzi area in the Liaodong district is shaped by the combined eects of stratigraphy,folds,and faults.Deep regional faults,induced by Mesozoic tectonomagmatic activities,intersect the strata and act as ore-guiding structures,while early strata form interlayer fracture zones under subsequent folding,constituting a structural-fluid joint ladder-like model.The ndings enrich the fault control theory of decratonic gold deposit and suggest that the gentle slopes of interlayer fracture zones around regional faults may be vital targets for future gold prospecting,indicating that substantial gold resources may yet be discovered in the Liaodong district.展开更多
With regards to the assembly line of cost control of Dechang company,the motor housing′s cost control of process will be necessarily appreciated.Though the supply quantity is large in a machine and the price of motor...With regards to the assembly line of cost control of Dechang company,the motor housing′s cost control of process will be necessarily appreciated.Though the supply quantity is large in a machine and the price of motor housing is cheaper,the cost control of automatic production line is significant in this respection.It is found that insufficiency power is main cause for production line failure.The cost control of equipment includes to structure wheel,conveyor and motor for benefit which also needs to be controlled in detail.Only in this way can we fundamentally resolve the main problem of high cost process.When the capital price L increases,Avc also increases too with nonlinear meantime,and Tc&Vc increase in proportion to K.Among them,the Tc increases the highest and then Vc increases lightly.Generally when labour quantity L and capital quantity K increase cost increases in proportion.展开更多
The control problems associated with vehicle height adjustment of electronically controlled air suspension （ECAS） still pose theoretical challenges for researchers, which manifest themselves in the publications on t...The control problems associated with vehicle height adjustment of electronically controlled air suspension （ECAS） still pose theoretical challenges for researchers, which manifest themselves in the publications on this subject over the last years. This paper deals with modeling and control of a vehicle height adjustment system for ECAS, which is an example of a hybrid dynamical system due to the coexistence and coupling of continuous variables and discrete events. A mixed logical dynamical （MLD） modeling approach is chosen for capturing enough details of the vehicle height adjustment process. The hybrid dynamic model is constructed on the basis of some assumptions and piecewise linear approximation for components nonlinearities. Then, the on-off statuses of solenoid valves and the piecewise approximation process are described by propositional logic, and the hybrid system is transformed into the set of linear mixed-integer equalities and inequalities, denoted as MLD model, automatically by HYSDEL. Using this model, a hybrid model predictive controller （HMPC） is tuned based on online mixed-integer quadratic optimization （MIQP）. Two different scenarios are considered in the simulation, whose results verify the height adjustment effectiveness of the proposed approach. Explicit solutions of the controller are computed to control the vehicle height adjustment system in realtime using an offline multi-parametric programming technology （MPT）, thus convert the controller into an equivalent explicit piecewise affine form. Finally, bench experiments for vehicle height lifting, holding and lowering procedures are conducted, which demonstrate that the HMPC can adjust the vehicle height by controlling the on-off statuses of solenoid valves directly. This research proposes a new modeling and control method for vehicle height adjustment of ECAS, which leads to a closed-loop system with favorable dynamical properties.展开更多
Nonlinear model predictive control(NMPC)scheme is an effective method of multi-objective optimization control in complex industrial systems.In this paper,a NMPC scheme for the wet limestone flue gas desulphurization(W...Nonlinear model predictive control(NMPC)scheme is an effective method of multi-objective optimization control in complex industrial systems.In this paper,a NMPC scheme for the wet limestone flue gas desulphurization(WFGD)system is proposed which provides a more flexible framework of optimal control and decision-making compared with PID scheme.At first,a mathematical model of the FGD process is deduced which is suitable for NMPC structure.To equipoise the model’s accuracy and conciseness,the wet limestone FGD system is separated into several modules.Based on the conservation laws,a model with reasonable simplification is developed to describe dynamics of different modules for the purpose of controller design.Then,by addressing economic objectives directly into the NMPC scheme,the NMPC controller can minimize economic cost and track the set-point simultaneously.The accuracy of model is validated by the field data of a 1000 MW thermal power plant in Henan Province,China.The simulation results show that the NMPC strategy improves the economic performance and ensures the emission requirement at the same time.In the meantime,the control scheme satisfies the multiobjective control requirements under complex operation conditions(e.g.,boiler load fluctuation and set point variation).The mathematical model and NMPC structure provides the basic work for the future development of advanced optimized control algorithms in the wet limestone FGD systems.展开更多
This paper provides an introduction to a support vector machine, a new kernel-based technique introduced in statistical learning theory and structural risk minimization, then presents a modeling-control framework base...This paper provides an introduction to a support vector machine, a new kernel-based technique introduced in statistical learning theory and structural risk minimization, then presents a modeling-control framework based on SVM. At last a numerical experiment is taken to demonstrate the proposed approach's correctness and effectiveness.展开更多
By analyzing the output voltage ripple of a buck-boost converter with large equivalent series resistance（ESR） of output capacitor, one valley voltage-mode controller for buck-boost converter is proposed. Considering...By analyzing the output voltage ripple of a buck-boost converter with large equivalent series resistance（ESR） of output capacitor, one valley voltage-mode controller for buck-boost converter is proposed. Considering the fact that the increasing and decreasing slopes of the inductor current are assumed to be constant during each switching cycle, an especial sampleddata model of valley voltage-mode controlled buck-boost converter is established. Based on this model, the dynamical effect of an output-capacitor time-constant on the valley voltage-mode controlled buck-boost converter is revealed and analyzed via the bifurcation diagrams, the movements of eigenvalues, the Lyapunov exponent spectra, the boundary equations,and the operating-state regions. It is found that with gradual reduction of output-capacitor time-constant, the buck-boost converter in continuous conduction mode（CCM） shows the evolutive dynamic behavior from period-1 to period-2, period-4, period-8, chaos, and invalid state. The stability boundary and the invalidated boundary are derived theoretically by stability analysis, where the stable state of valley voltage-mode controlled buck-boost converter can enter into an unstable state, and the converter can shift from the operation region to a forbidden region. These results verified by time-domain waveforms and phase portraits of both simulation and experiment indicate that the sampled-data model is correct and the time constant of the output capacitor is a critical factor for valley voltage-mode controlled buck-boost converter, which has a significant effect on the dynamics as well as control stability.展开更多
To develop a sound ozone（O_3） pollution control strategy,it is important to well understand and characterize the source contribution due to the complex chemical and physical formation processes of O_3.Using the ＂Sh...To develop a sound ozone（O_3） pollution control strategy,it is important to well understand and characterize the source contribution due to the complex chemical and physical formation processes of O_3.Using the ＂Shunde＂ city as a pilot summer case study,we apply an innovative response surface modeling（RSM） methodology based on the Community Multi-Scale Air Quality（CMAQ） modeling simulations to identify the O_3 regime and provide dynamic analysis of the precursor contributions to effectively assess the O_3 impacts of volatile organic compound（VOC） control strategy.Our results show that Shunde is a typical VOC-limited urban O_3 polluted city.The ＂Jiangmen＂ city,as the main upper wind area during July 2014,its VOCs and nitrogen oxides（NO_x） emissions make up the largest contribution（9.06%）.On the contrary,the contribution from local（Shunde） emission is lowest（6.35%） among the seven neighbor regions.The local VOCs industrial source emission has the largest contribution comparing to other precursor emission sectors in Shunde.The results of dynamic source contribution analysis further show that the local NO_x control could slightly increase the ground O_3 under low（10.00%） and medium（40.00%）reduction ratios,while it could start to turn positive to decrease ground O_3 under the high NO_x abatement ratio（75.00%）.The real-time assessment of O_3 impacts from VOCs control strategies in Pearl River Delta（PRD） shows that the joint regional VOCs emission control policy will effectively reduce the ground O_3 concentration in Shunde.展开更多
The pneumatic artificial muscles are widely used in the fields of medicalrobots, etc. Neural networks are applied to modeling and controlling of artificial muscle system. Asingle-joint artificial muscle test system is...The pneumatic artificial muscles are widely used in the fields of medicalrobots, etc. Neural networks are applied to modeling and controlling of artificial muscle system. Asingle-joint artificial muscle test system is designed. The recursive prediction error (RPE)algorithm which yields faster convergence than back propagation (BP) algorithm is applied to trainthe neural networks. The realization of RPE algorithm is given. The difference of modeling ofartificial muscles using neural networks with different input nodes and different hidden layer nodesis discussed. On this basis the nonlinear control scheme using neural networks for artificialmuscle system has been introduced. The experimental results show that the nonlinear control schemeyields faster response and higher control accuracy than the traditional linear control scheme.展开更多
A distributionally robust model predictive control(DRMPC)scheme is proposed based on neural network(NN)modeling to achieve the trajectory tracking control of robot manipulators with state and control torque constraint...A distributionally robust model predictive control(DRMPC)scheme is proposed based on neural network(NN)modeling to achieve the trajectory tracking control of robot manipulators with state and control torque constraints.First,an NN is used to fit the motion data of robot manipulators for data-driven dynamic modeling,converting it into a linear prediction model through gradients.Then,by statistically analyzing the stochastic characteristics of the NN modeling errors,a distributionally robust model predictive controller is designed based on the chance constraints,and the optimization problem is transformed into a tractable quadratic programming(QP)problem under the distributionally robust optimization(DRO)framework.The recursive feasibility and convergence of the proposed algorithm are proven.Finally,the effectiveness of the proposed algorithm is verified through numerical simulation.展开更多
Parallel connection of multiple inverters is an important means to solve the expansion,reserve and protection of distributed power generation,such as photovoltaics.In view of the shortcomings of traditional droop cont...Parallel connection of multiple inverters is an important means to solve the expansion,reserve and protection of distributed power generation,such as photovoltaics.In view of the shortcomings of traditional droop control methods such as weak anti-interference ability,low tracking accuracy of inverter output voltage and serious circulation phenomenon,a finite control set model predictive control(FCS-MPC)strategy of microgrid multiinverter parallel system based on Mixed Logical Dynamical(MLD)modeling is proposed.Firstly,the MLD modeling method is introduced logical variables,combining discrete events and continuous events to form an overall differential equation,which makes the modeling more accurate.Then a predictive controller is designed based on the model,and constraints are added to the objective function,which can not only solve the real-time changes of the control system by online optimization,but also effectively obtain a higher tracking accuracy of the inverter output voltage and lower total harmonic distortion rate(Total Harmonics Distortion,THD);and suppress the circulating current between the inverters,to obtain a good dynamic response.Finally,the simulation is carried out onMATLAB/Simulink to verify the correctness of the model and the rationality of the proposed strategy.This paper aims to provide guidance for the design and optimal control of multi-inverter parallel systems.展开更多
The control of civil, energy, and power systems presents significant new challenges for modeling and control theory. The purpose of this special issue is to provide a forum for researchers and practical engineers to d...The control of civil, energy, and power systems presents significant new challenges for modeling and control theory. The purpose of this special issue is to provide a forum for researchers and practical engineers to discuss the recent advances in modeling and control technology development for civil, energy, and power systems. Prospective authors are invited to submit their original contributions with the focus on theory or applications of modeling, control and decision. Topics of interest include modeling and control of, but are not limited to, the following：展开更多
The modeling and motion control of a universal part feeder is addressed. The feeder consists of a flat plate （or called bed） and a part placed on the plate. The bed can vibrate side-by-side （in x axis）, back and f...The modeling and motion control of a universal part feeder is addressed. The feeder consists of a flat plate （or called bed） and a part placed on the plate. The bed can vibrate side-by-side （in x axis）, back and forth （in y axis）, clockwise and counter clockwise （about z axis）, actuated by three linear motors （voice coils）. When the bed does these vibrations, the part placed on the plat will have position and/or orientation change due to the interaction between the two contact surfaces. By controlling the ways in which the plate vibrates, the position and orientation of the part can be controlled. The two vibration profiles of the bed are investigated in the research： the high-low vibration mode and the bang-bang vibration mode. The motion equations of the bed and the part as well as the control schemes for the high-low vibration mode are presented. Both simulation and real-time testing verify the system＇s dynamic model and indicate the feasibilities of the developed control laws.展开更多
The correlation between the grain size of electrodeposited coatings and the current densities was modeled by considering galvanostatic conditions. In order to test the model by experimental results, nanocrystalline ...The correlation between the grain size of electrodeposited coatings and the current densities was modeled by considering galvanostatic conditions. In order to test the model by experimental results, nanocrystalline （NC） nickel samples were deposited at different current densities using a Watts bath. The grain size of the deposits was evaluated by X-ray diffraction （XRD） technique. Model predictions were validated by finding a curve being the best-fit to the experimental results which were gathered from literature for different NC coatings in addition to those data measured in this research for NC nickel coatings. According to our model, the variation of grain size with the reciprocal of the current density follows a power law. A good agreement between the experimental results and model predictions was observed which indicated that the derived analytical model is applicable for producting the nanocrystalline electrodeposits with the desired grain size by controling current density.展开更多
基金Open access funding provided by The Science,Technology&Innovation Funding Authority(STDF)in cooperation with The Egyptian Knowledge Bank(EKB).
文摘Soft robotic manipulators represent a rapidly evolving field characterized by inherent compliance,adaptability,and safe interactions within unstructured environments.Over the past decade(2015-2025),significant advancements have trans-formed their capabilities through novel designs inspired by biological systems,advanced modeling frameworks,sophisti-cated control strategies,and integration into diverse real-world applications.Recent innovations in multifunctional mate-rials and emerging actuation technologies have markedly expanded manipulator performance,reliability,and dexterity.Concurrently,developments in modeling have progressed from simplified geometric methods toward highly accurate physics-based and hybrid data-driven approaches,substantially improving real-time prediction and controllability.Coupled with these developments,adaptive and robust control strategies-including learning-based techniques-have enabled unprec-edented autonomy and precision in challenging application domains such as Minimally Invasive Surgery(MIS),precision agriculture,deep-sea exploration,disaster recovery,and space missions.Despite these remarkable strides,key challenges remain,notably regarding scalability,long-term material durability,robust integrated sensing,and standardized evaluation procedures.This review comprehensively synthesizes recent advances,critically evaluates state-of-the-art methodologies,and systematically identifies existing gaps to provide a clear roadmap and targeted research directions,guiding future developments toward the broader adoption and optimal utilization of soft robotic manipulators.
文摘Fluctuating voltage levels in power grids necessitate automatic voltage regulators(AVRs)to ensure stability.This study examined the modeling and control of AVR in hydroelectric power plants using model predictive control(MPC),which utilizes an extensive mathe-matical model of the voltage regulation system to optimize the control actions over a defined prediction horizon.This predictive feature enables MPC to minimize voltage deviations while accounting for operational constraints,thereby improving stability and performance under dynamic conditions.Thefindings were compared with those derived from an optimal proportional integral derivative(PID)con-troller designed using the artificial bee colony(ABC)algorithm.Although the ABC-PID method adjusts the PID parameters based on historical data,it may be difficult to adapt to real-time changes in system dynamics under constraints.Comprehensive simulations assessed both frameworks,emphasizing performance metrics such as disturbance rejection,response to load changes,and resilience to uncertainties.The results show that both MPC and ABC-PID methods effectively achieved accurate voltage regulation;however,MPC excelled in controlling overshoot and settling time—recording 0.0%and 0.25 s,respectively.This demonstrates greater robustness compared to conventional control methods that optimize PID parameters based on performance criteria derived from actual system behavior,which exhibited settling times and overshoots exceeding 0.41 s and 5.0%,respectively.The controllers were implemented using MATLAB/Simulink software,indicating a significant advancement for power plant engineers pursuing state-of-the-art automatic voltage regulations.
基金supported by the Science and Technology Project of State Grid Jiangsu Electric Power Co.,Ltd.(J2024162).
文摘With the growing integration of renewable energy sources(RESs)and smart interconnected devices,conventional distribution networks have turned to active distribution networks(ADNs)with complex system model and power flow dynamics.The rapid fluctuation of RES power may easily result in frequent voltage violation issues.Taking the flexible RES reactive power as control variables,this paper proposes a two-layer control scheme with Koopman wide neural network(WNN)based model predictive control(MPC)method for optimal voltage regulation and network loss reduction.Based on Koopman operator theory,a data-driven WNN method is presented to fit a high-dimensional linear model of power flow.With the model,voltage and network loss sensitivities are computed analytically,and utilized for ADN partition and control model formulation.In the lower level,a dual-mode adaptive switching MPC strategy is put forward for optimal voltage control and network loss optimization in each individual partition to decide the RES reactive power.The upper level is to calculate the adjustment coefficients of the RES reactive power given in the low level by taking the coupling effects of different partitions into account,and then the final reactive power dispatches of RESs are obtained to realize optimal control of voltage and network loss.Simulation results on two ADNs demonstrate that the proposed strategy can reliably maintain the voltage at each node within the secure range,reduce network power losses,and enhance the overall system security and economic efficiency.
基金supported by the National Natural Science Foundation of China(No.52372371)。
文摘In modern gas turbine engine control,Direct Thrust Control(DTC) is an effective method for achieving the desired thrust.Model Predictive Control(MPC) has the characteristics of handling constraints while accomplishing command tracking,making it a promising approach for implementing DTC.However,since the performance of DTC is highly sensitive to MPC's tuning parameters,developing an efficient optimization strategy for these parameters becomes imperative.Therefore,a Model Predictive Direct Thrust Control(MP-DTC) architecture is designed,and its asymptotic stability is proven.Additionally,the influence of the tuning parameters on control performance is analyzed.Then,a tuning strategy for MP-DTC architecture is proposed.This strategy combines the objectives of DTC to design a Multi-Objective Optimization(MOO) index function,and uses Multi-Objective Grey Wolf Optimizer(MOGWO) to solve its Pareto front and obtain the tuning parameters.In the Hardware-in-Loop(HIL) experiments,the proposed MPDTC architecture achieves the shortest settling time and smallest overshoot compared to the latest DTC scheme.Its MOGWO-based tuning strategy provides more Pareto-optimal solutions,ensuring optimal selection based on rapidity and stability,and maintains precise DTC even under component degradation and various operating conditions,thereby providing robustness,optimality,and generalizability.
基金Supported by National Natural Science Foundation of China(Grant Nos.52005039,51575043,51975048,U1764257).
文摘Mode shift is a special mechanism for a power-split hybrid electric vehicle(HEV)to realise electrically variable transmission,but the sudden change of equivalent inertia caused by topological configuration recombination during mode shift induces a significant torque shock.Therefore,a smooth transient process,among other concerns,typically associated with this category of vehicles,is of great importance.The present research aims to introduce a novel control strategy to manage the dynamic torque of multiple power sources and therefore im-prove ride comfort.To this end,a dynamic model of the objective power-split HEV is first built.To resolve the contention between vehicle jerk and clutch friction loss,a model predictive control(MPC)combined with control allocation(CA)is then designed for the clutch-engaged phase.To reduce the torque fluctuation caused by the inertia torques of multiple power sources,a dynamic compensation control strategy(DCcs)that coordinates motorgenerator torque to compensate for the transition torque is proposed for the brake-disengaged phase.Finally,the proposed control strategy is validated by simulation and bench test,and results show great potential in reducing shift duration,torque variation,vehicle jerk and friction loss(the simulation results show decreases of 22%,39%,83%and 53%,and the experimental results show decreases of 21%,74%,77%,and 59%,re-spectively),thereby improving shift quality.
基金Supported by the Zhejiang Provincial Natural Science Foundation of China (No.LR23F030002)。
文摘This article proposes a Gaussian process(GP) based model predictive control(MPC) method to solve the tracking control of wheeled mobile robot( WMR) with uncertain model parameters.Firstly,a Gaussian process velocity prediction model is proposed to compensate for the unknown dynamic model,as the kinematic model cannot accurately characterize the motion characteristics of the robot.Then,by introducing the Lorentz function,the improved iterative linear quadratic regulator(iLQR) method is used to solve the nonlinear MPC(NMPC) controller with constraints.In addition,in order to reduce computational burden,a closed gradient calculation method is introduced to improve algorithm efficiency.Finally,the feasibility and effectiveness of this method are verified through simulation and experiment.
基金supported in part by the National Natural Science Foundation of China(62173048,62373065,61873304,62106023)the Key Science and Technology Projects of Jilin Province,China(20230204081YY)the Research and Innovation Team of Anhui Province(2024AH010023)。
文摘Dear Editor,This letter presents a model predictive control(MPC)scheme for human-robot interaction(HRI)in a multi-joint exoskeleton robot(ER)driven by series elastic actuator(SEA).The proposed scheme in robot-in-charge(RIC)mode facilitates the ER driven by SEA to provide the required assistance and support for the subject.
基金supported by a project from the Ministry of Science and Technology National Key R&D Program of China(2024ZD10028052022YFC2903505)National Natural Science Foundation of China(42030106).
文摘Influenced by the subduction of the Paleo-Pacic plate,the eastern part of the North China Craton(NCC)underwent intense destruction during the Mesozoic,leading to complex magmatic activities and mineralization events that formed the Jiaodong and Liaodong gold districts.However,the currently gold proved reserves in two regions dier signicantly,sparking debate over the consistency of their controlling models.This study employs high-precision short-oset Controlled-Source Electromagnetic Method(CSEM)advanced detection technology,focusing on the Sanshandao fault in the Jiaodong district and the Jianshanzi fault in the Liaodong district,to reveal the ne-scale resistivity structures of deep controlling factors in both districts.A new structural-fluid joint controlling model for the NCC is proposed.In the Jiaodong district,the Sanshandao fault exhibits both ore-guiding and mineralization attributes,forming a structural-fluid joint step-like model.In contrast,the Qingchengzi area in the Liaodong district is shaped by the combined eects of stratigraphy,folds,and faults.Deep regional faults,induced by Mesozoic tectonomagmatic activities,intersect the strata and act as ore-guiding structures,while early strata form interlayer fracture zones under subsequent folding,constituting a structural-fluid joint ladder-like model.The ndings enrich the fault control theory of decratonic gold deposit and suggest that the gentle slopes of interlayer fracture zones around regional faults may be vital targets for future gold prospecting,indicating that substantial gold resources may yet be discovered in the Liaodong district.
文摘With regards to the assembly line of cost control of Dechang company,the motor housing′s cost control of process will be necessarily appreciated.Though the supply quantity is large in a machine and the price of motor housing is cheaper,the cost control of automatic production line is significant in this respection.It is found that insufficiency power is main cause for production line failure.The cost control of equipment includes to structure wheel,conveyor and motor for benefit which also needs to be controlled in detail.Only in this way can we fundamentally resolve the main problem of high cost process.When the capital price L increases,Avc also increases too with nonlinear meantime,and Tc&Vc increase in proportion to K.Among them,the Tc increases the highest and then Vc increases lightly.Generally when labour quantity L and capital quantity K increase cost increases in proportion.
基金Supported by National Natural Science Foundation of China(Grant No.51375212)Priority Academic Program Development(PAPD)of Jiangsu Higher Education Institutions of China+1 种基金Research Fund for the Doctoral Program of Higher Education of China(Grant No.20133227130001)China Postdoctoral Science Foundation(Grant No.2014M551518)
文摘The control problems associated with vehicle height adjustment of electronically controlled air suspension （ECAS） still pose theoretical challenges for researchers, which manifest themselves in the publications on this subject over the last years. This paper deals with modeling and control of a vehicle height adjustment system for ECAS, which is an example of a hybrid dynamical system due to the coexistence and coupling of continuous variables and discrete events. A mixed logical dynamical （MLD） modeling approach is chosen for capturing enough details of the vehicle height adjustment process. The hybrid dynamic model is constructed on the basis of some assumptions and piecewise linear approximation for components nonlinearities. Then, the on-off statuses of solenoid valves and the piecewise approximation process are described by propositional logic, and the hybrid system is transformed into the set of linear mixed-integer equalities and inequalities, denoted as MLD model, automatically by HYSDEL. Using this model, a hybrid model predictive controller （HMPC） is tuned based on online mixed-integer quadratic optimization （MIQP）. Two different scenarios are considered in the simulation, whose results verify the height adjustment effectiveness of the proposed approach. Explicit solutions of the controller are computed to control the vehicle height adjustment system in realtime using an offline multi-parametric programming technology （MPT）, thus convert the controller into an equivalent explicit piecewise affine form. Finally, bench experiments for vehicle height lifting, holding and lowering procedures are conducted, which demonstrate that the HMPC can adjust the vehicle height by controlling the on-off statuses of solenoid valves directly. This research proposes a new modeling and control method for vehicle height adjustment of ECAS, which leads to a closed-loop system with favorable dynamical properties.
基金Financial support from the National Key R&D Program of China(No.2017YFB0601805)。
文摘Nonlinear model predictive control(NMPC)scheme is an effective method of multi-objective optimization control in complex industrial systems.In this paper,a NMPC scheme for the wet limestone flue gas desulphurization(WFGD)system is proposed which provides a more flexible framework of optimal control and decision-making compared with PID scheme.At first,a mathematical model of the FGD process is deduced which is suitable for NMPC structure.To equipoise the model’s accuracy and conciseness,the wet limestone FGD system is separated into several modules.Based on the conservation laws,a model with reasonable simplification is developed to describe dynamics of different modules for the purpose of controller design.Then,by addressing economic objectives directly into the NMPC scheme,the NMPC controller can minimize economic cost and track the set-point simultaneously.The accuracy of model is validated by the field data of a 1000 MW thermal power plant in Henan Province,China.The simulation results show that the NMPC strategy improves the economic performance and ensures the emission requirement at the same time.In the meantime,the control scheme satisfies the multiobjective control requirements under complex operation conditions(e.g.,boiler load fluctuation and set point variation).The mathematical model and NMPC structure provides the basic work for the future development of advanced optimized control algorithms in the wet limestone FGD systems.
文摘This paper provides an introduction to a support vector machine, a new kernel-based technique introduced in statistical learning theory and structural risk minimization, then presents a modeling-control framework based on SVM. At last a numerical experiment is taken to demonstrate the proposed approach's correctness and effectiveness.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61371033 and 51407054)the Foundation for the Author of National Excellent Doctoral Dissertation of China(Grant No.201442)the Fundamental Research Funds for the Central Universities of China(Grant No.2682016CX035)
文摘By analyzing the output voltage ripple of a buck-boost converter with large equivalent series resistance（ESR） of output capacitor, one valley voltage-mode controller for buck-boost converter is proposed. Considering the fact that the increasing and decreasing slopes of the inductor current are assumed to be constant during each switching cycle, an especial sampleddata model of valley voltage-mode controlled buck-boost converter is established. Based on this model, the dynamical effect of an output-capacitor time-constant on the valley voltage-mode controlled buck-boost converter is revealed and analyzed via the bifurcation diagrams, the movements of eigenvalues, the Lyapunov exponent spectra, the boundary equations,and the operating-state regions. It is found that with gradual reduction of output-capacitor time-constant, the buck-boost converter in continuous conduction mode（CCM） shows the evolutive dynamic behavior from period-1 to period-2, period-4, period-8, chaos, and invalid state. The stability boundary and the invalidated boundary are derived theoretically by stability analysis, where the stable state of valley voltage-mode controlled buck-boost converter can enter into an unstable state, and the converter can shift from the operation region to a forbidden region. These results verified by time-domain waveforms and phase portraits of both simulation and experiment indicate that the sampled-data model is correct and the time constant of the output capacitor is a critical factor for valley voltage-mode controlled buck-boost converter, which has a significant effect on the dynamics as well as control stability.
基金Financial support for this work is provided by the Shunde Environment ProtectionTransportation and Urban Administration Bureau(no.0851-1361FS02CL51)+5 种基金the Guangdong Provincial Science and Technology Plan Projects(no.2014A050503019)Guangzhou Environmental Protection Bureau(no.x2hjB2150020)supported by the funding of State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complexthe project of Atmospheric Haze Collaboration Control Technology Design(no.XDB05030400)from Chinese Academy of Sciencesthe Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund(U1501501)(the second phase)the Guangdong Provincial Engineering and Technology Research Center for Environmental Risk Prevention and Emergency Disposal(no.b2152120)
文摘To develop a sound ozone（O_3） pollution control strategy,it is important to well understand and characterize the source contribution due to the complex chemical and physical formation processes of O_3.Using the ＂Shunde＂ city as a pilot summer case study,we apply an innovative response surface modeling（RSM） methodology based on the Community Multi-Scale Air Quality（CMAQ） modeling simulations to identify the O_3 regime and provide dynamic analysis of the precursor contributions to effectively assess the O_3 impacts of volatile organic compound（VOC） control strategy.Our results show that Shunde is a typical VOC-limited urban O_3 polluted city.The ＂Jiangmen＂ city,as the main upper wind area during July 2014,its VOCs and nitrogen oxides（NO_x） emissions make up the largest contribution（9.06%）.On the contrary,the contribution from local（Shunde） emission is lowest（6.35%） among the seven neighbor regions.The local VOCs industrial source emission has the largest contribution comparing to other precursor emission sectors in Shunde.The results of dynamic source contribution analysis further show that the local NO_x control could slightly increase the ground O_3 under low（10.00%） and medium（40.00%）reduction ratios,while it could start to turn positive to decrease ground O_3 under the high NO_x abatement ratio（75.00%）.The real-time assessment of O_3 impacts from VOCs control strategies in Pearl River Delta（PRD） shows that the joint regional VOCs emission control policy will effectively reduce the ground O_3 concentration in Shunde.
基金This project is supported by Foundation of Public Laboratory on Robotics of Chinese Academy of Sciences.
文摘The pneumatic artificial muscles are widely used in the fields of medicalrobots, etc. Neural networks are applied to modeling and controlling of artificial muscle system. Asingle-joint artificial muscle test system is designed. The recursive prediction error (RPE)algorithm which yields faster convergence than back propagation (BP) algorithm is applied to trainthe neural networks. The realization of RPE algorithm is given. The difference of modeling ofartificial muscles using neural networks with different input nodes and different hidden layer nodesis discussed. On this basis the nonlinear control scheme using neural networks for artificialmuscle system has been introduced. The experimental results show that the nonlinear control schemeyields faster response and higher control accuracy than the traditional linear control scheme.
基金Project supported by the National Natural Science Foundation of China(Nos.62273245 and 62173033)the Sichuan Science and Technology Program of China(No.2024NSFSC1486)the Opening Project of Robotic Satellite Key Laboratory of Sichuan Province of China。
文摘A distributionally robust model predictive control(DRMPC)scheme is proposed based on neural network(NN)modeling to achieve the trajectory tracking control of robot manipulators with state and control torque constraints.First,an NN is used to fit the motion data of robot manipulators for data-driven dynamic modeling,converting it into a linear prediction model through gradients.Then,by statistically analyzing the stochastic characteristics of the NN modeling errors,a distributionally robust model predictive controller is designed based on the chance constraints,and the optimization problem is transformed into a tractable quadratic programming(QP)problem under the distributionally robust optimization(DRO)framework.The recursive feasibility and convergence of the proposed algorithm are proven.Finally,the effectiveness of the proposed algorithm is verified through numerical simulation.
基金supported by the Major Science and Technology Projects of Gansu Province(Grant No.20ZD7GF011)Gansu Province Higher Education Industry Support Plan Project:Research on the Collaborative Operation of Solar Thermal Storage+Wind-Solar Hybrid Power Generation--Based on“Integrated Energy Demonstration of Wind-Solar Energy Storage in Gansu Province”(Project No.2022CYZC-34).
文摘Parallel connection of multiple inverters is an important means to solve the expansion,reserve and protection of distributed power generation,such as photovoltaics.In view of the shortcomings of traditional droop control methods such as weak anti-interference ability,low tracking accuracy of inverter output voltage and serious circulation phenomenon,a finite control set model predictive control(FCS-MPC)strategy of microgrid multiinverter parallel system based on Mixed Logical Dynamical(MLD)modeling is proposed.Firstly,the MLD modeling method is introduced logical variables,combining discrete events and continuous events to form an overall differential equation,which makes the modeling more accurate.Then a predictive controller is designed based on the model,and constraints are added to the objective function,which can not only solve the real-time changes of the control system by online optimization,but also effectively obtain a higher tracking accuracy of the inverter output voltage and lower total harmonic distortion rate(Total Harmonics Distortion,THD);and suppress the circulating current between the inverters,to obtain a good dynamic response.Finally,the simulation is carried out onMATLAB/Simulink to verify the correctness of the model and the rationality of the proposed strategy.This paper aims to provide guidance for the design and optimal control of multi-inverter parallel systems.
文摘The control of civil, energy, and power systems presents significant new challenges for modeling and control theory. The purpose of this special issue is to provide a forum for researchers and practical engineers to discuss the recent advances in modeling and control technology development for civil, energy, and power systems. Prospective authors are invited to submit their original contributions with the focus on theory or applications of modeling, control and decision. Topics of interest include modeling and control of, but are not limited to, the following：
文摘The modeling and motion control of a universal part feeder is addressed. The feeder consists of a flat plate （or called bed） and a part placed on the plate. The bed can vibrate side-by-side （in x axis）, back and forth （in y axis）, clockwise and counter clockwise （about z axis）, actuated by three linear motors （voice coils）. When the bed does these vibrations, the part placed on the plat will have position and/or orientation change due to the interaction between the two contact surfaces. By controlling the ways in which the plate vibrates, the position and orientation of the part can be controlled. The two vibration profiles of the bed are investigated in the research： the high-low vibration mode and the bang-bang vibration mode. The motion equations of the bed and the part as well as the control schemes for the high-low vibration mode are presented. Both simulation and real-time testing verify the system＇s dynamic model and indicate the feasibilities of the developed control laws.
文摘The correlation between the grain size of electrodeposited coatings and the current densities was modeled by considering galvanostatic conditions. In order to test the model by experimental results, nanocrystalline （NC） nickel samples were deposited at different current densities using a Watts bath. The grain size of the deposits was evaluated by X-ray diffraction （XRD） technique. Model predictions were validated by finding a curve being the best-fit to the experimental results which were gathered from literature for different NC coatings in addition to those data measured in this research for NC nickel coatings. According to our model, the variation of grain size with the reciprocal of the current density follows a power law. A good agreement between the experimental results and model predictions was observed which indicated that the derived analytical model is applicable for producting the nanocrystalline electrodeposits with the desired grain size by controling current density.
