To address the issue of coordinated control of multiple hydrogen and battery storage units to suppress the grid-injected power deviation of wind farms,an online optimization strategy for Battery-hydrogen hybrid energy...To address the issue of coordinated control of multiple hydrogen and battery storage units to suppress the grid-injected power deviation of wind farms,an online optimization strategy for Battery-hydrogen hybrid energy storage systems based on measurement feedback is proposed.First,considering the high charge/discharge losses of hydrogen storage and the low energy density of battery storage,an operational optimization objective is established to enable adaptive energy adjustment in the Battery-hydrogen hybrid energy storage system.Next,an online optimization model minimizing the operational cost of the hybrid system is constructed to suppress grid-injected power deviations with satisfying the operational constraints of hydrogen storage and batteries.Finally,utilizing the online measurement of the energy states of hydrogen storage and batteries,an online optimization strategy based on measurement feedback is designed.Case study results show:before and after smoothing the fluctuations in wind power,the time when the power exceeded the upper and lower limits of the grid-injected power accounted for 24.1%and 1.45%of the total time,respectively,the proposed strategy can effectively keep the grid-injected power deviations of wind farms within the allowable range.Hydrogen storage and batteries respectively undertake long-term and short-term charge/discharge tasks,effectively reducing charge/discharge losses of the Battery-hydrogen hybrid energy storage systems and improving its operational efficiency.展开更多
This paper investigates mobility-aware online optimization for digital twin(DT)-assisted task execution in edge computing environments.In such systems,DTs,hosted on edge servers(ESs),require proactive migration to mai...This paper investigates mobility-aware online optimization for digital twin(DT)-assisted task execution in edge computing environments.In such systems,DTs,hosted on edge servers(ESs),require proactive migration to maintain proximity to their mobile physical twin(PT)counterparts.To minimize task response latency under a stringent energy consumption constraint,we jointly optimize three key components:the status data uploading frequency fromthe PT,theDT migration decisions,and the allocation of computational and communication resources.To address the asynchronous nature of these decisions,we propose a novel two-timescale mobility-aware online optimization(TMO)framework.The TMO scheme leverages an extended two-timescale Lyapunov optimization framework to decompose the long-term problem into sequential subproblems.At the larger timescale,a multi-armed bandit(MAB)algorithm is employed to dynamically learn the optimal status data uploading frequency.Within each shorter timescale,we first employ a gated recurrent unit(GRU)-based predictor to forecast the PT’s trajectory.Based on this prediction,an alternate minimization(AM)algorithm is then utilized to solve for the DT migration and resource allocation variables.Theoretical analysis confirms that the proposed TMO scheme is asymptotically optimal.Furthermore,simulation results demonstrate its significant performance gains over existing benchmark methods.展开更多
In this paper,we consider the distributed online optimization problem on a time-varying network,where each agent on the network has its own time-varying objective function and the goal is to minimize the overall loss ...In this paper,we consider the distributed online optimization problem on a time-varying network,where each agent on the network has its own time-varying objective function and the goal is to minimize the overall loss accumulated.Moreover,we focus on distributed algorithms which do not use gradient information and projection operators to improve the applicability and computational efficiency.By introducing the deterministic differences and the randomized differences to substitute the gradient information of the objective functions and removing the projection operator in the traditional algorithms,we design two kinds of gradient-free distributed online optimization algorithms without projection step,which can economize considerable computational resources as well as has less limitations on the applicability.We prove that both of two algorithms achieves consensus of the estimates and regrets of\(O\left(\log(T)\right)\)for local strongly convex objective,respectively.Finally,a simulation example is provided to verify the theoretical results.展开更多
This paper considers the distributed online optimization(DOO) problem over time-varying unbalanced networks, where gradient information is explicitly unknown. To address this issue, a privacy-preserving distributed on...This paper considers the distributed online optimization(DOO) problem over time-varying unbalanced networks, where gradient information is explicitly unknown. To address this issue, a privacy-preserving distributed online one-point residual feedback(OPRF) optimization algorithm is proposed. This algorithm updates decision variables by leveraging one-point residual feedback to estimate the true gradient information. It can achieve the same performance as the two-point feedback scheme while only requiring a single function value query per iteration. Additionally, it effectively eliminates the effect of time-varying unbalanced graphs by dynamically constructing row stochastic matrices. Furthermore, compared to other distributed optimization algorithms that only consider explicitly unknown cost functions, this paper also addresses the issue of privacy information leakage of nodes. Theoretical analysis demonstrate that the method attains sublinear regret while protecting the privacy information of agents. Finally, numerical experiments on distributed collaborative localization problem and federated learning confirm the effectiveness of the algorithm.展开更多
Traditionally,offline optimization of power systems is acceptable due to the largely predictable loads and reliable generation.The increasing penetration of fluctuating renewable generation and internet-of-things devi...Traditionally,offline optimization of power systems is acceptable due to the largely predictable loads and reliable generation.The increasing penetration of fluctuating renewable generation and internet-of-things devices allowing for fine-grained controllability of loads have led to the diminishing applicability of offline optimization in the power systems domain,and have redirected attention to online optimization methods.However,online optimization is a broad topic that can be applied in and motivated by different settings,operated on different time scales,and built on different theoretical foundations.This paper reviews the various types of online optimization techniques used in the power systems domain and aims to make clear the distinction between the most common techniques used.In particular,we introduce and compare four distinct techniques used covering the breadth of online optimization techniques used in the power systems domain,i.e.,optimization-guided dynamic control,feedback optimization for single-period problems,Lyapunov-based optimization,and online convex optimization techniques for multi-period problems.Lastly,we recommend some potential future directions for online optimization in the power systems domain.展开更多
This paper presents a combined strategy to solve the trajectory online optimization problem for unmanned combat aerial vehicle (UCAV). Firstly, as trajectory directly optimizing is quite time costing, an online trajec...This paper presents a combined strategy to solve the trajectory online optimization problem for unmanned combat aerial vehicle (UCAV). Firstly, as trajectory directly optimizing is quite time costing, an online trajectory functional representation method is proposed. Considering the practical requirement of online trajectory, the 4-order polynomial function is used to represent the trajectory, and which can be determined by two independent parameters with the trajectory terminal conditions; thus, the trajectory online optimization problem is converted into the optimization of the two parameters, which largely lowers the complexity of the optimization problem. Furthermore, the scopes of the two parameters have been assessed into small ranges using the golden section ratio method. Secondly, a multi-population rotation strategy differential evolution approach (MPRDE) is designed to optimize the two parameters; in which, 'current-to-best/1/bin', 'current-to-rand/1/bin' and 'rand/2/bin' strategies with fixed parameter settings are designed, these strategies are rotationally used by three subpopulations. Thirdly, the rolling optimization method is applied to model the online trajectory optimization process. Finally, simulation results demonstrate the efficiency and real-time calculation capability of the designed combined strategy for UCAV trajectory online optimizing under dynamic and complicated environments.展开更多
This paper considers the problem of distributed online regularized optimization over a network that consists of multiple interacting nodes.Each node is endowed with a sequence of loss functions that are time-varying a...This paper considers the problem of distributed online regularized optimization over a network that consists of multiple interacting nodes.Each node is endowed with a sequence of loss functions that are time-varying and a regularization function that is fixed over time.A distributed forward-backward splitting algorithm is proposed for solving this problem and both fixed and adaptive learning rates are adopted.For both cases,we show that the regret upper bounds scale as O(VT),where T is the time horizon.In particular,those rates match the centralized counterpart.Finally,we show the effectiveness of the proposed algorithms over an online distributed regularized linear regression problem.展开更多
In this paper,the problem of online distributed optimization subject to a convex set is studied via a network of agents.Each agent only has access to a noisy gradient of its own objective function,and can communicate ...In this paper,the problem of online distributed optimization subject to a convex set is studied via a network of agents.Each agent only has access to a noisy gradient of its own objective function,and can communicate with its neighbors via a network.To handle this problem,an online distributed stochastic mirror descent algorithm is proposed.Existing works on online distributed algorithms involving stochastic gradients only provide the expectation bounds of the regrets.Different from them,we study the high probability bound of the regrets,i.e.,the sublinear bound of the regret is characterized by the natural logarithm of the failure probability's inverse.Under mild assumptions on the graph connectivity,we prove that the dynamic regret grows sublinearly with a high probability if the deviation in the minimizer sequence is sublinear with the square root of the time horizon.Finally,a simulation is provided to demonstrate the effectiveness of our theoretical results.展开更多
This paper focuses on the online distributed optimization problem based on multi-agent systems. In this problem, each agent can only access its own cost function and a convex set, and can only exchange local state inf...This paper focuses on the online distributed optimization problem based on multi-agent systems. In this problem, each agent can only access its own cost function and a convex set, and can only exchange local state information with its current neighbors through a time-varying digraph. In addition, the agents do not have access to the information about the current cost functions until decisions are made. Different from most existing works on online distributed optimization, here we consider the case where the cost functions are strongly pseudoconvex and real gradients of the cost functions are not available. To handle this problem, a random gradient-free online distributed algorithm involving the multi-point gradient estimator is proposed. Of particular interest is that under the proposed algorithm, each agent only uses the estimation information of gradients instead of the real gradient information to make decisions. The dynamic regret is employed to measure the proposed algorithm. We prove that if the cumulative deviation of the minimizer sequence grows within a certain rate, then the expectation of dynamic regret increases sublinearly. Finally, a simulation example is given to corroborate the validity of our results.展开更多
The nitrogen-vacancy(NV)center in diamond has been developed as a promising platform for quantum sensing,especially for magnetic field measurements in the nano-tesla range with a nano-meter resolution.Optical spin rea...The nitrogen-vacancy(NV)center in diamond has been developed as a promising platform for quantum sensing,especially for magnetic field measurements in the nano-tesla range with a nano-meter resolution.Optical spin readout performance has a direct effect on the signal-to-noise ratio(SNR)of experiments.In this work,we introduce an online optimization method to customize the laser waveform for readout.Both simulations and experiments reveal that our new scheme optimizes the optically detected magnetic resonance in NV center.The SNR of optical spin readout has been witnessed a 44.1%increase in experiments.In addition,we applied the scheme to the Rabi oscillation experiment,which shows an improvement of 46.0%in contrast and a reduction of 12.1%in mean deviation compared to traditional constant laser power SNR optimization.This scheme is promising to improve sensitivities for a wide range of NV-based applications in the future.展开更多
This article presents a new two-axis solar tracker based on an online optimization algorithm so as to track the position of the sun without using its movement model.In this research,four well-known optimization algori...This article presents a new two-axis solar tracker based on an online optimization algorithm so as to track the position of the sun without using its movement model.In this research,four well-known optimization algorithms are employed to find the two unknown parameters named azimuth and zenith angles,which determine the position of the sun.The magnitude of the sunray is considered as the cost function of all algorithms.Then,several experiments are carried out to find the best optimization algorithm with optimal population size,number of iterations,and also the best initialization method.Uniform initialization leads to faster convergence compared to random initialization.The results clearly show that the particle swarm optimization algorithm with a population size of 15 and 7 iterations using uniform initialization method has better performance than the other algorithms,with a convergence time of less than 40 s.The average fitness value or voltage received by the tracker is 2.4 Volts in this method,which is higher than other methods.TLBO also performs well with a population size of 15 and 7 iterations.Afterward,the artificial neural network with one hidden layer and 20 neurons is employed to predict these two parameters in each day and moment in a year in Shiraz city according to the experimental data extracted from PSO.Number of the day from January and the time are inputs and zenith and azimuth angles are considered the output of neural network modeling.The performance of the proposed ANN model is evaluated using regression plots,demonstrating a strong correlation between predicted and target outputs.Finally,the outcomes reveal the feasibility of using online optimization algorithms and neural network modeling in an effort to bypass the complex mathematical model of mechatronic systems and predict the movement of the sun automatically.展开更多
Combustion noise takes large proportion in diesel engine noise and the studies of its influence factors play an important role in noise reduction. Engine noise and cylinder pressure measurement experiments were carrie...Combustion noise takes large proportion in diesel engine noise and the studies of its influence factors play an important role in noise reduction. Engine noise and cylinder pressure measurement experiments were carried out. And the improved attenuation curves were obtained, by which the engine noise was predicted. The effect of fuel injection parameters in combustion noise was investigated during the combustion process. At last, the method combining single variable optimization and multivariate combination was introduced to online optimize the combustion noise. The results show that injection parameters can affect the cylinder pressure rise rate and heat release rate, and consequently affect the cylinder pressure load and pressure oscillation to influence the combustion noise. Among these parameters, main injection advance angle has the greatest influence on the combustion noise, while the pilot injection interval time takes the second place, and the pilot injection quantity is of minimal impact. After the optimal design of the combustion noise, the average sound pressure level of the engine is distinctly reduced by 1.0 d B(A) generally. Meanwhile, the power, emission and economy performances are ensured.展开更多
In this work, an online calibration mechanism is proposed for the combustion phase in a diesel engine. In particular, a simplified event-based engine model, of which the output predicts the optimum combustion phase, i...In this work, an online calibration mechanism is proposed for the combustion phase in a diesel engine. In particular, a simplified event-based engine model, of which the output predicts the optimum combustion phase, is used to aid the calibration, and the model is updated online along with the engine operation to keep the integrity high so as to improve the quality of optimum combustion phase prediction. It is found this mechanism can be applied to develop an online automated calibration process when the engine system shifts to a new operating point. of the proposed mechanism. Engine test results are included to demonstrate the effectiveness展开更多
In this paper,we consider online convex optimization(OCO)with time-varying loss and constraint functions.Specifically,the decision-maker chooses sequential decisions based only on past information;meantime,the loss an...In this paper,we consider online convex optimization(OCO)with time-varying loss and constraint functions.Specifically,the decision-maker chooses sequential decisions based only on past information;meantime,the loss and constraint functions are revealed over time.We first develop a class of model-based augmented Lagrangian methods(MALM)for time-varying functional constrained OCO(without feedback delay).Under standard assumptions,we establish sublinear regret and sublinear constraint violation of MALM.Furthermore,we extend MALM to deal with time-varying functional constrained OCO with delayed feedback,in which the feedback information of loss and constraint functions is revealed to decision-maker with delays.Without additional assumptions,we also establish sublinear regret and sublinear constraint violation for the delayed version of MALM.Finally,numerical results for several examples of constrained OCO including online network resource allocation,online logistic regression and online quadratically constrained quadratical program are presented to demonstrate the efficiency of the proposed algorithms.展开更多
The dynamic traveling salesman problem(DTSP)is significant in logistics distribution in real-world applications in smart cities,but it is uncertain and difficult to solve.This paper proposes a scheme library-based ant...The dynamic traveling salesman problem(DTSP)is significant in logistics distribution in real-world applications in smart cities,but it is uncertain and difficult to solve.This paper proposes a scheme library-based ant colony optimization(ACO)with a two-optimization(2-opt)strategy to solve the DTSP efficiently.The work is novel and contributes to three aspects:problemmodel,optimization framework,and algorithmdesign.Firstly,in the problem model,traditional DTSP models often consider the change of travel distance between two nodes over time,while this paper focuses on a special DTSP model in that the node locations change dynamically over time.Secondly,in the optimization framework,the ACO algorithm is carried out in an offline optimization and online application framework to efficiently reuse the historical information to help fast respond to the dynamic environment.The framework of offline optimization and online application is proposed due to the fact that the environmental change inDTSPis caused by the change of node location,and therefore the newenvironment is somehowsimilar to certain previous environments.This way,in the offline optimization,the solutions for possible environmental changes are optimized in advance,and are stored in a mode scheme library.In the online application,when an environmental change is detected,the candidate solutions stored in the mode scheme library are reused via ACO to improve search efficiency and reduce computational complexity.Thirdly,in the algorithm design,the ACO cooperates with the 2-opt strategy to enhance search efficiency.To evaluate the performance of ACO with 2-opt,we design two challenging DTSP cases with up to 200 and 1379 nodes and compare them with other ACO and genetic algorithms.The experimental results show that ACO with 2-opt can solve the DTSPs effectively.展开更多
The Chinese express delivery industry processes nearly 110 billion items in 2022,averaging an annual growth rate of 200%.Among the various types of sorting systems used for handling express items,cross-belt sorting sy...The Chinese express delivery industry processes nearly 110 billion items in 2022,averaging an annual growth rate of 200%.Among the various types of sorting systems used for handling express items,cross-belt sorting systems stand out as the most crucial.However,despite their high degree of automation,the workload for operators has intensified owing to the surging volume of express items.In the era of Industry 5.0,it is imperative to adopt new technologies that not only enhance worker welfare but also improve the efficiency of cross-belt systems.Striking a balance between efficiency in handling express items and operator well-being is challenging.Digital twin technology offers a promising solution in this respect.A realization method of a human-machine integrated digital twin is proposed in this study,enabling the interaction of biological human bodies,virtual human bodies,virtual equipment,and logistics equipment in a closed loop,thus setting an operating framework.Key technologies in the proposed framework include a collection of heterogeneous data from multiple sources,construction of the relationship between operator fatigue and operation efficiency based on physiological measurements,virtual model construction,and an online optimization module based on real-time simulation.The feasibility of the proposed method was verified in an express distribution center.展开更多
This paper studies an online distributed optimization problem over multi-agent systems.In this problem,the goal of agents is to cooperatively minimize the sum of locally dynamic cost functions.Different from most exis...This paper studies an online distributed optimization problem over multi-agent systems.In this problem,the goal of agents is to cooperatively minimize the sum of locally dynamic cost functions.Different from most existing works on distributed optimization,here we consider the case where the cost function is strongly pseudoconvex and real gradients of objective functions are not available.To handle this problem,an online zeroth-order stochastic optimization algorithm involving the single-point gradient estimator is proposed.Under the algorithm,each agent only has access to the information associated with its own cost function and the estimate of the gradient,and exchange local state information with its immediate neighbors via a time-varying digraph.The performance of the algorithm is measured by the expectation of dynamic regret.Under mild assumptions on graphs,we prove that if the cumulative deviation of minimizer sequence grows within a certain rate,then the expectation of dynamic regret grows sublinearly.Finally,a simulation example is given to illustrate the validity of our results.展开更多
This paper describes the design and experimental tests of a path planning and reference tracking algorithm for autonomous ground vehicles. The ground vehicles under consideration are equipped with forward looking sens...This paper describes the design and experimental tests of a path planning and reference tracking algorithm for autonomous ground vehicles. The ground vehicles under consideration are equipped with forward looking sensors that provide a preview capability over a certain horizon. A two-level control framework is proposed for real-time implementation of the model predictive control （MPC） algorithm, where the high-level performs on-line optimization to generate the best possible local reference respect to various constraints and the low-level commands the vehicle to follow realistic trajectories generated by the high-level controller. The proposed control scheme is implemented on an indoor testbed through networks with satisfactory performance.展开更多
Decentralized Online Learning(DOL)extends online learning to the domain of distributed networks.However,limitations of local data in decentralized settings lead to a decrease in the accuracy of decisions or models com...Decentralized Online Learning(DOL)extends online learning to the domain of distributed networks.However,limitations of local data in decentralized settings lead to a decrease in the accuracy of decisions or models compared to centralized methods.Considering the increasing requirement to achieve a high-precision model or decision with distributed data resources in a network,applying ensemble methods is attempted to achieve a superior model or decision with only transferring gradients or models.A new boosting method,namely Boosting for Distributed Online Convex Optimization(BD-OCO),is designed to realize the application of boosting in distributed scenarios.BD-OCO achieves the regret upper bound O(M+N/MNT)where M measures the size of the distributed network and N is the number of Weak Learners(WLs)in each node.The core idea of BD-OCO is to apply the local model to train a strong global one.BD-OCO is evaluated on the basis of eight different real-world datasets.Numerical results show that BD-OCO achieves excellent performance in accuracy and convergence,and is robust to the size of the distributed network.展开更多
In generalized Nash equilibrium(GNE)seeking problems over physical networks such as power grids,the enforcement of network constraints and time-varying environment may bring high computational costs.Developing online ...In generalized Nash equilibrium(GNE)seeking problems over physical networks such as power grids,the enforcement of network constraints and time-varying environment may bring high computational costs.Developing online algorithms is recognized as a promising method to cope with this challenge,where the task of computing system states is replaced by directly using measured values from the physical network.In this paper,we propose an online distributed algorithm via measurement feedback to track the GNE in a time-varying networked resource sharing market.Regarding that some system states are not measurable and measurement noise always exists,a dynamic state estimator is incorporated based on a Kalman filter,rendering a closed-loop dynamics of measurement-feedback driven online algorithm.We prove that,with a fixed step size,this online algorithm converges to a neighborhood of the GNE in expectation.Numerical simulations validate the theoretical results.展开更多
基金Supported by State Grid Zhejiang Electric Power Co.,Ltd.Science and Technology Project Funding(No.B311DS230005).
文摘To address the issue of coordinated control of multiple hydrogen and battery storage units to suppress the grid-injected power deviation of wind farms,an online optimization strategy for Battery-hydrogen hybrid energy storage systems based on measurement feedback is proposed.First,considering the high charge/discharge losses of hydrogen storage and the low energy density of battery storage,an operational optimization objective is established to enable adaptive energy adjustment in the Battery-hydrogen hybrid energy storage system.Next,an online optimization model minimizing the operational cost of the hybrid system is constructed to suppress grid-injected power deviations with satisfying the operational constraints of hydrogen storage and batteries.Finally,utilizing the online measurement of the energy states of hydrogen storage and batteries,an online optimization strategy based on measurement feedback is designed.Case study results show:before and after smoothing the fluctuations in wind power,the time when the power exceeded the upper and lower limits of the grid-injected power accounted for 24.1%and 1.45%of the total time,respectively,the proposed strategy can effectively keep the grid-injected power deviations of wind farms within the allowable range.Hydrogen storage and batteries respectively undertake long-term and short-term charge/discharge tasks,effectively reducing charge/discharge losses of the Battery-hydrogen hybrid energy storage systems and improving its operational efficiency.
基金funded by the State Key Laboratory of Massive Personalized Customization System and Technology,grant No.H&C-MPC-2023-04-01.
文摘This paper investigates mobility-aware online optimization for digital twin(DT)-assisted task execution in edge computing environments.In such systems,DTs,hosted on edge servers(ESs),require proactive migration to maintain proximity to their mobile physical twin(PT)counterparts.To minimize task response latency under a stringent energy consumption constraint,we jointly optimize three key components:the status data uploading frequency fromthe PT,theDT migration decisions,and the allocation of computational and communication resources.To address the asynchronous nature of these decisions,we propose a novel two-timescale mobility-aware online optimization(TMO)framework.The TMO scheme leverages an extended two-timescale Lyapunov optimization framework to decompose the long-term problem into sequential subproblems.At the larger timescale,a multi-armed bandit(MAB)algorithm is employed to dynamically learn the optimal status data uploading frequency.Within each shorter timescale,we first employ a gated recurrent unit(GRU)-based predictor to forecast the PT’s trajectory.Based on this prediction,an alternate minimization(AM)algorithm is then utilized to solve for the DT migration and resource allocation variables.Theoretical analysis confirms that the proposed TMO scheme is asymptotically optimal.Furthermore,simulation results demonstrate its significant performance gains over existing benchmark methods.
文摘In this paper,we consider the distributed online optimization problem on a time-varying network,where each agent on the network has its own time-varying objective function and the goal is to minimize the overall loss accumulated.Moreover,we focus on distributed algorithms which do not use gradient information and projection operators to improve the applicability and computational efficiency.By introducing the deterministic differences and the randomized differences to substitute the gradient information of the objective functions and removing the projection operator in the traditional algorithms,we design two kinds of gradient-free distributed online optimization algorithms without projection step,which can economize considerable computational resources as well as has less limitations on the applicability.We prove that both of two algorithms achieves consensus of the estimates and regrets of\(O\left(\log(T)\right)\)for local strongly convex objective,respectively.Finally,a simulation example is provided to verify the theoretical results.
基金supported by the National Natural Science Foundation of China (62033010, U23B2061)Qing Lan Project of Jiangsu Province(R2023Q07)。
文摘This paper considers the distributed online optimization(DOO) problem over time-varying unbalanced networks, where gradient information is explicitly unknown. To address this issue, a privacy-preserving distributed online one-point residual feedback(OPRF) optimization algorithm is proposed. This algorithm updates decision variables by leveraging one-point residual feedback to estimate the true gradient information. It can achieve the same performance as the two-point feedback scheme while only requiring a single function value query per iteration. Additionally, it effectively eliminates the effect of time-varying unbalanced graphs by dynamically constructing row stochastic matrices. Furthermore, compared to other distributed optimization algorithms that only consider explicitly unknown cost functions, this paper also addresses the issue of privacy information leakage of nodes. Theoretical analysis demonstrate that the method attains sublinear regret while protecting the privacy information of agents. Finally, numerical experiments on distributed collaborative localization problem and federated learning confirm the effectiveness of the algorithm.
基金supported by the National Natural Science Foundation of China(62103265)the“ChenGuang Program”Supported by the Shanghai Education Development Foundation+1 种基金Shanghai Municipal Education Commission of China(20CG11)the Young Elite Scientists Sponsorship Program by Cast of China Association for Science and Technology。
文摘Traditionally,offline optimization of power systems is acceptable due to the largely predictable loads and reliable generation.The increasing penetration of fluctuating renewable generation and internet-of-things devices allowing for fine-grained controllability of loads have led to the diminishing applicability of offline optimization in the power systems domain,and have redirected attention to online optimization methods.However,online optimization is a broad topic that can be applied in and motivated by different settings,operated on different time scales,and built on different theoretical foundations.This paper reviews the various types of online optimization techniques used in the power systems domain and aims to make clear the distinction between the most common techniques used.In particular,we introduce and compare four distinct techniques used covering the breadth of online optimization techniques used in the power systems domain,i.e.,optimization-guided dynamic control,feedback optimization for single-period problems,Lyapunov-based optimization,and online convex optimization techniques for multi-period problems.Lastly,we recommend some potential future directions for online optimization in the power systems domain.
基金supported by the National Natural Science Foundation of China(61601505)the Aeronautical Science Foundation of China(20155196022)the Shaanxi Natural Science Foundation of China(2016JQ6050)
文摘This paper presents a combined strategy to solve the trajectory online optimization problem for unmanned combat aerial vehicle (UCAV). Firstly, as trajectory directly optimizing is quite time costing, an online trajectory functional representation method is proposed. Considering the practical requirement of online trajectory, the 4-order polynomial function is used to represent the trajectory, and which can be determined by two independent parameters with the trajectory terminal conditions; thus, the trajectory online optimization problem is converted into the optimization of the two parameters, which largely lowers the complexity of the optimization problem. Furthermore, the scopes of the two parameters have been assessed into small ranges using the golden section ratio method. Secondly, a multi-population rotation strategy differential evolution approach (MPRDE) is designed to optimize the two parameters; in which, 'current-to-best/1/bin', 'current-to-rand/1/bin' and 'rand/2/bin' strategies with fixed parameter settings are designed, these strategies are rotationally used by three subpopulations. Thirdly, the rolling optimization method is applied to model the online trajectory optimization process. Finally, simulation results demonstrate the efficiency and real-time calculation capability of the designed combined strategy for UCAV trajectory online optimizing under dynamic and complicated environments.
基金This work was supported in part by the National Natural Science Foundation of China(Nos.62022042,62273181 and 62073166)in part by the Fundamental Research Funds for the Central Universities(No.30919011105)in part by the Open Project of the Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment(No.GDSC202017).
文摘This paper considers the problem of distributed online regularized optimization over a network that consists of multiple interacting nodes.Each node is endowed with a sequence of loss functions that are time-varying and a regularization function that is fixed over time.A distributed forward-backward splitting algorithm is proposed for solving this problem and both fixed and adaptive learning rates are adopted.For both cases,we show that the regret upper bounds scale as O(VT),where T is the time horizon.In particular,those rates match the centralized counterpart.Finally,we show the effectiveness of the proposed algorithms over an online distributed regularized linear regression problem.
文摘In this paper,the problem of online distributed optimization subject to a convex set is studied via a network of agents.Each agent only has access to a noisy gradient of its own objective function,and can communicate with its neighbors via a network.To handle this problem,an online distributed stochastic mirror descent algorithm is proposed.Existing works on online distributed algorithms involving stochastic gradients only provide the expectation bounds of the regrets.Different from them,we study the high probability bound of the regrets,i.e.,the sublinear bound of the regret is characterized by the natural logarithm of the failure probability's inverse.Under mild assumptions on the graph connectivity,we prove that the dynamic regret grows sublinearly with a high probability if the deviation in the minimizer sequence is sublinear with the square root of the time horizon.Finally,a simulation is provided to demonstrate the effectiveness of our theoretical results.
基金supported by the National Natural Science Foundation of China(Nos.62103169,51875380)the China Postdoctoral Science Foundation(No.2021M691313).
文摘This paper focuses on the online distributed optimization problem based on multi-agent systems. In this problem, each agent can only access its own cost function and a convex set, and can only exchange local state information with its current neighbors through a time-varying digraph. In addition, the agents do not have access to the information about the current cost functions until decisions are made. Different from most existing works on online distributed optimization, here we consider the case where the cost functions are strongly pseudoconvex and real gradients of the cost functions are not available. To handle this problem, a random gradient-free online distributed algorithm involving the multi-point gradient estimator is proposed. Of particular interest is that under the proposed algorithm, each agent only uses the estimation information of gradients instead of the real gradient information to make decisions. The dynamic regret is employed to measure the proposed algorithm. We prove that if the cumulative deviation of the minimizer sequence grows within a certain rate, then the expectation of dynamic regret increases sublinearly. Finally, a simulation example is given to corroborate the validity of our results.
基金This work was supported by the National Key R&D Program of China(Grant Nos.2018YFA0306600 and 2019YFA0308100)the National Natural Science Foundation of China(Grant Nos.92265114,92265204,and 11875159)the Research Initiation Project(No.K2022MB0PI02)of Zhejiang Lab.
文摘The nitrogen-vacancy(NV)center in diamond has been developed as a promising platform for quantum sensing,especially for magnetic field measurements in the nano-tesla range with a nano-meter resolution.Optical spin readout performance has a direct effect on the signal-to-noise ratio(SNR)of experiments.In this work,we introduce an online optimization method to customize the laser waveform for readout.Both simulations and experiments reveal that our new scheme optimizes the optically detected magnetic resonance in NV center.The SNR of optical spin readout has been witnessed a 44.1%increase in experiments.In addition,we applied the scheme to the Rabi oscillation experiment,which shows an improvement of 46.0%in contrast and a reduction of 12.1%in mean deviation compared to traditional constant laser power SNR optimization.This scheme is promising to improve sensitivities for a wide range of NV-based applications in the future.
文摘This article presents a new two-axis solar tracker based on an online optimization algorithm so as to track the position of the sun without using its movement model.In this research,four well-known optimization algorithms are employed to find the two unknown parameters named azimuth and zenith angles,which determine the position of the sun.The magnitude of the sunray is considered as the cost function of all algorithms.Then,several experiments are carried out to find the best optimization algorithm with optimal population size,number of iterations,and also the best initialization method.Uniform initialization leads to faster convergence compared to random initialization.The results clearly show that the particle swarm optimization algorithm with a population size of 15 and 7 iterations using uniform initialization method has better performance than the other algorithms,with a convergence time of less than 40 s.The average fitness value or voltage received by the tracker is 2.4 Volts in this method,which is higher than other methods.TLBO also performs well with a population size of 15 and 7 iterations.Afterward,the artificial neural network with one hidden layer and 20 neurons is employed to predict these two parameters in each day and moment in a year in Shiraz city according to the experimental data extracted from PSO.Number of the day from January and the time are inputs and zenith and azimuth angles are considered the output of neural network modeling.The performance of the proposed ANN model is evaluated using regression plots,demonstrating a strong correlation between predicted and target outputs.Finally,the outcomes reveal the feasibility of using online optimization algorithms and neural network modeling in an effort to bypass the complex mathematical model of mechatronic systems and predict the movement of the sun automatically.
基金Project(2011BAE22B05)supported by the National Science and Technology Pillar Program during the 12th Five-year Plan of China
文摘Combustion noise takes large proportion in diesel engine noise and the studies of its influence factors play an important role in noise reduction. Engine noise and cylinder pressure measurement experiments were carried out. And the improved attenuation curves were obtained, by which the engine noise was predicted. The effect of fuel injection parameters in combustion noise was investigated during the combustion process. At last, the method combining single variable optimization and multivariate combination was introduced to online optimize the combustion noise. The results show that injection parameters can affect the cylinder pressure rise rate and heat release rate, and consequently affect the cylinder pressure load and pressure oscillation to influence the combustion noise. Among these parameters, main injection advance angle has the greatest influence on the combustion noise, while the pilot injection interval time takes the second place, and the pilot injection quantity is of minimal impact. After the optimal design of the combustion noise, the average sound pressure level of the engine is distinctly reduced by 1.0 d B(A) generally. Meanwhile, the power, emission and economy performances are ensured.
文摘In this work, an online calibration mechanism is proposed for the combustion phase in a diesel engine. In particular, a simplified event-based engine model, of which the output predicts the optimum combustion phase, is used to aid the calibration, and the model is updated online along with the engine operation to keep the integrity high so as to improve the quality of optimum combustion phase prediction. It is found this mechanism can be applied to develop an online automated calibration process when the engine system shifts to a new operating point. of the proposed mechanism. Engine test results are included to demonstrate the effectiveness
基金supported in part by the National Key R&D Program of China(No.2022YFA1004000)the National Natural Science Foundation of China(Nos.11971089 and 12271076).
文摘In this paper,we consider online convex optimization(OCO)with time-varying loss and constraint functions.Specifically,the decision-maker chooses sequential decisions based only on past information;meantime,the loss and constraint functions are revealed over time.We first develop a class of model-based augmented Lagrangian methods(MALM)for time-varying functional constrained OCO(without feedback delay).Under standard assumptions,we establish sublinear regret and sublinear constraint violation of MALM.Furthermore,we extend MALM to deal with time-varying functional constrained OCO with delayed feedback,in which the feedback information of loss and constraint functions is revealed to decision-maker with delays.Without additional assumptions,we also establish sublinear regret and sublinear constraint violation for the delayed version of MALM.Finally,numerical results for several examples of constrained OCO including online network resource allocation,online logistic regression and online quadratically constrained quadratical program are presented to demonstrate the efficiency of the proposed algorithms.
基金supported in part by the National Research Foundation of Korea (NRF-2021H1D3A2A01082705).
文摘The dynamic traveling salesman problem(DTSP)is significant in logistics distribution in real-world applications in smart cities,but it is uncertain and difficult to solve.This paper proposes a scheme library-based ant colony optimization(ACO)with a two-optimization(2-opt)strategy to solve the DTSP efficiently.The work is novel and contributes to three aspects:problemmodel,optimization framework,and algorithmdesign.Firstly,in the problem model,traditional DTSP models often consider the change of travel distance between two nodes over time,while this paper focuses on a special DTSP model in that the node locations change dynamically over time.Secondly,in the optimization framework,the ACO algorithm is carried out in an offline optimization and online application framework to efficiently reuse the historical information to help fast respond to the dynamic environment.The framework of offline optimization and online application is proposed due to the fact that the environmental change inDTSPis caused by the change of node location,and therefore the newenvironment is somehowsimilar to certain previous environments.This way,in the offline optimization,the solutions for possible environmental changes are optimized in advance,and are stored in a mode scheme library.In the online application,when an environmental change is detected,the candidate solutions stored in the mode scheme library are reused via ACO to improve search efficiency and reduce computational complexity.Thirdly,in the algorithm design,the ACO cooperates with the 2-opt strategy to enhance search efficiency.To evaluate the performance of ACO with 2-opt,we design two challenging DTSP cases with up to 200 and 1379 nodes and compare them with other ACO and genetic algorithms.The experimental results show that ACO with 2-opt can solve the DTSPs effectively.
基金Supported by National Natural Science Foundation of China(Grant No.52075036)Key Technologies Research and Development Program of China(Grant No.2022YFC3302204).
文摘The Chinese express delivery industry processes nearly 110 billion items in 2022,averaging an annual growth rate of 200%.Among the various types of sorting systems used for handling express items,cross-belt sorting systems stand out as the most crucial.However,despite their high degree of automation,the workload for operators has intensified owing to the surging volume of express items.In the era of Industry 5.0,it is imperative to adopt new technologies that not only enhance worker welfare but also improve the efficiency of cross-belt systems.Striking a balance between efficiency in handling express items and operator well-being is challenging.Digital twin technology offers a promising solution in this respect.A realization method of a human-machine integrated digital twin is proposed in this study,enabling the interaction of biological human bodies,virtual human bodies,virtual equipment,and logistics equipment in a closed loop,thus setting an operating framework.Key technologies in the proposed framework include a collection of heterogeneous data from multiple sources,construction of the relationship between operator fatigue and operation efficiency based on physiological measurements,virtual model construction,and an online optimization module based on real-time simulation.The feasibility of the proposed method was verified in an express distribution center.
基金Supported by National Natural Science Foundation of China(62103169,51875380)China Postdoctoral Science Foundation(2021M691313)。
文摘This paper studies an online distributed optimization problem over multi-agent systems.In this problem,the goal of agents is to cooperatively minimize the sum of locally dynamic cost functions.Different from most existing works on distributed optimization,here we consider the case where the cost function is strongly pseudoconvex and real gradients of objective functions are not available.To handle this problem,an online zeroth-order stochastic optimization algorithm involving the single-point gradient estimator is proposed.Under the algorithm,each agent only has access to the information associated with its own cost function and the estimate of the gradient,and exchange local state information with its immediate neighbors via a time-varying digraph.The performance of the algorithm is measured by the expectation of dynamic regret.Under mild assumptions on graphs,we prove that if the cumulative deviation of minimizer sequence grows within a certain rate,then the expectation of dynamic regret grows sublinearly.Finally,a simulation example is given to illustrate the validity of our results.
文摘This paper describes the design and experimental tests of a path planning and reference tracking algorithm for autonomous ground vehicles. The ground vehicles under consideration are equipped with forward looking sensors that provide a preview capability over a certain horizon. A two-level control framework is proposed for real-time implementation of the model predictive control （MPC） algorithm, where the high-level performs on-line optimization to generate the best possible local reference respect to various constraints and the low-level commands the vehicle to follow realistic trajectories generated by the high-level controller. The proposed control scheme is implemented on an indoor testbed through networks with satisfactory performance.
基金This work was supported by the National Natural Science Foundation of China(No.U19B2024)the National Key Research and Development Program(No.2018YFE0207600)。
文摘Decentralized Online Learning(DOL)extends online learning to the domain of distributed networks.However,limitations of local data in decentralized settings lead to a decrease in the accuracy of decisions or models compared to centralized methods.Considering the increasing requirement to achieve a high-precision model or decision with distributed data resources in a network,applying ensemble methods is attempted to achieve a superior model or decision with only transferring gradients or models.A new boosting method,namely Boosting for Distributed Online Convex Optimization(BD-OCO),is designed to realize the application of boosting in distributed scenarios.BD-OCO achieves the regret upper bound O(M+N/MNT)where M measures the size of the distributed network and N is the number of Weak Learners(WLs)in each node.The core idea of BD-OCO is to apply the local model to train a strong global one.BD-OCO is evaluated on the basis of eight different real-world datasets.Numerical results show that BD-OCO achieves excellent performance in accuracy and convergence,and is robust to the size of the distributed network.
基金This work is supported by the Joint Research Fund in Smart Grid(No.U1966601)under cooperative agreement between the National Natural Science Foundation of China(NSFC)and State Grid Corporation of China.
文摘In generalized Nash equilibrium(GNE)seeking problems over physical networks such as power grids,the enforcement of network constraints and time-varying environment may bring high computational costs.Developing online algorithms is recognized as a promising method to cope with this challenge,where the task of computing system states is replaced by directly using measured values from the physical network.In this paper,we propose an online distributed algorithm via measurement feedback to track the GNE in a time-varying networked resource sharing market.Regarding that some system states are not measurable and measurement noise always exists,a dynamic state estimator is incorporated based on a Kalman filter,rendering a closed-loop dynamics of measurement-feedback driven online algorithm.We prove that,with a fixed step size,this online algorithm converges to a neighborhood of the GNE in expectation.Numerical simulations validate the theoretical results.
