Design of general multivariable process controllers is an attractive and practical alternative to optimizing design by evolutionary algorithms (EAs) since it can be formulated as an optimization problem. A closed-loop...Design of general multivariable process controllers is an attractive and practical alternative to optimizing design by evolutionary algorithms (EAs) since it can be formulated as an optimization problem. A closed-loop particle swarm optimization (CLPSO) algorithm is proposed by mapping PSO elements into the closed-loop system based on control theories. At each time step, a proportional integral (PI) controller is used to calculate an updated inertia weight for each particle in swarms from its last fitness. With this modification, limitations caused by a uniform inertia weight for the whole population are avoided, and the particles have enough diversity. After the effectiveness, efficiency and robustness are tested by benchmark functions, CLPSO is applied to design a multivariable proportional-integral-derivative (PID) controller for a solvent dehydration tower in a chemical plant and has improved its performances.展开更多
To improve the energy efficiency of a direct expansion air conditioning(DX A/C) system while guaranteeing occupancy comfort, a hierarchical controller for a DX A/C system with uncertain parameters is proposed. The con...To improve the energy efficiency of a direct expansion air conditioning(DX A/C) system while guaranteeing occupancy comfort, a hierarchical controller for a DX A/C system with uncertain parameters is proposed. The control strategy consists of an open loop optimization controller and a closed-loop guaranteed cost periodically intermittent-switch controller(GCPISC). The error dynamics system of the closed-loop control is modelled based on the GCPISC principle. The difference,compared to the previous DX A/C system control methods, is that the controller designed in this paper performs control at discrete times. For the ease of designing the controller, a series of matrix inequalities are derived to be the sufficient conditions of the lower-layer closed-loop GCPISC controller. In this way, the DX A/C system output is derived to follow the optimal references obtained through the upper-layer open loop controller in exponential time, and the energy efficiency of the system is improved. Moreover, a static optimization problem is addressed for obtaining an optimal GCPISC law to ensure a minimum upper bound on the DX A/C system performance considering energy efficiency and output tracking error. The advantages of the designed hierarchical controller for a DX A/C system with uncertain parameters are demonstrated through some simulation results.展开更多
Additive manufacturing(AM)promotes the production of metallic parts with significant design flexibility,yet its use in critical applications is hindered by challenges in ensuring consistent quality and performance.Pro...Additive manufacturing(AM)promotes the production of metallic parts with significant design flexibility,yet its use in critical applications is hindered by challenges in ensuring consistent quality and performance.Process variability often leads to defects,insufficient geometric accuracy and inadequate material properties,which are difficult to effectively manage due to limitations of traditional quality control methods in modeling highdimensional nonlinear relationships and enabling adaptive control.Machine learning(ML)offers a transformative approach to model intricate process-structure-property relationships by leveraging the rich data environment of AM.The study presents a comprehensive examination of ML-driven quality assurance implementations in metallic AM.First,it uniquely examines the innovative exploration of ML in predicting and understanding the fundamental multi-physics fields that influence the quality of a fabricated component,including temperature fields,fluid dynamics and stress/strain evolution.Subsequently,the application of ML in optimizing key quality attributes,including defect detection and mitigation(porosity,cracks,etc.),geometric fidelity enhancement(dimensional accuracy,surface roughness,etc.)and material property tailoring(mechanical strength,fatigue life,corrosion resistance,etc.),are discussed in detail.Finally,the development of ML-driven real-time closed-loop control systems for intelligent quality assurance,the strategies for addressing the data scarcity and cross-scenario transferability in metal AM are discussed.This article provides a novel perspective on the profound potential of ML technology for metal AM quality control applications,highlights the challenges faced during research,and outlines future development directions.展开更多
Dear Editor,This letter studies a real-world issue in leader-follower multi-agent systems(MASs)named open topology,which permits the variations of agent set and network connections.Specially,a novel transition process...Dear Editor,This letter studies a real-world issue in leader-follower multi-agent systems(MASs)named open topology,which permits the variations of agent set and network connections.Specially,a novel transition process is developed to explain how the involved variation of network scale affects the dynamic behavior of the MASs.From a resource limited perspective,the distributed saturated impulsive control is then designed,under which some sufficient criteria are integrated into local quasi-consensus performance.We also provide a combined optimization algorithm for all agents to make the estimated domain of initial errors closer to the real one,thereby resulting in less conservativeness.Finally,a numerical example validates our results.展开更多
An optimal control problem is studied for a linear mean-field stochastic differential equation with a quadratic cost functional.The coefficients and the weighting matrices in the cost functional are all assumed to be ...An optimal control problem is studied for a linear mean-field stochastic differential equation with a quadratic cost functional.The coefficients and the weighting matrices in the cost functional are all assumed to be deterministic.Closedloop strategies are introduced,which require to be independent of initial states;and such a nature makes it very useful and convenient in applications.In this paper,the existence of an optimal closed-loop strategy for the system(also called the closedloop solvability of the problem)is characterized by the existence of a regular solution to the coupled two(generalized)Riccati equations,together with some constraints on the adapted solution to a linear backward stochastic differential equation and a linear terminal value problem of an ordinary differential equation.展开更多
The paper presents a preview controller design for ATS （active trailer steering） systems to improve high-speed stability of AHVs （articulated heavy vehicles）. An AHV consists of a towing unit, namely tractor or tr...The paper presents a preview controller design for ATS （active trailer steering） systems to improve high-speed stability of AHVs （articulated heavy vehicles）. An AHV consists of a towing unit, namely tractor or truck, and one or more towed units which called trailers. Individual units are connected to one another at articulated joints by mechanical couplings. Due to the multi-unit configurations, AHVs exhibit unique unstable motion modes, including jack-knifing, trailer swing and rollover. These unstable motion modes are the leading cause of highway accidents. To prevent these unstable motion modes, the preview controller, namely the LPDP （lateral position deviation preview） controller, is proposed. For a truck/full-trailer combination, the LPDP controller is designed to control the steering of the front and rear axle wheels of the trailing unit. The calculation of the corrective steering angle of the trailer front axle wheels is based on the preview information of the lateral position deviation of the trajectory of the axle center from that of the truck front axle center. Similarly, the steering angle of the trailer rear axle wheels is calculated by using the lateral position deviation of the trajectory of the axle center from that of the truck front axle. To perform closed-loop dynamic simulations and evaluate the vehicle performance measure, a driver model is introduced and it ＇derives＇ the AHV model based on well-defined testing specifications. The proposed preview control scheme in the continuous time domain is developed by using the LQR （linear quadratic regular） technique. The closed-loop simulation results indicate that the performance of the AHV with the LPDP controller is improved by decreasing rearward amplification ratio from the baseline value of 1.28 to 0.98 and reducing transient off-tracking by 95.03%. The proposed LPDP control algorithm provides an alternative method for the design optimization of AHVs with ATS systems.展开更多
Production optimal control technologies have become important tools for efficiently developing oil and gas reservoirs in recent years.This paper presents an overview of the research and application of these technologi...Production optimal control technologies have become important tools for efficiently developing oil and gas reservoirs in recent years.This paper presents an overview of the research and application of these technologies in smart oilfield,including reservoir data matching and prediction,well production optimization,and automatic well monitoring and control technologies.With the support of the National Natural Science Foundation of China,we made years of effort and finally derived a novel data—driven reservoir data matching and prediction methods.Besides,the new automatic optimization technologies and flow monitoring and control devices were also presented.The proposed technologies helped improve the computational efficiency by hundreds of times compared to traditional technologies.The real-time optimization and control of the injection and production parameters was realized using the proposed technologies,which have been widely applied in actual reservoirs at home and abroad,achieving significant economic benefits.展开更多
Purpose–The purpose of this paper is to develop an automatic control system for mechanical ventilation therapy based on the open lung concept(OLC)using artificial intelligence.In addition,mean arterial blood pressure...Purpose–The purpose of this paper is to develop an automatic control system for mechanical ventilation therapy based on the open lung concept(OLC)using artificial intelligence.In addition,mean arterial blood pressure(MAP)is stabilized by means of a decoupling controller with automated noradrenaline(NA)dosage to ensure adequate systemic perfusion during ventilation therapy for patients with acute respiratory distress syndrome(ARDS).Design/methodology/approach–The aim is to develop an automatic control system for mechanical ventilation therapy based on the OLC using artificial intelligence.In addition,MAP is stabilized by means of a decoupling controller with automated NA dosage to ensure adequate systemic perfusion during ventilation therapy for patients with ARDS.Findings–Thisinnovativeclosed-loop mechanicalventilation system leadsto a significant improvement in oxygenation,regulates end-tidal carbon dioxide for appropriate gas exchange and stabilizes MAP to guarantee proper systemic perfusion during the ventilation therapy.Research limitations/implications–Currently,this automatic ventilation system based on the OLC can only be applied in animal trials;for clinical use,such a system generally requires a mechanical ventilator and sensors with medical approval for humans.Practical implications–For implementation of a closed-loop ventilation system,reliable signals from the sensors are a prerequisite for successful application.Originality/value–Theexperiment with porcine dynamics demonstrates thefeasibility and usefulness of this automatic closed-loop ventilation therapy,with hemodynamic control for severe ARDS.Moreover,this pilot study validated a new algorithm for implementation of the OLC,whereby all control objectives are fulfilled during the ventilation therapy with adequate hemodynamic control of patients with ARDS.展开更多
文摘Design of general multivariable process controllers is an attractive and practical alternative to optimizing design by evolutionary algorithms (EAs) since it can be formulated as an optimization problem. A closed-loop particle swarm optimization (CLPSO) algorithm is proposed by mapping PSO elements into the closed-loop system based on control theories. At each time step, a proportional integral (PI) controller is used to calculate an updated inertia weight for each particle in swarms from its last fitness. With this modification, limitations caused by a uniform inertia weight for the whole population are avoided, and the particles have enough diversity. After the effectiveness, efficiency and robustness are tested by benchmark functions, CLPSO is applied to design a multivariable proportional-integral-derivative (PID) controller for a solvent dehydration tower in a chemical plant and has improved its performances.
基金supported by the National Natural Science Foundation of China(61773220,61876192,61907021)the National Natural Science Foundation of Hubei(ZRMS2019000752)+2 种基金the Fundamental Research Funds for the Central Universities(2662018QD057,CZT20022,CZT20020)Academic Team in Universities(KTZ20051)School Talent Funds(YZZ19004)。
文摘To improve the energy efficiency of a direct expansion air conditioning(DX A/C) system while guaranteeing occupancy comfort, a hierarchical controller for a DX A/C system with uncertain parameters is proposed. The control strategy consists of an open loop optimization controller and a closed-loop guaranteed cost periodically intermittent-switch controller(GCPISC). The error dynamics system of the closed-loop control is modelled based on the GCPISC principle. The difference,compared to the previous DX A/C system control methods, is that the controller designed in this paper performs control at discrete times. For the ease of designing the controller, a series of matrix inequalities are derived to be the sufficient conditions of the lower-layer closed-loop GCPISC controller. In this way, the DX A/C system output is derived to follow the optimal references obtained through the upper-layer open loop controller in exponential time, and the energy efficiency of the system is improved. Moreover, a static optimization problem is addressed for obtaining an optimal GCPISC law to ensure a minimum upper bound on the DX A/C system performance considering energy efficiency and output tracking error. The advantages of the designed hierarchical controller for a DX A/C system with uncertain parameters are demonstrated through some simulation results.
基金supported by the National Key R&D Program of China(No.2024YFB4609700)Major Research Plan of the National Natural Science Foundation of China(No.92266102)+4 种基金National Natural Science Foundation of China(No.52271135,No.52433016)Open project of Key Laboratory of Green Fabrication and Surface Technology of Advanced Metal Materials,China(No.GFST2024KF05)Innovative Research Group Project of Hubei Provincial Natural Science Foundation,China(No.2025AFA014)ECU DVC Strategic Research Support Fund,Australia(No.23965)Natural Science Foundation of Hubei Province,China(No.2025AFD399).
文摘Additive manufacturing(AM)promotes the production of metallic parts with significant design flexibility,yet its use in critical applications is hindered by challenges in ensuring consistent quality and performance.Process variability often leads to defects,insufficient geometric accuracy and inadequate material properties,which are difficult to effectively manage due to limitations of traditional quality control methods in modeling highdimensional nonlinear relationships and enabling adaptive control.Machine learning(ML)offers a transformative approach to model intricate process-structure-property relationships by leveraging the rich data environment of AM.The study presents a comprehensive examination of ML-driven quality assurance implementations in metallic AM.First,it uniquely examines the innovative exploration of ML in predicting and understanding the fundamental multi-physics fields that influence the quality of a fabricated component,including temperature fields,fluid dynamics and stress/strain evolution.Subsequently,the application of ML in optimizing key quality attributes,including defect detection and mitigation(porosity,cracks,etc.),geometric fidelity enhancement(dimensional accuracy,surface roughness,etc.)and material property tailoring(mechanical strength,fatigue life,corrosion resistance,etc.),are discussed in detail.Finally,the development of ML-driven real-time closed-loop control systems for intelligent quality assurance,the strategies for addressing the data scarcity and cross-scenario transferability in metal AM are discussed.This article provides a novel perspective on the profound potential of ML technology for metal AM quality control applications,highlights the challenges faced during research,and outlines future development directions.
基金supported by the Natural Science Foundation of Jiangsu Province(BK20240009)the National Natural Science Foundation of China(62373105,62373262)Jiangsu Provincial Scientific Research Center of Applied Mathematics(BK20233002).
文摘Dear Editor,This letter studies a real-world issue in leader-follower multi-agent systems(MASs)named open topology,which permits the variations of agent set and network connections.Specially,a novel transition process is developed to explain how the involved variation of network scale affects the dynamic behavior of the MASs.From a resource limited perspective,the distributed saturated impulsive control is then designed,under which some sufficient criteria are integrated into local quasi-consensus performance.We also provide a combined optimization algorithm for all agents to make the estimated domain of initial errors closer to the real one,thereby resulting in less conservativeness.Finally,a numerical example validates our results.
基金supported by Hong Kong RGC under grants 519913,15209614 and 15224215Jingrui Sun was partially supported by the National Natural Science Foundation of China(11401556)+1 种基金the Fundamental Research Funds for the Central Universities(WK 2040000012)Jiongmin Yong was partially supported by NSF DMS-1406776.
文摘An optimal control problem is studied for a linear mean-field stochastic differential equation with a quadratic cost functional.The coefficients and the weighting matrices in the cost functional are all assumed to be deterministic.Closedloop strategies are introduced,which require to be independent of initial states;and such a nature makes it very useful and convenient in applications.In this paper,the existence of an optimal closed-loop strategy for the system(also called the closedloop solvability of the problem)is characterized by the existence of a regular solution to the coupled two(generalized)Riccati equations,together with some constraints on the adapted solution to a linear backward stochastic differential equation and a linear terminal value problem of an ordinary differential equation.
文摘The paper presents a preview controller design for ATS （active trailer steering） systems to improve high-speed stability of AHVs （articulated heavy vehicles）. An AHV consists of a towing unit, namely tractor or truck, and one or more towed units which called trailers. Individual units are connected to one another at articulated joints by mechanical couplings. Due to the multi-unit configurations, AHVs exhibit unique unstable motion modes, including jack-knifing, trailer swing and rollover. These unstable motion modes are the leading cause of highway accidents. To prevent these unstable motion modes, the preview controller, namely the LPDP （lateral position deviation preview） controller, is proposed. For a truck/full-trailer combination, the LPDP controller is designed to control the steering of the front and rear axle wheels of the trailing unit. The calculation of the corrective steering angle of the trailer front axle wheels is based on the preview information of the lateral position deviation of the trajectory of the axle center from that of the truck front axle center. Similarly, the steering angle of the trailer rear axle wheels is calculated by using the lateral position deviation of the trajectory of the axle center from that of the truck front axle. To perform closed-loop dynamic simulations and evaluate the vehicle performance measure, a driver model is introduced and it ＇derives＇ the AHV model based on well-defined testing specifications. The proposed preview control scheme in the continuous time domain is developed by using the LQR （linear quadratic regular） technique. The closed-loop simulation results indicate that the performance of the AHV with the LPDP controller is improved by decreasing rearward amplification ratio from the baseline value of 1.28 to 0.98 and reducing transient off-tracking by 95.03%. The proposed LPDP control algorithm provides an alternative method for the design optimization of AHVs with ATS systems.
基金the National Natural Science Foundation of China(Grant Nos.51344003,51674039,51874044,51922007,and 51604035)the National Science and Technology Major Project of China(Grant No.2016ZX05014).
文摘Production optimal control technologies have become important tools for efficiently developing oil and gas reservoirs in recent years.This paper presents an overview of the research and application of these technologies in smart oilfield,including reservoir data matching and prediction,well production optimization,and automatic well monitoring and control technologies.With the support of the National Natural Science Foundation of China,we made years of effort and finally derived a novel data—driven reservoir data matching and prediction methods.Besides,the new automatic optimization technologies and flow monitoring and control devices were also presented.The proposed technologies helped improve the computational efficiency by hundreds of times compared to traditional technologies.The real-time optimization and control of the injection and production parameters was realized using the proposed technologies,which have been widely applied in actual reservoirs at home and abroad,achieving significant economic benefits.
基金Pulsion Medical Systems AG for the use of their pulse oximeter during the animal experiment conducted at the CharitéUniversity Hospital Berlin.
文摘Purpose–The purpose of this paper is to develop an automatic control system for mechanical ventilation therapy based on the open lung concept(OLC)using artificial intelligence.In addition,mean arterial blood pressure(MAP)is stabilized by means of a decoupling controller with automated noradrenaline(NA)dosage to ensure adequate systemic perfusion during ventilation therapy for patients with acute respiratory distress syndrome(ARDS).Design/methodology/approach–The aim is to develop an automatic control system for mechanical ventilation therapy based on the OLC using artificial intelligence.In addition,MAP is stabilized by means of a decoupling controller with automated NA dosage to ensure adequate systemic perfusion during ventilation therapy for patients with ARDS.Findings–Thisinnovativeclosed-loop mechanicalventilation system leadsto a significant improvement in oxygenation,regulates end-tidal carbon dioxide for appropriate gas exchange and stabilizes MAP to guarantee proper systemic perfusion during the ventilation therapy.Research limitations/implications–Currently,this automatic ventilation system based on the OLC can only be applied in animal trials;for clinical use,such a system generally requires a mechanical ventilator and sensors with medical approval for humans.Practical implications–For implementation of a closed-loop ventilation system,reliable signals from the sensors are a prerequisite for successful application.Originality/value–Theexperiment with porcine dynamics demonstrates thefeasibility and usefulness of this automatic closed-loop ventilation therapy,with hemodynamic control for severe ARDS.Moreover,this pilot study validated a new algorithm for implementation of the OLC,whereby all control objectives are fulfilled during the ventilation therapy with adequate hemodynamic control of patients with ARDS.
