Complete State Information Research Articles

Prescribed‐time resilient current‐sensorless DC power converter control

AbstractWhile conventional control techniques for stabilizing power electronic converters with constant‐power load (CPL) often rely on complete system state information, the associated challenges, including the cost and increased size due to the installation of multiple sensors, necessitate exploration of alternative methodologies. To address these concerns, a novel observer‐based sliding‐mode control approach is proposed. This approach aims to enhance cost‐effectiveness and reliability in the context of a DC‐DC buck converter supplying power to an unknown CPL. The core concept involves designing a state observer to estimate the system's current based on voltage measurements. A non‐certainty equivalent adaptive update law is then introduced to estimate the unknown CPL, incorporating feedback from voltage measurements and current estimation. To guarantee stability in the face of uncertainties in system parameters, a thorough analysis utilizing the Lyapunov theorem is conducted on the closed‐loop system. The efficacy and feasibility of the proposed control algorithm are substantiated through experimental results on the OPAL‐RT platform, showcasing its promising performance.

Resilient microgrid formation considering communication interruptions

AbstractDistribution system (DS) communication failures following extreme events often degrade monitoring and control functions, thus preventing the acquisition of complete global DS component state information, on which existing post‐disaster DS restoration methods are based. This paper proposes methods of inferring the states of DS components in the case of incomplete component state information. By using the known DS information, the operating states of unobservable DS branches and buses can be inferred, providing complete information for DS performance restoration before full communication recovery.

An improved stochastic car-following model considering the complete state information of multiple preceding vehicles under connected vehicles environment

Studying the car-following model contributes to improving vehicle driving efficiency and traffic flow stability within the context of connected automated vehicles (CAVs). Therefore, an improved stochastic car-following model (SMLVM) is proposed in this paper, building upon the traditional FVD model and BLVD model. This new model comprehensively considers four deterministic factors: acceleration, velocity, headway, and optimal speed memory of multiple preceding vehicles, as well as the comprehensive influence of stochastic factors on car-following behavior during the traveling process. Meanwhile, weight functions are utilized to represent the varying strengths of influence from vehicles ahead with different positions and speeds on the following vehicle. The SMLVM model is categorized and discussed based on practical application scenarios in this paper. When the focus is primarily on fundamental traffic flow behavior, relative motion between vehicles, and other basic principles, the SMLVM model degenerates into the MLVM model. Through stability analysis and simulation experiments on MLVM model, critical stability conditions are derived. Comparisons with other classical models demonstrate that the MLVM model exhibits stronger stability and resistance to disturbances. When the focus is primarily on various realistic driving scenarios, through simulation experiments on SMLVM models in different scenarios, it is found that the influence of stochastic factors on SMLVM models mainly depends on the magnitude of the intensity of stochastic fluctuations, which is rarely affected by the specific functional expressions of the stochastic terms. Finally, empirical analysis is conducted by selecting five sets of processed data from the next-generation simulation (NGSIM) dataset. Based on parameter estimation using the SMLVM model, and a comparison is made with the FVD model and BLVD model. The results confirm that the model proposed in this paper fits vehicle speeds better and absorbs disturbances more effectively, and exhibiting greater stability in traffic flow.

Residual Q-Networks for Value Function Factorizing in Multiagent Reinforcement Learning.

Multiagent reinforcement learning (MARL) is useful in many problems that require the cooperation and coordination of multiple agents. Learning optimal policies using reinforcement learning in a multiagent setting can be very difficult as the number of agents increases. Recent solutions such as value decomposition networks (VDNs), QMIX, QTRAN, and QPLEX adhere to the centralized training and decentralized execution (CTDE) scheme and perform factorization of the joint action-value functions. However, these methods still suffer from increased environmental complexity, and at times fail to converge in a stable manner. We propose a novel concept of residual Q-networks (RQNs) for MARL, which learns to transform the individual Q -value trajectories in a way that preserves the individual-global-max (IGM) criteria, but is more robust in factorizing action-value functions. The RQN acts as an auxiliary network that accelerates convergence and will become obsolete as the agents reach the training objectives. The performance of the proposed method is compared against several state-of-the-art techniques such as QPLEX, QMIX, QTRAN, and VDN, in a range of multiagent cooperative tasks. The results illustrate that the proposed method, in general, converges faster, with increased stability, and shows robust performance in a wider family of environments. The improvements in results are more prominent in environments with severe punishments for noncooperative behaviors and especially in the absence of complete state information during training time.

Novel extended Kalman filter + H∞$$ {\mathbf{H}}_{\infty } $$ optimal output feedback control configuration for small satellites with high pointing stability requirements

SummaryOne of the most challenging subsystems for the CubeSats is the attitude determination and control system (ADCS) because it involves hardware and software integration, advanced strategies and algorithms to determine and control the attitude that impacts on space maneuvering and well working of a large set of payload (e.g., the optical payload). Although ADCS is largely studied, it still requires further investigations and analysis in order to achieve high‐performance and a reduced efforts. This paper presents an effective and reliable solution for the ADCS of CubeSats involved in Earth observation missions. The solution is based on an innovative framework that leverages the strengths of two distinct methodologies—H‐infinity optimal output feedback control and the extended Kalman filter (EKF)‐to significantly enhance the pointing stability of satellite systems. While H‐infinity optimal output feedback control is traditionally associated with partial state knowledge, we intentionally extend its application to scenarios with complete state information. The study includes a comparative performance analysis involving three algorithm configurations: the classic combination of EKF and model predictive control (MPC), the utilization of H‐infinity optimal output feedback control without EKF, and our proposed integrated approach. Performance evaluations are based on extrinsic indicators, demonstrating the advantages of our method in terms of pointing stability and overall control system performance. This research underscore the significance of combining innovative thinking with well‐established methodologies, unlocking new possibilities for stability enhancement in a range of engineering applications.

Intermittent output tracking control of heterogeneous multi-agent systems over wide-area clustered communication networks

In this paper, an output tracking problem is investigated for a wide-area heterogeneous multi-agent system with multiple leaders through intermittent communication, where the communication network consists of clustered subnetworks distributed in a wide area. Firstly, a hierarchical clustered network model is built to describe the communication network topology of heterogeneous agents communicated by an intermittent communication mechanism. Subsequently, dynamic compensators are designed for the heterogeneous agents to estimate the leaders’ state information and a dynamic state feedback hybrid control strategy is designed for each agent to ensure the output tracking consensus. Furthermore, the proposed hybrid control strategy is extended to a case where the complete state information of the agent itself is also not available. Then, under the proposed hybrid control strategy, the closed-loop tracking error system is modeled by a switched system. Consequently, sufficient conditions are derived for the stability of the closed-loop tracking error system under the proposed hybrid control strategy by using a piecewise Lyapunov function and convex synthesis methods. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed hybrid control strategy.

Finite-time synchronization of complex networks with partial communication channels failure

This paper addresses the finite-time synchronization problem of complex networks under a new communication constraint. The situation that some communication channels used to transmit sub-information between two nodes may fail is considered, which causes the receiving node to be unable to obtain complete state information. Moreover, due to less information transmission between nodes, the analysis of finite-time synchronization problem becomes more difficult. Therefore, several questions naturally arise here: Under this communication restriction, how to remodel the complex network, and can the network achieve finite-time synchronization? To better solve these questions, the complex network model with partial communication channels failure is established first. Then, based on the finite-time stability theory and state layered method, two finite-time synchronization criteria for complex networks are provided. Finally, the effectiveness of obtained results are verified by numerical simulations.

A general delta-based in-band network telemetry framework with extremely low bandwidth overhead

In-band network telemetry (INT) enriches network management at scale through the embedding of complete device-internal states into each packet along its forwarding path, yet such embedding of INT information also incurs significant bandwidth overhead in the data plane. We propose DeltaINT, a general INT framework that achieves extremely low bandwidth overhead and supports various packet-level and flow-level applications in network management. DeltaINTbuilds on the insight that state changes are often negligible at most time, so it embeds the complete state information into a packet only when the state change is deemed significant. We propose two variants for DeltaINTthat trade between bandwidth usage and measurement accuracy, while both variants achieve significantly lower bandwidth overhead than the original INT framework. We theoretically derive the time/space complexities and the guarantees of bandwidth mitigation for DeltaINT. We implement DeltaINTin both software and P4. Our evaluation shows that DeltaINTsignificantly mitigates the bandwidth overhead, and the deployment in a Tofino switch incurs limited hardware resource usage.

Privacy-Preserving Intelligent Resource Allocation for Federated Edge Learning in Quantum Internet

Federated edge learning (FEL) is a promising paradigm of distributed machine learning that can preserve data privacy while training the global model collaboratively. However, FEL is still facing model confidentiality issues due to eavesdropping risks of exchanging cryptographic keys through traditional encryption schemes. Therefore, in this paper, we propose a hierarchical architecture for quantum-secured FEL systems with ideal security based on the quantum key distribution (QKD) to facilitate public key and model encryption against eavesdropping attacks. Specifically, we propose a stochastic resource allocation model for efficient QKD to encrypt FEL keys and models. In FEL systems, remote FEL workers are connected to cluster heads via quantum-secured channels to train an aggregated global model collaboratively. However, due to the unpredictable number of workers at each location, the demand for secret-key rates to support secure model transmission to the server is unpredictable. The proposed systems need to efficiently allocate limited QKD resources (i.e., wavelengths) such that the total cost is minimized in the presence of stochastic demand by formulating the optimization problem for the proposed architecture as a stochastic programming model. To this end, we propose a federated reinforcement learning-based resource allocation scheme to solve the proposed model without complete state information. The proposed scheme enables QKD managers and controllers to train a global QKD resource allocation policy while keeping their private experiences local. Numerical results demonstrate that the proposed schemes can successfully achieve the cost-minimizing objective under uncertain demand while improving the training efficiency by about 50\% compared to state-of-the-art schemes.

Fully distributed event-triggered output feedback control for linear multi-agent systems with a derivable leader under directed graphs

This article studies the design method of fully distributed event-triggered output feedback protocol for linear multi-agent systems with a derivable leader under directed graphs. The research on such problems in the existing reference needs complete state feedback and the state variable of the adaptive law used in the literature controller is monotonic and non decreasing, which may make the control input exceed the actual saturation value, thus affecting the practical application. How to design a completely distributed event-triggered adaptive output feedback protocols for multi-agent systems with a derivable leader on directed graphs is an open issue. In order to overcome the difficulty of designing the controller without complete state information, we design the event-triggered output feedback protocol, and with the help of σ-modification techniques, ensure the boundedness of the adaptive law in proposed protocol. The conditions to ensure the realization of the leader–follower consensus and exclude Zeno behavior are given. Furthermore, the control protocol is fully distributed and uses neighbor information only when the event trigger conditions are satisfied. Finally, the availability of the proposed protocol is illustrated by simulation examples.

Nonlocal quantum state interferography

Quantum state interferography has recently been proposed as an interference-based technique for the measurement of the general qubit and pure qudit states [Phys. Rev. Lett. 125, 123601 (2020)]. This interferometric method exploits the relationship between the off-diagonal elements of the density matrix and the expectation values of non-Hermitian spin ladder operators. The extension of this method to a composite system is however nontrivial, as it involves the measurement of both single-photon and nonlocal multiphoton interferences. In this work, we present the theoretical analysis of the two-photon quantum state interferography of an arbitrary quantum state. We show that complete state information could in principle be obtained with two measurement settings and by recording six interferograms, using a single-photon sensitive imaging device. We then experimentally demonstrate two-photon nonlocal quantum state interferography of polarization-entangled photon pairs generated with type-I spontaneous parametric down conversion. Quantum state interferography of all four Bell states is performed. An upper bound on the separability of the bipartite states is established in terms of the visibility, phase average intensity, and phase shift of the interference patterns related to the antidiagonal elements of the matrix. Each of the four Bell states can be distinguished by the violation of these bounds uniquely.

Iterative EKF as a controller in novel MPC formulation: First principles model based IEKF-MPC for SISO systems

In the present study, novel MPC control strategy, using first principles models and complete state information is formulated using iterative EKF as predictive controller. This is based on extension of iterative EKF control estimation concept to MPC, instead of solution of an optimization problem approach followed in conventional MPC. The proposed approach incorporates all aspects of MPC including feedback correction term to handle process-model mismatch, move-suppression factor for control effort minimization, and constraints handling capability including terminal constraints. The performance of the proposed control strategy is evaluated through simulation case studies of SISO anaerobic digestion process and isothermal methyl methacrylate polymerization reactor with relative degree 1 and 2, respectively. The proposed strategy has shown superior performance over standard MPC in terms of faster response, set point tracking and disturbance rejection in both the case studies. Also, the proposed approach is found to be computationally more efficient than the standard MPC.

Nonlinear disturbance observer‐based adaptive feedback linearized model predictive controller design for a class of nonlinear systems

AbstractAdaptive control with less prior information of a nonlinear system subjected to external disturbance and parametric uncertainties is a longstanding problem in the control community. In this paper, a nonlinear disturbance observer‐based adaptive feedback linearized model predictive control (NDO‐AFLMPC) technique is proposed for a class of nonlinear MIMO systems. In this approach, a nonlinear disturbance observer (NDO) is used to observe the external disturbance of the system. A suitable constraint mapping algorithm is developed to map the input–output constraints within the proposed control scheme. Here, a multiple estimation model and the concept of second‐level adaptation technique is used to handle the parametric uncertainty of the real‐time plant. The boundedness of the estimated parameter is solved by developing the projection‐based parameter adaptation laws. Using an aerodynamic laboratory set‐up, known as the twin‐rotor MIMO system (TRMS), the effectiveness of the proposed control algorithm is verified. The complete state information of the real‐time system is provided to the proposed adaptive controller from an extended Kalman filter (EKF)‐based state observer. The performance of the proposed control algorithm is compared with other existing nonlinear control algorithms in the presence of unknown external disturbance and parametric uncertainties.

Deep Learning Approaches for Prognosis of Automated Skin Disease.

Skin problems are among the most common ailments on Earth. Despite its popularity, assessing it is not easy because of the complexities in skin tones, hair colors, and hairstyles. Skin disorders provide a significant public health risk across the globe. They become dangerous when they enter the invasive phase. Dermatological illnesses are a significant concern for the medical community. Because of increased pollution and poor diet, the number of individuals with skin disorders is on the rise at an alarming rate. People often overlook the early signs of skin illness. The current approach for diagnosing and treating skin conditions relies on a biopsy process examined and administered by physicians. Human assessment can be avoided with a hybrid technique, thus providing hopeful findings on time. Approaches to a thorough investigation indicate that deep learning methods might be used to construct frameworks capable of identifying diverse skin conditions. Skin and non-skin tissue must be distinguished to detect skin diseases. This research developed a skin disease classification system using MobileNetV2 and LSTM. For this system, accuracy in skin disease forecasting is the primary aim while ensuring excellent efficiency in storing complete state information for exact forecasts.

Performance Analysis of Linear Precoding in Downlink Based on Polynomial Expansion on Massive MIMO Systems

The performance of linear precoding schemes in downlink Massive MIMO systems is dealt with in this paper. Linear precoding schemes are incorporated with zero-forcing (ZF) and maximum ratio transmission (MRT), truncated polynomial expansion (TPE), regularized zero force (RZF) in Downlink massive MIMO systems. Massive MIMO downlink output is evaluated with linear precoding included. This paper expresses the performance of achievable sum-rate linear precoding with variable signal-to-noise (SNR) ratio and achievable sum rate and several transmitter-receiver antennas, such as imperfect CSI, less complex processing, and inter-user interference. The transmitter has complete state information on the channel. The information narrates how a signal propagates to the receiver from the transmitter and reflects, for example, the cumulative effect of distance scattering, fading, and power decay. They show that the performance analysis of two linear precoding techniques, i.e., Maximum Ratio Transmission (MRT) and Zero Forcing (ZF) for downlink mMIMO output network over a perfect chain. The results show the improved ZF precoding achievable sum rate compared to the MRT precoding schemes and compared the average achievable rate RZF and TPE.

Distributed Parameter State Estimation for the Gray–Scott Reaction-Diffusion Model

A constructive approach is provided for the reconstruction of stationary and non-stationary patterns in the one-dimensional Gray-Scott model, utilizing measurements of the system state at a finite number of locations. Relations between the parameters of the model and the density of the sensor locations are derived that ensure the exponential convergence of the estimated state to the original one. The designed observer is capable of tracking a variety of complex spatiotemporal behaviors and self-replicating patterns. The theoretical findings are illustrated in particular numerical case studies. The results of the paper can be used for the synchronization analysis of the master–slave configuration of two identical Gray–Scott models coupled via a finite number of spatial points and can also be exploited for the purposes of feedback control applications in which the complete state information is required.

Self-Learning Threshold-Based Load Balancing

We consider a large-scale service system where incoming tasks have to be instantaneously dispatched to one out of many parallel server pools. The user-perceived performance degrades with the number of concurrent tasks and the dispatcher aims at maximizing the overall quality of service by balancing the load through a simple threshold policy. We demonstrate that such a policy is optimal on the fluid and diffusion scales, while only involving a small communication overhead, which is crucial for large-scale deployments. In order to set the threshold optimally, it is important, however, to learn the load of the system, which may be unknown. For that purpose, we design a control rule for tuning the threshold in an online manner. We derive conditions that guarantee that this adaptive threshold settles at the optimal value, along with estimates for the time until this happens. In addition, we provide numerical experiments that support the theoretical results and further indicate that our policy copes effectively with time-varying demand patterns. Summary of Contribution: Data centers and cloud computing platforms are the digital factories of the world, and managing resources and workloads in these systems involves operations research challenges of an unprecedented scale. Due to the massive size, complex dynamics, and wide range of time scales, the design and implementation of optimal resource-allocation strategies is prohibitively demanding from a computation and communication perspective. These resource-allocation strategies are essential for certain interactive applications, for which the available computing resources need to be distributed optimally among users in order to provide the best overall experienced performance. This is the subject of the present article, which considers the problem of distributing tasks among the various server pools of a large-scale service system, with the objective of optimizing the overall quality of service provided to users. A solution to this load-balancing problem cannot rely on maintaining complete state information at the gateway of the system, since this is computationally unfeasible, due to the magnitude and complexity of modern data centers and cloud computing platforms. Therefore, we examine a computationally light load-balancing algorithm that is yet asymptotically optimal in a regime where the size of the system approaches infinity. The analysis is based on a Markovian stochastic model, which is studied through fluid and diffusion limits in the aforementioned large-scale regime. The article analyzes the load-balancing algorithm theoretically and provides numerical experiments that support and extend the theoretical results.

State regulation in the field of energy conservation and energy efficiency

The article examines the regulatory aspects of energy conservation and energy efficiency, specific terminological tools and technological procedures. The authors provide a critical review of the policy in Russian Federation in the field of energy conservation and energy efficiency in goods realization; construction and operation of buildings, enterprises, facilities; and energy audits. The quality comparison analysis of state regulation in the field of energy conservation and energy efficiency in Russian Federation and Sweden is carried out. Based on comparison analysis, it has been established that Russian Federation has not achieved such progress as the EU countries. The idea is substantiated that an extensive internal energy supply, cold climate, and inefficient technologies lead to high energy intensity remaining inherent in every Russian economy sector. It is reflected that main problems are the lack of a unified methodology for conducting energy audits, the provision of qualified personnel, the construction of a model for regulating activities in the field of energy conservation and energy efficiency. It is concluded that mechanisms of state support for energy conservation and energy efficiency improvement, organizational and information support for implementation of goals and objectives of this program should include/add: provision of state debt to enterprises for projects implementation; creation a complete and understandable state information system (monitoring the situation); methodological and regulatory support for energy saving and energy efficiency improvement; investment, co-financing of the best regional projects (provision of subsidies); training and advanced training of persons responsible for the growth of energy efficiency (executive power, budgetary and commercial organizations, population); motivating the formation of a lean and economic model of citizens’ behavior.

A New Adaptive Explicit Nonlinear Model Predictive Control Design for a Nonlinear MIMO System: An Application to Twin Rotor MIMO System

This paper proposed an adaptive explicit nonlinear model predictive control (AENMPC) technique using multiple estimation models with a convex combination framework [18] for a class of nonlinear MIMO systems. Here, the explicit solution for the control signal is obtained from an optimal performance index which can be formulated without online optimization. In this work, a closed-form control law is developed by approximating the tracking error in the receding horizon by its Taylor series expansion. The control performance of any model-based control technologies explicitly depends on the quality of the unknown system parameters hence an adaptive parameter estimator is used to estimate the system parameter online [16,17]. To ensure the boundedness of the estimated parameter within a predefined compact region, a projection based adaptive law is used [43]. Using an aerodynamic laboratory set-up, known as the twin-rotor multi-input multi-output system (TRMS), the effectiveness of the proposed control algorithm has been verified. The complete state information of the system to the proposed adaptive controller is given from an extended Kalman filter based state observer. The performance of the proposed adaptive control algorithm has been verified successfully in simulations as well as real-time experimental setup of the TRMS model and compared with an existing control approach.

Survey of Optimal Control and Model Predictive Control with State Estimation and a Real Time Application

Abstract The optimal control and its limited version namely the model predictive control represent one of the most important nonlinear control alternatives nowadays. The success of them are also proven in many practical applications. These can provide for several industrial applications the optimal trajectory calculation as well as calculation of the real-time control signal. One successful version of this is Generalized Predictive Control (GPC). A big advantage of these control algorithms is that they solutions are able to take into account the limitations of the inputs, and the states. In some cases, it is important to know the mathematical model chosen and the complete state information. Otherwise, the model can be estimated during the operation. Our study shows through the control of the cathode heating of a high-power electron beam device the self-tuning adaptive control thus constructed. Using a suitable dynamic model and an extended Kalman estimator, we determine the estimated temperature of the two cathodes during operation and the saturation electron current, which ensures the maximum cathode life. The practical application was tested on a CTW 5/60 type electron gun.