Online Optimization Research Articles

Privacy Preserving Distributed Bandit Residual Feedback Online Optimization Over Time-Varying Unbalanced Graphs

Privacy Preserving Distributed Bandit Residual Feedback Online Optimization Over Time-Varying Unbalanced Graphs

Gust load alleviation of a flexible flying wing with linear parameter-varying modeling and model predictive control

This paper presents a practical model predictive control (MPC) framework for gust load alleviation of a flexible flying wing. Both the controller solving and state estimation are based on a reduced-order model, which features a linear parameter-varying (LPV) form, avoiding online linearization and reducing the scale of the corresponding quadratic programming problem. An improved modeling and model reduction process is used to enhance modeling efficiency and ensure that the reduced-order model can accurately capture the rigid-flexible coupled characteristics of the flexible flying wing under arbitrary gusts. By reconstructing the output of the control-oriented model to include both rigid-body motion and flexible vibrations, the rigid-flexible coupled multi-objective control is established as an MPC problem for reference tracking. The online optimization is formulated in a sparse fashion and combined with an iterative algorithm based on predicted trajectories, describing the variation of model dynamics within the prediction horizon more accurately. With a time-varying Kalman estimator for state updating, the closed-loop simulations are performed for gust alleviation performance validation. Additionally, the real-time potential of the proposed MPC framework is demonstrated through Monte Carlo simulations.

Distributed cooperative guidance strategy based on virtual negotiation and rolling optimization

The problem of multi-UAV cooperative guidance under target maneuvering conditions is studied, and a distributed cooperative guidance strategy based on virtual negotiation mechanism and rolling horizon optimization is proposed. Firstly, in order to improve the time coordination and guidance control effect at the same time, local coordination variables and objective functions are designed under the rolling horizon optimization framework, and a multi-constrained cooperative guidance optimization model in a limited time domain is constructed; secondly, in order to enhance the robustness of the guidance system to maneuvering targets, a disturbance observer based on nonlinear autoregressive neural network is designed; thirdly, for the control input coupling between UAVs, a virtual negotiation mechanism is proposed to achieve synchronous decision-making and eliminate internal "differences", and the root mean square propagation algorithm of Nesterov momentum is used to design the guidance command generation strategy; finally, digital simulation and semi-physical simulation experiments are carried out respectively. The results show that the proposed guidance strategy can cope with various interferences and improve the cooperative guidance effect through online prediction and optimization, and is expected to be further applied and tested in actual tasks.

Efficient nonlinear model predictive and active disturbance rejection control for trajectory tracking of unmanned vehicles

This article proposes an efficient trajectory tracking control strategy of unmanned vehicles. The method is based on nonlinear model predictive control (NMPC) and active disturbance reject control (ADRC). The designed control algorithm considers three challenges including nonlinear characteristics, multiple constraints, and external disturbance. First, NMPC method is presented for the nonlinear vehicle model with multiple constraints. To relax inequality constraints and reduce the heavy calculation burden, the penalty term with the variable factor is added to the cost function, an improved continuous/generalized minimum residual method is proposed to solve NMPC online optimization problem. Then, an ADRC scheme is designed to estimate the unknown disturbance via extended state observer, and compensate them by feedback control law in real time, the corresponding parameters are obtained by a novel particle swarm optimization algorithm to further improve the control precision. It ensures the stability to a certain extent. Finally, the results indicate the designed algorithm can raise the calculation efficiency and meet the real-time requirements, and obviously increase the tracking accuracy and robustness performance.

Development of AI-assisted microscopy frameworks through realistic simulation with pySTED

The integration of artificial intelligence into microscopy systems significantly enhances performance, optimizing both image acquisition and analysis phases. Development of artificial intelligence-assisted super-resolution microscopy is often limited by access to large biological datasets, as well as by difficulties to benchmark and compare approaches on heterogeneous samples. We demonstrate the benefits of a realistic stimulated emission depletion microscopy simulation platform, pySTED, for the development and deployment of artificial intelligence strategies for super-resolution microscopy. pySTED integrates theoretically and empirically validated models for photobleaching and point spread function generation in stimulated emission depletion microscopy, as well as simulating realistic point-scanning dynamics and using a deep learning model to replicate the underlying structures of real images. This simulation environment can be used for data augmentation to train deep neural networks, for the development of online optimization strategies and to train reinforcement learning models. Using pySTED as a training environment allows the reinforcement learning models to bridge the gap between simulation and reality, as showcased by its successful deployment on a real microscope system without fine tuning.

Model predictive control with real-time variable weight for civil aircraft towing taxi-out control systems

Unlike the traditional method of taxying, where civil aircraft reach the runway by relying on their engines, the new mode of towing taxi-out may become preferred because of its lower energy consumption, lower emissions, and higher efficiency. To improve the tracking accuracy and lateral stability of the towing system, this study used a two-level time-varying weight control method (RMPC) based on model predictive control (MPC) and fuzzy control. The tractor speed and front wheel angle were set as the control variables in the MPC controller, and the real-time lateral displacement error and yaw angle error of the tractor were set as the fuzzy control inputs. The influence of the weight matrix on the accuracy and stability of path tracking was coordinated by online optimization of the MPC objective function weight. The simulations of multiple working conditions were performed using TruckSim software in Simulink, which showed that the proposed control method can limit the lateral displacement error of the tractor and aircraft within 0.4 m, which can be reduced by 70.83% and 77.4% compared with the single MPC, respectively. Additionally, the maximum yaw angle errors of the tractor and aircraft are reduced by 76.11% and 51.31% compared with the single MPC, respectively. Furthermore, the yaw angle error of tractors can be limited to 4° and that of aircraft can be limited to 6.5°, which is an improvement of the lateral stability and driving safety for the civil aircraft towing system. The new controller may provide technical insights and support for the practical development and safe application of the towing taxi-out mode.

AI-based security functions co optimization in the SFC

Dealing with large-scale attack traffic and complex attack scenarios can be challenging for a single attack detection system in the 6G era. The ubiquitous artificial intelligence security services enabled by the AI-based Security Functions (AISF) and Service Function Chain (SFC) become strong candidate to solve this problem. However, supervised learning-based AISF requires a significant amount of manually labeled data and is unable to adapt to changing attack scenarios once it is deployed. To this end, we propose an AISF co-optimization method in the SFC. The goal is to use unlabeled network traffic samples to update the model based on pseudo-labeling and co-training. First, we model the AISF chain composed of AISFs with various detection targets and feature subspaces. We also define its detection capability evaluation metrics and final detection result. Then, we design an AISF co-optimization flow including online detection and online optimization workflows. In online optimization, we design the dynamic threshold of normalized comprehensive confidence to generate pseudo-labels for network traffic samples and combine labeled and pseudo-labeled samples to train the new models of AISFs. These models replace the previous ones and continue to detect and optimize. Experimental results in a prototype system show that compared with a single AISF, the AISF chain has higher detection capabilities that can even detect unknown attacks to a certain extent, and co-optimization can make better use of unlabeled network traffic samples than individual optimization.

Optimalisasi Dashboard Pemesanan Makanan Online Menggunakan Looker dan JavaScript

Advances in information technology have enabled rapid development in internet-based services, including online food ordering applications. This growth demands efficient data management and analysis systems to improve user experience and operational performance. This research focuses on developing an optimal online food ordering dashboard using Looker and JavaScript. Research methods include needs identification, literature study, needs analysis, design, and implementation. The research results show that the dashboard developed is able to manage and analyze order data effectively, identify trends, predict customer needs, and increase operational efficiency. This dashboard visualizes important information such as number of orders based on age, gender, income, as well as customer behavior analysis. In doing so, service providers can gain better insight into consumer behavior and ordering trends, supporting more informed and strategic decision making. The results of this study contribute to the literature on the use of data visualization technologies in the online food service sector.

Online control parameter optimization design for multi-machine coordinated loading system of hazardous substances

To address the parameter instability issues in hazardous materials handling during multi-machine loading and unloading operations, we propose a Full-Scale Smart Parameter Optimization Control (FSPOC) system specifically designed for multi-machine coordination. This system leverages a novel fish scale prediction algorithm tailored for cooperative multi-machine environments. Initially, the fish scale prediction algorithm, inspired by bionic fish scales, is developed to predict future system behavior by analyzing historical data. Building on this algorithm, we introduce a disturbance cancellation control theorem and design a parameter optimization controller to enhance stability in high-dimensional nonlinear spaces. The FSPOC method is then applied to a multi-machine cooperative system, enabling online distributed parameter optimization for complex systems with multiple degrees of freedom. The effectiveness of the proposed method was validated through simulations, where it was compared with two other optimization techniques: Genetic Algorithm-based PID (GAPID) and Chaotic Atomic Search Algorithm-based PID (CHASO). The simulation results confirm the superiority of the FSPOC method.

Formulating iron ore pellet induration process in an industrial straight grate system: Real-time experimentation and validation

Understanding the complex process of iron ore pellet induration is crucial in a straight grate system in the steelmaking industry. There is no attempt made so far to predict the behavior of this unit considering the physiochemical processes (drying, carbon combustion and limestone calcination) along with the heat transfer within the pellet, and between the hot gas and pellet. Moreover, the reported models are validated mainly with the pot test data. To address these research gaps, in this contribution, a rigorous model framework is proposed for an industrial induration unit considering all these above-mentioned issues together with some other practical aspects, including the heat transfer from hot gas to grate bar and heat loss due to air leakage into the system. To validate this rigorous formulation, attempt is further made to perform the real-time experimentation with induration system equipped with a thermocar. It is shown first time that the predicted temperature profiles of the pellets, grate bar and exit gas associated with the running bed are consistent with the plant data. The fuel consumption data is further obtained to investigate the performance of the proposed formulation at different operating regimes. It is observed that the mean absolute percentage error (MAPE) between the measured and predicted fuel rates is reasonably low (i.e., 4.2%). With this, it is recommended to use the proposed formulation for online property prediction, process design, optimization, troubleshooting, control and scale-up of the induration unit.

Lyapunov-based robust model predictive tracking controller design for discrete-time linear systems with ellipsoidal terminal constraint

In this article, a robust model predictive control (MPC) method is introduced based on Lyapunov-theory to control the discrete-time uncertain linear systems due to external disturbances. The structure of the proposed controller consists of two parts designed based on the offline/online schemes. In the offline-scheme, an H ∞ -based robust tracking controller is provided to satisfy the robust and tracking performance. Based on the H ∞ performance, a new linear matrix inequality problem is developed to calculate the offline-controller gains. By considering the Lyapunov-function of the offline-controller as the terminal cost of the MPC and the designed offline-controller, the online optimisation problem (OOP) is structured. This means, the MPC is designed based on the stabilised system to satisfy the physical limitations of the overall controller and the system states. Moreover, to improve the feasibility problem of the OOP, an ellipsoidal terminal constraint is added to the proposed MPC problem. Therefore, compared with the existing min–max MPC and tube MPC, the advantages of the overall controller are feasibility improvement and reducing the computational complexity with less conservatism while the robustness against parametric uncertainties and disturbances is achieved. Two simulation examples are employed to show the superiorities of the proposed robust MPC.

Multi-objective online driving strategy optimization for energy storage tram considering battery life

Compared with the traditional overhead contact grid or third-rail power supply, energy storage trams equipped with lithium batteries have been developed rapidly because of their advantages of flexible railway laying and high regenerative braking energy utilization. However, trams may face expensive battery replacement costs due to battery degradation. Therefore, this paper proposes a multi-objective optimization method for the tram's driving strategy to reduce operational energy consumption and extend battery life. The method describes the optimization problem as second-order cone programming (SOCP). It uses the interior point algorithm for fast solving, thus obtaining the theoretical optimal speed trajectory in real-time. This feature enables the method to be applied online. To verify the effectiveness and robustness of the proposed method, we conducted simulations under actual operating conditions. The simulation results show that the proposed multi-objective optimization method can extend the battery life by 40 % compared to the single-objective optimization method for energy saving. As a result, the total cost of the whole life cycle is reduced by 6.54 %. Furthermore, this online optimization driving strategy method can effectively deal with unexpected conditions during the actual operation of the tram to ensure the secure operation, punctual arrival, and precise parking of trams. Finally, the effectiveness of the proposed method is further verified by a hardware-in-the-loop (HIL) test.

A new voltage sensitivity-based distributed feedback online optimization for voltage control in active distribution networks

Rising sustainability, environmental, and decarbonization considerations have prompted a recent increase in the integration of inverter-based distributed energy resources (DER), such as photovoltaics (PV), into the power grid. This trend raises concerns about potential voltage violations within distribution systems, emphasizing the growing relevance of effective voltage regulation. Utilizing smart inverters for active and reactive power control proves highly efficient in addressing voltage rise issues within active distribution networks (ADN). This work proposes a distributed feedback online optimization (FOO) strategy for voltage regulation in ADNs. The FOO strategy utilizes online recursive least squares (RLS) sensitivity-based feedback, relying on local measurements to iteratively compute optimal reactive power set points for DERs, ensuring bus voltage regulation to within acceptable limits. The distributed control algorithm employs a primal-dual update approach, enabling coordinated information exchange between neighboring controllers. This online feedback-based control offers a promising solution for real-time coordinated voltage control in distribution grids, particularly with the increasing integration of inverter-based DERs at the grid's edge. The approach demonstrates robustness under varying conditions, communication dynamics, and topology changes, validated using real distribution feeders with actual solar PV production and consumption data. Advancements in sensing, communication systems, and smart grid tools further enhance the feasibility and scalability of this strategy for evolving power systems.

Deep Koopman-operator-based model predictive control for free-floating space robots with disturbance observer

This paper investigates the optimal tracking control problem of free-floating space robots in the presence of non-ideal factors, such as model uncertainties and external disturbances. To address this issue, we first leverage the deep Koopman operator, facilitated with a deep neural network, to establish an offline formulation of a global linearization model for a micro-nano free-floating space robot. Based on the estimated linearization model, we then employ the model predictive control method for online optimization control, achieving a significantly reduced computational burden. Additionally, a decay factor is integrated into the model predictive control optimization objective function to balance the precision of joint angles tracking with the suppression of base satellite attitude disturbance. To further address the modeling inaccuracies and external disturbances, we incorporate a disturbance observer based on a radial basis function neural network for online compensation within the model predictive control framework. This augmentation enhances tracking precision and robustness. Several groups of simulation results are carried out to demonstrate the effectiveness of the proposed method, showing its capability of energy consumption, disturbance rejection and enhanced robustness. These highlight the potential of the proposed method to improve the control performance of free-floating space robots, even in the absence of a precise dynamic model.

Residential demand response online optimization based on multi-agent deep reinforcement learning

Demand side management in the context of smart grids exploits the value stream of load flexibility, promoting stable and economic operation of the power grid. To address multi-uncertainties such as device characteristics, distributed renewable generation, and residential electricity demand, this paper proposes a model-free framework based on multi-agent deep reinforcement learning to achieve efficient online optimization of demand response strategies. Firstly, the demand side management is formulated as a partially observable Markov game. Then, a multi-agent soft actor-critic algorithm is proposed, combining a temporal electricity price feature extraction technology to improve learning efficiency. Reward correction mechanism is designed to avoid new charging and discharging peaks in the demand response. Finally, case studies demonstrate the effectiveness of the proposed method in reducing the electricity cost and aggregate peak demand of residents. The proposed method reduces the daily average cost by 21.40%, 8.09%, and 5.88% compared to MAPPO, MADDPG, and SUMADRL, and exhibits the highest training efficiency and scalability against the benchmarks.

A novel optical orthogonality control approach based on noise propagation and its application in spin-exchange relaxation-free co-magnetometers

This paper presents a non-destructive optical orthogonality control method based on adaptive gradient descent for spin-exchange relaxation-free co-magnetometers (SERFCM). The spin-exchange optical pumping technique serves as the foundation of SERFCM measurement. Analyzing the impact of pump power fluctuation on the transverse polarizability of alkali metal atoms in the presence of non-orthogonal beams, considering stochastic noise propagation, is addressed. Subsequently, an optical orthogonal calibration scheme is proposed. Leveraging the proposed orthogonal calibration scheme, an adaptive gradient descent method based on the reference model is formulated, and simulation validates its effectiveness in addressing the rapid online optimization control of SERFCM optical quadrature. Experimental results indicate that the proposed optical orthogonal calibration scheme offers higher accuracy, with the proposed optical orthogonal control algorithm saving 34% of the time compared to the traditional algorithm. Finally, the stability of SERFCM is evaluated using Allan variance, demonstrating that the adjusted SERFCM enhances short-term stability by 84% at around 1s and long-term stability by 639% at around 300s.

Relationships of optimal online learning with academic aptitude, effort, and context mediated by academic adjustment

Relationships of optimal online learning with academic aptitude, effort, and context mediated by academic adjustment

Mitigating domain shift in online process monitoring for material extrusion additive manufacturing via transfer learning

The additive manufacturing (AM) method has experienced rapid growth in recent decades. However, its application in end-use products is constrained by printing defects. Therefore, online process evaluation and optimization are crucial for producing parts that meet quality standards. Data-driven methods have been extensively employed for in-situ process evaluation and control in various AM processes to establish correlations among process parameters, process information, and part quality. However, a critical weakness of these methods is their poor generalization, primarily due to two factors: the diverse nature of printing itself and the significant influence of environmental factors. This study proposes a framework for in-situ process evaluation and control of AM using transfer learning to address the domain shift problems. It highlights a significant domain shift issue in AM monitoring, especially when using deep learning models. A comprehensive monitoring system for material extrusion additive manufacturing is constructed to collect data from different domains, emphasizing the notable differences in data characteristics across these domains. Lastly, transfer learning methods are tested to tackle the domain shift issue in AM, enhancing the robustness and reliability of the monitoring system.

A Time-Series Feature-Extraction Methodology Based on Multiscale Overlapping Windows, Adaptive KDE, and Continuous Entropic and Information Functionals

We propose a new methodology to transform a time series into an ordered sequence of any entropic and information functionals, providing a novel tool for data analysis. To achieve this, a new algorithm has been designed to optimize the Probability Density Function (PDF) associated with a time signal in the context of non-parametric Kernel Density Estimation (KDE). We illustrate the applicability of this method for anomaly detection in time signals. Specifically, our approach combines a non-parametric kernel density estimator with overlapping windows of various scales. Regarding the parameters involved in the KDE, it is well-known that bandwidth tuning is crucial for the kernel density estimator. To optimize it for time-series data, we introduce an adaptive solution based on Jensen–Shannon divergence, which adjusts the bandwidth for each window length to balance overfitting and underfitting. This solution selects unique bandwidth parameters for each window scale. Furthermore, it is implemented offline, eliminating the need for online optimization for each time-series window. To validate our methodology, we designed a synthetic experiment using a non-stationary signal generated by the composition of two stationary signals and a modulation function that controls the transitions between a normal and an abnormal state, allowing for the arbitrary design of various anomaly transitions. Additionally, we tested the methodology on real scalp-EEG data to detect epileptic crises. The results show our approach effectively detects and characterizes anomaly transitions. The use of overlapping windows at various scales significantly enhances detection ability, allowing for the simultaneous analysis of phenomena at different scales.

Digital twin for monitoring threshing performance of combine harvesters

The threshing performance of combine harvesters is crucial for minimizing grain losses. However, threshing is a highly complex process, and direct monitoring is infeasible due to high rotational speeds and concealed locations. This study introduces a method to enhance threshing performance monitoring by using digital twin (DT). A sensor network was developed to acquire on-site data. The DT was constructed by integrating a surrogate model of a discrete element model, which describes system responses, with a neural network predicting the future grain breakage rate (GBR). Test results indicate that the DT improved threshing monitoring capabilities and facilitated online optimization of settings. Compared to manual and feedback control modes, the GBR was reduced by 2.08 % and 1.00 %, and the working speed increased by 1.12 km/h and 1.47 km/h. The practical value of this study lies in reducing grain loss and enhancing harvest efficiency, providing advanced technological support for mechanical grain harvesting.