Energy efficiency stands as an essential factor when implementing deep reinforcement learning(DRL)policies for robotic control systems.Standard algorithms,including Deep Deterministic Policy Gradient(DDPG),primarily o...Energy efficiency stands as an essential factor when implementing deep reinforcement learning(DRL)policies for robotic control systems.Standard algorithms,including Deep Deterministic Policy Gradient(DDPG),primarily optimize task rewards but at the cost of excessively high energy consumption,making them impractical for real-world robotic systems.To address this limitation,we propose Physics-Informed DDPG(PI-DDPG),which integrates physics-based energy penalties to develop energy-efficient yet high-performing control policies.The proposed method introduces adaptive physics-informed constraints through a dynamic weighting factor(λ),enabling policies that balance reward maximization with energy savings.Our motivation is to overcome the impracticality of rewardonly optimization by designing controllers that achieve competitive performance while substantially reducing energy consumption.PI-DDPG was evaluated in nine MuJoCo continuous control environments,where it demonstrated significant improvements in energy efficiency without compromising stability or performance.Experimental results confirm that PI-DDPG substantially reduces energy consumption compared to standard DDPG,while maintaining competitive task performance.For instance,energy costs decreased from 5542.98 to 3119.02 in HalfCheetah-v4 and from1909.13 to 1586.75 in Ant-v4,with stable performance in Hopper-v4(205.95 vs.130.82)and InvertedPendulum-v4(322.97 vs.311.29).Although DDPG sometimes yields higher rewards,such as in HalfCheetah-v4(5695.37 vs.4894.59),it requires significantly greater energy expenditure.These results highlight PI-DDPG as a promising energy-conscious alternative for robotic control.展开更多
The industrial sector is a predominant contributor to global energy consumption and carbon emissions.In manufacturing plants,district cooling systems account for a large portion of the energy consumption.However,facil...The industrial sector is a predominant contributor to global energy consumption and carbon emissions.In manufacturing plants,district cooling systems account for a large portion of the energy consumption.However,facility operators often lack understanding of the interactions between water-side systems,distribution piping networks,and demand-side systems(air-side systems and production room)due to the complexities of HVAC control management,manufacturing schedules,and heat flow in and around buildings.This results in poor system operation energy efficiency.Specifically,critical control parameters such as chilled water temperature setpoints and equipment sequencing are frequently managed through empirical settings,leading to inefficiencies as the static operation strategy is difficult to adapt to dynamic operating conditions or actual cooling demands.Therefore,this study proposes a decoupled step-wise simulation-based energy-efficient control method as a dynamic adjustment strategy for industrial space air-conditioning.The method establishes and integrates a Modelica-based water-side system model,distribution system model and room thermal response model to dynamically adjust the operation of pumps,and chillers by predicting the status of the demand-side system under different working conditions.Its main advantage lies in the decoupled and step-wise optimization of subsystems,which enhances interpretability,computability and scalability for practical deployment in industrial scenarios.A case study at a large-scale manufacturing plant showed that by applying the dynamic adjustment strategy,this method can contribute to reducing system energy consumption by around 23%during typical cooling seasons compared to the baseline rule-based control,demonstrating the significant potential of the proposed method for improving energy efficiency.展开更多
Proportional, integral and derivative (PID) control strategy has been widely applied in heating systems in decades. To improve the accuracy and the robustness of PID control, self-tuning radial-basis-function neural n...Proportional, integral and derivative (PID) control strategy has been widely applied in heating systems in decades. To improve the accuracy and the robustness of PID control, self-tuning radial-basis-function neural network PID (RBF-PID) is developed and used. Even though being popular, during the control process both of PID and RBF-PID control strategy are inadequate in achieving simultaneous high energy-efficiency and good control accuracy. To address this problem, in this paper we develop and report an enhanced self-tuning radial-basis-function neural network PID (e-RBF-PID) controller. To identify the superiority of e-RBF-PID, following works are conducted and reported in this paper. Firstly, four controllers, i.e., on-off, PID, RBF-PID and e-RBF-PID are designed. Secondly, in order to test the performance of the e-RBF-PID controller, an experimental water heating system is constructed for being controlled. Finally, the energy consumption for the four controllers under the three control scenarios is investigated through experiments. The experimental results indicate that in the three scenarios, the developed e-RBF-PID controller outperforms on-off controller as having higher accuracy. Compared to the PID controller, the e-RBF-PID controller has higher speed in control, and the experimental results show that settling time savings is between 12.6% - 49.0%. Most importantly, less control energy consumption is obtained if using the e-RBF-PID controller. It is found that up to 28.5% energy consumption can be saved. Therefore, it is concluded that the proposed e-RBF-PID is capable of enhancing energy efficiency during control process.展开更多
This study presents an energy-efficient Internet of Things(IoT)-based wireless sensor network(WSN)framework for autonomous data validation in remote environmental monitoring.We address two critical challenges in WSNs:...This study presents an energy-efficient Internet of Things(IoT)-based wireless sensor network(WSN)framework for autonomous data validation in remote environmental monitoring.We address two critical challenges in WSNs:ensuring data reliability and optimizing energy consumption.Our novel approach integrates an artificial neural network(ANN)-based multi-fault detection algorithm with an energy-efficient IoT-WSN architecture.The proposed ANN model is designed to simultaneously detect multiple fault types,including spike faults,stuckat faults,outliers,and out-of-range faults.We collected sensor data at 5-minute intervals over three months,using temperature and humidity sensors.The ANN was trained on 70%of the 26,280 data points per sensor,with 15%each for validation and testing.Our framework demonstrated a 97.1%improvement in fault detection accuracy(measured by F1 score)compared to existing methods,including rule-based,moving average,and statistical outlier detection approaches.The energy efficiency of the system was evaluated through 24-h power consumption tests,showing significant savings over traditional WSN architectures.Key contributions include a multi-fault detection ANN model balancing accuracy and computational efficiency,an energy-optimized IoTWSN architecture for remote deployments,and a comprehensive performance evaluation framework.While our approach offers improvements in both data validation and energy efficiency,we acknowledge limitations such as potential scalability issues and the need for further real-world testing.This research advances the field of remote environmental monitoring by providing a robust,energy-efficient solution for ensuring data reliability in challenging deployment scenarios.Future work will explore more advanced machine learning techniques and extended field testing to further validate and improve the system’s performance.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
This paper solves an energy-efficient optimization problem of a fixed-wing unmanned aerial vehicle(UAV) assisted full-duplex mobile relaying in maritime communication environments.Taking the speed and the acceleration...This paper solves an energy-efficient optimization problem of a fixed-wing unmanned aerial vehicle(UAV) assisted full-duplex mobile relaying in maritime communication environments.Taking the speed and the acceleration of the UAV and the information-causality constraints into consideration,the energy-efficiency of the system under investigation is maximized by jointly optimizing the UAV’s trajectory and the individual transmit power levels of the source and the UAV relay nodes.The optimization problem is non-convex and thus cannot be solved directly.Therefore,it is decoupled into two subproblems.One sub-problem is for the transmit power control at the source and the UAV relay nodes,and the other aims at optimizing the UAV s flight trajectory.By using the Lagrangian dual and Dinkelbach methods,the two sub-problems are solved,leading to an iterative algorithm for the joint design of transmit power control and trajectory optimization.Computer simulations demonstrated that by conducting the proposed algorithm,the flight trajectory of the UAV and the individual transmit power levels of the nodes can be flexibly adjusted according to the system conditions,and the proposed algorithm can achieve signiflcantly higher energy efficiency as compared with the other benchmark schemes.展开更多
The reduction of energy consumption is an increasingly important topic of the railway system.Energy-efficient train control(EETC)is one solution,which refers to mathematically computing when to accelerate,which cruisi...The reduction of energy consumption is an increasingly important topic of the railway system.Energy-efficient train control(EETC)is one solution,which refers to mathematically computing when to accelerate,which cruising speed to hold,how long one should coast over a suitable space,and when to brake.Most approaches in literature and industry greatly simplify a lot of nonlinear effects,such that they ignore mostly the losses due to energy conversion in traction components and auxiliaries.To fill this research gap,a series of increasingly detailed nonlinear losses is described and modelled.We categorize an increasing detail in this representation as four levels.We study the impact of those levels of detail on the energy optimal speed trajectory.To do this,a standard approach based on dynamic programming is used,given constraints on total travel time.This evaluation of multiple test cases highlights the influence of the dynamic losses and the power consumption of auxiliary components on railway trajectories,also compared to multiple benchmarks.The results show how the losses can make up 50%of the total energy consumption for an exemplary trip.Ignoring them would though result in consistent but limited errors in the optimal trajectory.Overall,more complex trajectories can result in less energy consumption when including the complexity of nonlinear losses than when a simpler model is considered.Those effects are stronger when the trajectory includes many acceleration and braking phases.展开更多
This paper solves the problem of model-free dual-arm space robot maneuvering after non-cooperative target capture under high control quality requirements.The explicit system model is unavailable,and the maneuvering mi...This paper solves the problem of model-free dual-arm space robot maneuvering after non-cooperative target capture under high control quality requirements.The explicit system model is unavailable,and the maneuvering mission is disturbed by the measurement noise and the target adversarial behavior.To address these problems,a model-free Combined Adaptive-length Datadriven Predictive Controller(CADPC)is proposed.It consists of a separated subsystem identification method and a combined predictive control strategy.The subsystem identification method is composed of an adaptive data length,thereby reducing sensitivity to undetermined measurement noises and disturbances.Based on the subsystem identification,the combined predictive controller is established,reducing calculating resource.The stability of the CADPC is rigorously proven using the Input-to-State Stable(ISS)theorem and the small-gain theorem.Simulations demonstrate that CADPC effectively handles the model-free space robot post operation in the presence of significant disturbances,state measurement noise,and control input errors.It achieves improved steady-state accuracy,reduced steady-state control consumption,and minimized control input chattering.展开更多
Energy-efficient retrofitting(EER)of existing buildings has significant potential for addressing energy and environmental issues.However,the traditional market trading model is characterized by an inefficient dissemin...Energy-efficient retrofitting(EER)of existing buildings has significant potential for addressing energy and environmental issues.However,the traditional market trading model is characterized by an inefficient dissemination of critical information,which leads to insufficient incentives for market participants to trade.To solve these problems,this study constructs a three-party evolutionary game model with energy saving service companies(ESCO),homeowners,and trading information platforms as the main players,analyzes the interaction and evolution of the three parties'strategies under the scenario of government rewards and penalties,and explores the effects of the three parties'initial willingness and changes of model parameters on the evolution of their strategies.There are some findings as follows:first,the positive transactions of homeowners and ESCOs have less influence on the platform side;second,compared with homeowners,the government penalties have more obvious constraints on the platform side and ESCOs;third,government subsidies and EER revenues are the important factors influencing the speed of the evolution of three-party strategies,fourth,platform service compensation,the factors governing cost and benefit sharing are pivotal in determining the alignment of strategic choices among the three parties involved.Based on the research conclusions.This study offers theoretical guidance for the advancement of platform-based market transactions for EER.展开更多
The convergence of Internet of things(IoT)and 5G holds immense potential for transforming industries by enabling real-time,massive-scale connectivity and automation.However,the growing number of devices connected to t...The convergence of Internet of things(IoT)and 5G holds immense potential for transforming industries by enabling real-time,massive-scale connectivity and automation.However,the growing number of devices connected to the IoT systems demands a communication network capable of handling vast amounts of data with minimal delay.These generated enormous complex,high-dimensional,high-volume,and high-speed data also brings challenges on its storage,transmission,processing,and energy cost,due to the limited computing capabilities,battery capacity,memory,and energy utilization of current IoT networks.In this paper,a seamless architecture by combining mobile and cloud computing is proposed.It can agilely bargain with 5G-IoT devices,sensor nodes,and mobile computing in a distributed manner,enabling minimized energy cost,high interoperability,and high scalability as well as overcoming the memory constraints.An artificial intelligence(AI)-powered green and energy-efficient architecture is then proposed for 5G-IoT systems and sustainable smart cities.The experimental results reveal that the proposed approach dramatically reduces the transmitted data volume and power consumption and yields superior results regarding interoperability,compression ratio,and energy saving.This is especially critical in enabling the deployment of 5G and even 6G wireless systems for smart cities.展开更多
In this paper, the energy-efficient power control problem in cognitive radio （CR） networks is studied not only to provide energy-efficient transmission, but also to guarantee the normal operation of primary users （...In this paper, the energy-efficient power control problem in cognitive radio （CR） networks is studied not only to provide energy-efficient transmission, but also to guarantee the normal operation of primary users （PUs）. Moreover, the static energy-efficient power control （SEPC） algorithm is proposed in static scenario to maximize the capacity of secondary users （SUs） and to reduce the power consumption according to the interference from PU to SU. Furthermore, based on the analysis of PU＇s dynamic feature with Markov chain and SEPC algorithm,the dynamic energy-efficient power control （DEPC） algorithm is proposed taking into account the probability of detection and false alarm caused by sensing errors. Extensive simulation results show that the performance of the proposed algorithms is significantly improved compared with the existing algorithm.展开更多
In marine wireless sensor networks(MWSNs),an appropriate routing protocol is the key to the collaborative collection and efficient transmission of massive data.However,designing an appropriate routing protocol under t...In marine wireless sensor networks(MWSNs),an appropriate routing protocol is the key to the collaborative collection and efficient transmission of massive data.However,designing an appropriate routing protocol under the condition of sparse marine node deployment,highly dynamic network topology,and limited node energy is complicated.Moreover,the absence of continuous endto-end connection introduces further difficulties in the design of routing protocols.In this case,we present a novel energy-efficient opportunistic routing(Novel Energy-Efficient Opportunistic Routing,NEOR)protocol for MWSNs that is based on compressed sensing and power control.First,a lightweight time-series prediction method-weighted moving average method is proposed to predict the packet advancement value such that the number of location information that is exchanged among a node and its neighbor nodes can be minimized.Second,an adaptive power control mechanism is presented to determine the optimal transmitting power and candidate nodeset on the basis of node mobility,packet advancement,communication link quality,and remaining node energy.Subsequently,a timer-based scheduling algorithm is utilized to coordinate packet forwarding to avoid packet conflict.Furthermore,we introduce the compressed sensing theory to compress perceptual data at source nodes and reconstruct the original data at sink nodes.Therefore,energy consumption in the MWSNs is greatly reduced due to the decrease in the amount of data perception and transmission.Numerical simulation experiments are carried out in a wide range of marine scenarios to verify the superiority of our approach over selected benchmark algorithms.展开更多
Mosquito-borne diseases pose a significant global health threat,necessitating the development of innovative vector control strategies.In this study,we investigated the potential of harnessing host immunity against mos...Mosquito-borne diseases pose a significant global health threat,necessitating the development of innovative vector control strategies.In this study,we investigated the potential of harnessing host immunity against mosquitoes through vaccination.Using Culex pipiens(C.pipiens)as a model,we demonstrated that polyclonal antibodies against C.pipiens abdominal protein extracts significantly impaired oviposition and increased mosquito mortality,primarily through the classical complement activation pathways.However,repeated exposure led to resistance,indicating potential adaptation.Proteomic analysis identified metabolic proteins as key targets,with Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses highlighting their roles in carboxylic acid metabolism,tyrosine degradation,and the proteasome pathways.Notably,cross-species reactivity was revealed by Western blotting,showing strong binding of Culex-specific antibodies to Anopheles and Aedes abdominal proteins.This study provides mechanistic insights into antibody-based mosquito suppression,highlighting its potential as an innovative vector control strategy while underscoring the need for further research on resistance management and ecological impacts.展开更多
This paper examines a model that combines vortex generators and leading-edge tubercles for controlling the laminar separation bubble(LSB)over an airfoil at low Reynolds numbers(Re).This new concept of passive flow con...This paper examines a model that combines vortex generators and leading-edge tubercles for controlling the laminar separation bubble(LSB)over an airfoil at low Reynolds numbers(Re).This new concept of passive flow control technique utilizing a tubercle and vortex generator(VG)close to the leading edge was analyzed numerically for a NACA0015 airfoil.In this study,the Shear Stress Transport(SST)turbulence model was employed in the numerical modelling.Numerical modelling was completed using the ANSYS-Fluent 18.2 solver.Analyses were conducted to investigate the flow pattern and understand the underlying LSB control phenomena that enabled the new passive flow control method to provide this significant performance benefit.The findings indicated that the new concept of passive flow control technique suppressed the formation of an LSB at the suction surface of the NACA0015 airfoil,resulting in a higher lift coefficient and improved aerodynamic performance.Improvements in LSB dynamics and aerodynamic performance through the passive flow control method lead to increased energy output and enhanced stability.展开更多
Steady speed control of agricultural machinery can improve operating quality and efficiency.To address the impact of farmland slope variations on the speed stability of unmanned operation agricultural machinery,a hybr...Steady speed control of agricultural machinery can improve operating quality and efficiency.To address the impact of farmland slope variations on the speed stability of unmanned operation agricultural machinery,a hybrid control method was proposed.This method included a hybrid controller composed of a slope-based controller and a proportional-integral-derivative(PID)controller.The speed of agricultural machinery was influenced by longitudinal forces,which were divided into two parts:one part was slope-related forces and conventional resistance,and the other was hard-to-estimate forces,such as sliding friction.For the first part,a slope-based controller was designed;for the second part,a PID controller was implemented.By combining these two controllers,the system can dynamically adjust the throttle opening and the brake master cylinder pressure,ensuring steady speed travel on sloping farmland.Simulation tests at a target speed of 7 km/h demonstrated that the proposed controller maintained a stable speed,achieving a root mean square error of 0.13 km/h and a mean absolute percentage error of 1.6%.Field tests on a practical experimental platform validated the method’s effectiveness,with results showing consistent control performance across varying slope conditions.The proposed controller demonstrated superior control performance.Experimental data verified that this method can achieve precise control of the agricultural machinery’s movement speed,meeting the stability requirements for agricultural operations.展开更多
This study investigates prescribed-time position tracking control for electromagnetic satellite formations subject to model uncertainties and external disturbances.Using the Clohessy-Wiltshire equations as the relativ...This study investigates prescribed-time position tracking control for electromagnetic satellite formations subject to model uncertainties and external disturbances.Using the Clohessy-Wiltshire equations as the relative motion dynamics model,a prescribed time output feedback control strategy is proposed.A prescribed-time extended state observer is designed to estimate the relative velocity and external disturbances.The disturbance estimates are then used as the feedforward component of the controller.Building on this framework,a novel prescribed-time active disturbance rejection control strategy for position tracking is developed via a backstepping control design.The convergence of the extended state observer and the stability of the closed-loop system are rigorously analyzed using Lyapunov stability theory.Numerical simulations are performed to validate the effectiveness of the proposed controller.展开更多
Energy efficiency is critical in Wireless Sensor Networks(WSNs)due to the limited power supply.While clustering algorithms are commonly used to extend network lifetime,most of them focus on single-layer optimization.T...Energy efficiency is critical in Wireless Sensor Networks(WSNs)due to the limited power supply.While clustering algorithms are commonly used to extend network lifetime,most of them focus on single-layer optimization.To this end,an Energy-efficient Cross-layer Clustering approach based on the Gini(ECCG)index theory was proposed in this paper.Specifically,a novel mechanism of Gini Index theory-based energy-efficient Cluster Head Election(GICHE)is presented based on the Gini Index and the expected energy distribution to achieve balanced energy consumption among different clusters.In addition,to improve inter-cluster energy efficiency,a Queue synchronous Media Access Control(QMAC)protocol is proposed to reduce intra-cluster communication overhead.Finally,extensive simulations have been conducted to evaluate the effectiveness of ECCG.Simulation results show that ECCG achieves 50.6%longer the time until the First Node Dies(FND)rounds,up to 30%lower energy consumption compared with Low-Energy Adaptive Clustering Hierarchy(LEACH),and higher throughput under different traffic loads,thereby validating its effectiveness in improving energy efficiency and prolonging the network lifetime.展开更多
As a closed-loop learning control method,repetitive control has been widely used in a variety of areas from appliances to aviation.A repetitive control system features perfect reference tracking and disturbance reject...As a closed-loop learning control method,repetitive control has been widely used in a variety of areas from appliances to aviation.A repetitive control system features perfect reference tracking and disturbance rejection in the steady state for periodic signals with a fixed period.This characteristic is important not only for conventional technologies and conventional industries but also for advanced technologies and emerging industries.This paper first explains the concept of repetitive control from its original idea.Next,it describes the structure of a repetitive controller as an internal model and shows the respective points of continuous-and discrete-time repetitive control.It presents a categorized list of practical applications of repetitive control.Moreover,two concrete applications,namely the control of a robotic manipulator and a rotating system,demonstrate the validity of the method with experimental results.Several current studies in this field are also reviewed,and some challenges and future studies for repetitive control are provided.展开更多
This article investigates the robust current tracking control problem of three-phase grid-connected inverters with LCL filter under external disturbance by a dynamic state feedback control method.First,this paper cons...This article investigates the robust current tracking control problem of three-phase grid-connected inverters with LCL filter under external disturbance by a dynamic state feedback control method.First,this paper constructs an internal model to learn the information of the states and input of the grid-connected inverter under steady state.Second,by utilizing the internal model principle,the paper turns the tracking control problem into the robust stabilization control problem based on some appropriate coordinate transformations.Then,The paper designs a dynamics state feedback control law to deal with this robust stabilization problem,and thus the solution of the robust current tracking control problem of three-phase grid-connected inverters can be obtained.This control method can ensure the asymptotic stability of the closedloop system.Finally,the paper illustrates the effectiveness of the proposed control approach through several groups of simulations,and compares it with the feedforward control method to verify the robustness of the proposed control method to uncertain parameters.展开更多
Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion...Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots.展开更多
文摘Energy efficiency stands as an essential factor when implementing deep reinforcement learning(DRL)policies for robotic control systems.Standard algorithms,including Deep Deterministic Policy Gradient(DDPG),primarily optimize task rewards but at the cost of excessively high energy consumption,making them impractical for real-world robotic systems.To address this limitation,we propose Physics-Informed DDPG(PI-DDPG),which integrates physics-based energy penalties to develop energy-efficient yet high-performing control policies.The proposed method introduces adaptive physics-informed constraints through a dynamic weighting factor(λ),enabling policies that balance reward maximization with energy savings.Our motivation is to overcome the impracticality of rewardonly optimization by designing controllers that achieve competitive performance while substantially reducing energy consumption.PI-DDPG was evaluated in nine MuJoCo continuous control environments,where it demonstrated significant improvements in energy efficiency without compromising stability or performance.Experimental results confirm that PI-DDPG substantially reduces energy consumption compared to standard DDPG,while maintaining competitive task performance.For instance,energy costs decreased from 5542.98 to 3119.02 in HalfCheetah-v4 and from1909.13 to 1586.75 in Ant-v4,with stable performance in Hopper-v4(205.95 vs.130.82)and InvertedPendulum-v4(322.97 vs.311.29).Although DDPG sometimes yields higher rewards,such as in HalfCheetah-v4(5695.37 vs.4894.59),it requires significantly greater energy expenditure.These results highlight PI-DDPG as a promising energy-conscious alternative for robotic control.
基金supported under the project of Research and Demonstration of Key Technologies for Low-Carbon Design and Optimization of Community(Park)Regional Integrated Energy Systems,by the Ningbo Science and Technology Bureau Major Science and Technology Programme,with project code 2022Z161.
文摘The industrial sector is a predominant contributor to global energy consumption and carbon emissions.In manufacturing plants,district cooling systems account for a large portion of the energy consumption.However,facility operators often lack understanding of the interactions between water-side systems,distribution piping networks,and demand-side systems(air-side systems and production room)due to the complexities of HVAC control management,manufacturing schedules,and heat flow in and around buildings.This results in poor system operation energy efficiency.Specifically,critical control parameters such as chilled water temperature setpoints and equipment sequencing are frequently managed through empirical settings,leading to inefficiencies as the static operation strategy is difficult to adapt to dynamic operating conditions or actual cooling demands.Therefore,this study proposes a decoupled step-wise simulation-based energy-efficient control method as a dynamic adjustment strategy for industrial space air-conditioning.The method establishes and integrates a Modelica-based water-side system model,distribution system model and room thermal response model to dynamically adjust the operation of pumps,and chillers by predicting the status of the demand-side system under different working conditions.Its main advantage lies in the decoupled and step-wise optimization of subsystems,which enhances interpretability,computability and scalability for practical deployment in industrial scenarios.A case study at a large-scale manufacturing plant showed that by applying the dynamic adjustment strategy,this method can contribute to reducing system energy consumption by around 23%during typical cooling seasons compared to the baseline rule-based control,demonstrating the significant potential of the proposed method for improving energy efficiency.
文摘Proportional, integral and derivative (PID) control strategy has been widely applied in heating systems in decades. To improve the accuracy and the robustness of PID control, self-tuning radial-basis-function neural network PID (RBF-PID) is developed and used. Even though being popular, during the control process both of PID and RBF-PID control strategy are inadequate in achieving simultaneous high energy-efficiency and good control accuracy. To address this problem, in this paper we develop and report an enhanced self-tuning radial-basis-function neural network PID (e-RBF-PID) controller. To identify the superiority of e-RBF-PID, following works are conducted and reported in this paper. Firstly, four controllers, i.e., on-off, PID, RBF-PID and e-RBF-PID are designed. Secondly, in order to test the performance of the e-RBF-PID controller, an experimental water heating system is constructed for being controlled. Finally, the energy consumption for the four controllers under the three control scenarios is investigated through experiments. The experimental results indicate that in the three scenarios, the developed e-RBF-PID controller outperforms on-off controller as having higher accuracy. Compared to the PID controller, the e-RBF-PID controller has higher speed in control, and the experimental results show that settling time savings is between 12.6% - 49.0%. Most importantly, less control energy consumption is obtained if using the e-RBF-PID controller. It is found that up to 28.5% energy consumption can be saved. Therefore, it is concluded that the proposed e-RBF-PID is capable of enhancing energy efficiency during control process.
基金supported by King Saud University through Researchers Supporting Project number(RSPD2024R1006),King Saud University,Riyadh,Saudi Arabia.
文摘This study presents an energy-efficient Internet of Things(IoT)-based wireless sensor network(WSN)framework for autonomous data validation in remote environmental monitoring.We address two critical challenges in WSNs:ensuring data reliability and optimizing energy consumption.Our novel approach integrates an artificial neural network(ANN)-based multi-fault detection algorithm with an energy-efficient IoT-WSN architecture.The proposed ANN model is designed to simultaneously detect multiple fault types,including spike faults,stuckat faults,outliers,and out-of-range faults.We collected sensor data at 5-minute intervals over three months,using temperature and humidity sensors.The ANN was trained on 70%of the 26,280 data points per sensor,with 15%each for validation and testing.Our framework demonstrated a 97.1%improvement in fault detection accuracy(measured by F1 score)compared to existing methods,including rule-based,moving average,and statistical outlier detection approaches.The energy efficiency of the system was evaluated through 24-h power consumption tests,showing significant savings over traditional WSN architectures.Key contributions include a multi-fault detection ANN model balancing accuracy and computational efficiency,an energy-optimized IoTWSN architecture for remote deployments,and a comprehensive performance evaluation framework.While our approach offers improvements in both data validation and energy efficiency,we acknowledge limitations such as potential scalability issues and the need for further real-world testing.This research advances the field of remote environmental monitoring by providing a robust,energy-efficient solution for ensuring data reliability in challenging deployment scenarios.Future work will explore more advanced machine learning techniques and extended field testing to further validate and improve the system’s performance.
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National Natural Science Foundation of China(No.61871241)Nantong Science and Technology Project(JC2019114,JC2021129).
文摘This paper solves an energy-efficient optimization problem of a fixed-wing unmanned aerial vehicle(UAV) assisted full-duplex mobile relaying in maritime communication environments.Taking the speed and the acceleration of the UAV and the information-causality constraints into consideration,the energy-efficiency of the system under investigation is maximized by jointly optimizing the UAV’s trajectory and the individual transmit power levels of the source and the UAV relay nodes.The optimization problem is non-convex and thus cannot be solved directly.Therefore,it is decoupled into two subproblems.One sub-problem is for the transmit power control at the source and the UAV relay nodes,and the other aims at optimizing the UAV s flight trajectory.By using the Lagrangian dual and Dinkelbach methods,the two sub-problems are solved,leading to an iterative algorithm for the joint design of transmit power control and trajectory optimization.Computer simulations demonstrated that by conducting the proposed algorithm,the flight trajectory of the UAV and the individual transmit power levels of the nodes can be flexibly adjusted according to the system conditions,and the proposed algorithm can achieve signiflcantly higher energy efficiency as compared with the other benchmark schemes.
基金supported by Swiss Federal Office of Transport,the ETH foundation and via the grant RAILPOWER.
文摘The reduction of energy consumption is an increasingly important topic of the railway system.Energy-efficient train control(EETC)is one solution,which refers to mathematically computing when to accelerate,which cruising speed to hold,how long one should coast over a suitable space,and when to brake.Most approaches in literature and industry greatly simplify a lot of nonlinear effects,such that they ignore mostly the losses due to energy conversion in traction components and auxiliaries.To fill this research gap,a series of increasingly detailed nonlinear losses is described and modelled.We categorize an increasing detail in this representation as four levels.We study the impact of those levels of detail on the energy optimal speed trajectory.To do this,a standard approach based on dynamic programming is used,given constraints on total travel time.This evaluation of multiple test cases highlights the influence of the dynamic losses and the power consumption of auxiliary components on railway trajectories,also compared to multiple benchmarks.The results show how the losses can make up 50%of the total energy consumption for an exemplary trip.Ignoring them would though result in consistent but limited errors in the optimal trajectory.Overall,more complex trajectories can result in less energy consumption when including the complexity of nonlinear losses than when a simpler model is considered.Those effects are stronger when the trajectory includes many acceleration and braking phases.
基金supported by the National Natural Science Foundation of China(No.12372045)the National Key Research and the Development Program of China(Nos.2023YFC2205900,2023YFC2205901)。
文摘This paper solves the problem of model-free dual-arm space robot maneuvering after non-cooperative target capture under high control quality requirements.The explicit system model is unavailable,and the maneuvering mission is disturbed by the measurement noise and the target adversarial behavior.To address these problems,a model-free Combined Adaptive-length Datadriven Predictive Controller(CADPC)is proposed.It consists of a separated subsystem identification method and a combined predictive control strategy.The subsystem identification method is composed of an adaptive data length,thereby reducing sensitivity to undetermined measurement noises and disturbances.Based on the subsystem identification,the combined predictive controller is established,reducing calculating resource.The stability of the CADPC is rigorously proven using the Input-to-State Stable(ISS)theorem and the small-gain theorem.Simulations demonstrate that CADPC effectively handles the model-free space robot post operation in the presence of significant disturbances,state measurement noise,and control input errors.It achieves improved steady-state accuracy,reduced steady-state control consumption,and minimized control input chattering.
基金supported by the National Natural Science Foundation of China(Grant No.71872122)the Late-stage Subsidy Project of Humanities and Social Sciences of the Education Department of China(Grant No.20JHQ095).
文摘Energy-efficient retrofitting(EER)of existing buildings has significant potential for addressing energy and environmental issues.However,the traditional market trading model is characterized by an inefficient dissemination of critical information,which leads to insufficient incentives for market participants to trade.To solve these problems,this study constructs a three-party evolutionary game model with energy saving service companies(ESCO),homeowners,and trading information platforms as the main players,analyzes the interaction and evolution of the three parties'strategies under the scenario of government rewards and penalties,and explores the effects of the three parties'initial willingness and changes of model parameters on the evolution of their strategies.There are some findings as follows:first,the positive transactions of homeowners and ESCOs have less influence on the platform side;second,compared with homeowners,the government penalties have more obvious constraints on the platform side and ESCOs;third,government subsidies and EER revenues are the important factors influencing the speed of the evolution of three-party strategies,fourth,platform service compensation,the factors governing cost and benefit sharing are pivotal in determining the alignment of strategic choices among the three parties involved.Based on the research conclusions.This study offers theoretical guidance for the advancement of platform-based market transactions for EER.
文摘The convergence of Internet of things(IoT)and 5G holds immense potential for transforming industries by enabling real-time,massive-scale connectivity and automation.However,the growing number of devices connected to the IoT systems demands a communication network capable of handling vast amounts of data with minimal delay.These generated enormous complex,high-dimensional,high-volume,and high-speed data also brings challenges on its storage,transmission,processing,and energy cost,due to the limited computing capabilities,battery capacity,memory,and energy utilization of current IoT networks.In this paper,a seamless architecture by combining mobile and cloud computing is proposed.It can agilely bargain with 5G-IoT devices,sensor nodes,and mobile computing in a distributed manner,enabling minimized energy cost,high interoperability,and high scalability as well as overcoming the memory constraints.An artificial intelligence(AI)-powered green and energy-efficient architecture is then proposed for 5G-IoT systems and sustainable smart cities.The experimental results reveal that the proposed approach dramatically reduces the transmitted data volume and power consumption and yields superior results regarding interoperability,compression ratio,and energy saving.This is especially critical in enabling the deployment of 5G and even 6G wireless systems for smart cities.
基金the National Natural Science Foundation of China，Beijing Municipal Natural Science Foundation，the Key Project of Ministry of Industry and Information Technology，the National Youth Science Foundation
文摘In this paper, the energy-efficient power control problem in cognitive radio （CR） networks is studied not only to provide energy-efficient transmission, but also to guarantee the normal operation of primary users （PUs）. Moreover, the static energy-efficient power control （SEPC） algorithm is proposed in static scenario to maximize the capacity of secondary users （SUs） and to reduce the power consumption according to the interference from PU to SU. Furthermore, based on the analysis of PU＇s dynamic feature with Markov chain and SEPC algorithm,the dynamic energy-efficient power control （DEPC） algorithm is proposed taking into account the probability of detection and false alarm caused by sensing errors. Extensive simulation results show that the performance of the proposed algorithms is significantly improved compared with the existing algorithm.
基金supported by the National Natural Science Foundation of China(Nos.52201403,52201401,52071200,52102397,61701299,51709167)the National Key Research and Development Program(No.2021YFC2801002)+4 种基金the China Postdoctoral Science Foundation(Nos.2021M 700790,2022M712027)the Fund of National Engineering Research Center for Water Transport Safety(No.A2022003)the Foundation for Jiangsu Key Laboratory of Traffic and Transportation Security(No.TTS2021-05)the Fund of Hubei Key Laboratory of Inland Shipping Technology(No.NHHY2021002)the Top-Notch Innovative Program for Postgraduates of Shanghai Maritime University(Nos.2019YBR006,2019YBR002).
文摘In marine wireless sensor networks(MWSNs),an appropriate routing protocol is the key to the collaborative collection and efficient transmission of massive data.However,designing an appropriate routing protocol under the condition of sparse marine node deployment,highly dynamic network topology,and limited node energy is complicated.Moreover,the absence of continuous endto-end connection introduces further difficulties in the design of routing protocols.In this case,we present a novel energy-efficient opportunistic routing(Novel Energy-Efficient Opportunistic Routing,NEOR)protocol for MWSNs that is based on compressed sensing and power control.First,a lightweight time-series prediction method-weighted moving average method is proposed to predict the packet advancement value such that the number of location information that is exchanged among a node and its neighbor nodes can be minimized.Second,an adaptive power control mechanism is presented to determine the optimal transmitting power and candidate nodeset on the basis of node mobility,packet advancement,communication link quality,and remaining node energy.Subsequently,a timer-based scheduling algorithm is utilized to coordinate packet forwarding to avoid packet conflict.Furthermore,we introduce the compressed sensing theory to compress perceptual data at source nodes and reconstruct the original data at sink nodes.Therefore,energy consumption in the MWSNs is greatly reduced due to the decrease in the amount of data perception and transmission.Numerical simulation experiments are carried out in a wide range of marine scenarios to verify the superiority of our approach over selected benchmark algorithms.
基金supported by the National Natural Science Foundation of China(Grant No.82472312).
文摘Mosquito-borne diseases pose a significant global health threat,necessitating the development of innovative vector control strategies.In this study,we investigated the potential of harnessing host immunity against mosquitoes through vaccination.Using Culex pipiens(C.pipiens)as a model,we demonstrated that polyclonal antibodies against C.pipiens abdominal protein extracts significantly impaired oviposition and increased mosquito mortality,primarily through the classical complement activation pathways.However,repeated exposure led to resistance,indicating potential adaptation.Proteomic analysis identified metabolic proteins as key targets,with Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses highlighting their roles in carboxylic acid metabolism,tyrosine degradation,and the proteasome pathways.Notably,cross-species reactivity was revealed by Western blotting,showing strong binding of Culex-specific antibodies to Anopheles and Aedes abdominal proteins.This study provides mechanistic insights into antibody-based mosquito suppression,highlighting its potential as an innovative vector control strategy while underscoring the need for further research on resistance management and ecological impacts.
基金the Scientific Research Projects Unit of Erciyes University under contract no:FDS-2022-11532 and FOA-2025-14773.
文摘This paper examines a model that combines vortex generators and leading-edge tubercles for controlling the laminar separation bubble(LSB)over an airfoil at low Reynolds numbers(Re).This new concept of passive flow control technique utilizing a tubercle and vortex generator(VG)close to the leading edge was analyzed numerically for a NACA0015 airfoil.In this study,the Shear Stress Transport(SST)turbulence model was employed in the numerical modelling.Numerical modelling was completed using the ANSYS-Fluent 18.2 solver.Analyses were conducted to investigate the flow pattern and understand the underlying LSB control phenomena that enabled the new passive flow control method to provide this significant performance benefit.The findings indicated that the new concept of passive flow control technique suppressed the formation of an LSB at the suction surface of the NACA0015 airfoil,resulting in a higher lift coefficient and improved aerodynamic performance.Improvements in LSB dynamics and aerodynamic performance through the passive flow control method lead to increased energy output and enhanced stability.
文摘Steady speed control of agricultural machinery can improve operating quality and efficiency.To address the impact of farmland slope variations on the speed stability of unmanned operation agricultural machinery,a hybrid control method was proposed.This method included a hybrid controller composed of a slope-based controller and a proportional-integral-derivative(PID)controller.The speed of agricultural machinery was influenced by longitudinal forces,which were divided into two parts:one part was slope-related forces and conventional resistance,and the other was hard-to-estimate forces,such as sliding friction.For the first part,a slope-based controller was designed;for the second part,a PID controller was implemented.By combining these two controllers,the system can dynamically adjust the throttle opening and the brake master cylinder pressure,ensuring steady speed travel on sloping farmland.Simulation tests at a target speed of 7 km/h demonstrated that the proposed controller maintained a stable speed,achieving a root mean square error of 0.13 km/h and a mean absolute percentage error of 1.6%.Field tests on a practical experimental platform validated the method’s effectiveness,with results showing consistent control performance across varying slope conditions.The proposed controller demonstrated superior control performance.Experimental data verified that this method can achieve precise control of the agricultural machinery’s movement speed,meeting the stability requirements for agricultural operations.
文摘This study investigates prescribed-time position tracking control for electromagnetic satellite formations subject to model uncertainties and external disturbances.Using the Clohessy-Wiltshire equations as the relative motion dynamics model,a prescribed time output feedback control strategy is proposed.A prescribed-time extended state observer is designed to estimate the relative velocity and external disturbances.The disturbance estimates are then used as the feedforward component of the controller.Building on this framework,a novel prescribed-time active disturbance rejection control strategy for position tracking is developed via a backstepping control design.The convergence of the extended state observer and the stability of the closed-loop system are rigorously analyzed using Lyapunov stability theory.Numerical simulations are performed to validate the effectiveness of the proposed controller.
基金supported by the National Natural Science Foundation of China under Grant No.62461041Natural Science Foundation of Jiangxi Province under Grant No.20224BAB212016 and No.20242BA B25068China Scholarship Council under Grant No.202106825021.
文摘Energy efficiency is critical in Wireless Sensor Networks(WSNs)due to the limited power supply.While clustering algorithms are commonly used to extend network lifetime,most of them focus on single-layer optimization.To this end,an Energy-efficient Cross-layer Clustering approach based on the Gini(ECCG)index theory was proposed in this paper.Specifically,a novel mechanism of Gini Index theory-based energy-efficient Cluster Head Election(GICHE)is presented based on the Gini Index and the expected energy distribution to achieve balanced energy consumption among different clusters.In addition,to improve inter-cluster energy efficiency,a Queue synchronous Media Access Control(QMAC)protocol is proposed to reduce intra-cluster communication overhead.Finally,extensive simulations have been conducted to evaluate the effectiveness of ECCG.Simulation results show that ECCG achieves 50.6%longer the time until the First Node Dies(FND)rounds,up to 30%lower energy consumption compared with Low-Energy Adaptive Clustering Hierarchy(LEACH),and higher throughput under different traffic loads,thereby validating its effectiveness in improving energy efficiency and prolonging the network lifetime.
基金supported in part by the Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research(B)(23K25252,24K03325)the National Natural Science Foundation of China(61873348)the Natural Science Foundation of Hubei Province,China(2020CFA031)。
文摘As a closed-loop learning control method,repetitive control has been widely used in a variety of areas from appliances to aviation.A repetitive control system features perfect reference tracking and disturbance rejection in the steady state for periodic signals with a fixed period.This characteristic is important not only for conventional technologies and conventional industries but also for advanced technologies and emerging industries.This paper first explains the concept of repetitive control from its original idea.Next,it describes the structure of a repetitive controller as an internal model and shows the respective points of continuous-and discrete-time repetitive control.It presents a categorized list of practical applications of repetitive control.Moreover,two concrete applications,namely the control of a robotic manipulator and a rotating system,demonstrate the validity of the method with experimental results.Several current studies in this field are also reviewed,and some challenges and future studies for repetitive control are provided.
基金Supported by the Fundamental Research Funds for the Central Universities(2024ZYGXZR047)the National Natural Science Foundation of China(62373156)the Guangdong Basic and Applied Basic Research Foundation(2024A1515011736)。
文摘This article investigates the robust current tracking control problem of three-phase grid-connected inverters with LCL filter under external disturbance by a dynamic state feedback control method.First,this paper constructs an internal model to learn the information of the states and input of the grid-connected inverter under steady state.Second,by utilizing the internal model principle,the paper turns the tracking control problem into the robust stabilization control problem based on some appropriate coordinate transformations.Then,The paper designs a dynamics state feedback control law to deal with this robust stabilization problem,and thus the solution of the robust current tracking control problem of three-phase grid-connected inverters can be obtained.This control method can ensure the asymptotic stability of the closedloop system.Finally,the paper illustrates the effectiveness of the proposed control approach through several groups of simulations,and compares it with the feedforward control method to verify the robustness of the proposed control method to uncertain parameters.
文摘Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots.
