Decentralized Control Research Articles

Stochastic Plantwide Optimizing Control for an Acrylic Acid Plant

This work addresses the design of an optimized control system for an acrylic acid plant through the lens of the Stochastic Plant-Wide Optimizing Control (S-PWOC) framework. The S-PWOC employs stochastic optimization methods and advanced computer modeling to optimize plant performance by dynamically adjusting operational parameters under varying uncertainties. A comparison between the proposed S-PWOC model and two conventional approaches, the two-level identification method and the typical plant-wide decentralized control structure, highlights the advantages of S-PWOC despite its higher computational demands. Experimental results demonstrate significant improvements, including a 15% increase in process efficiency, a 10% reduction in energy consumption, enhanced product quality consistency, and greater economic viability. Additionally, S-PWOC proves effective in reducing safety risks and improving control efficiency, making it a robust solution for handling uncertainties in real-world plant operations.

Blockchain empowered dynamic access control for secure data sharing in collaborative emergency management

In the realm of emergency management involving multi-party collaboration, securely sharing data among multiple entities poses a significant challenge. Traditional centralized data platforms struggle with fine-grained access control for data from different sources and are susceptible to single-point failures and risks of forged authorization. This paper delves into the prospective application of blockchain technology for decentralized data governance. We propose a dynamic access control technology for multi-party collaborative emergency response, which combines blockchain and attribute-based access control technologies to enable dynamic access control in multi-party data sharing scenarios. A data sharing system for collaborative emergency management built with Hyperledger Fabric has been developed and subjected to a series of evaluation tests. The experimental results reveal that our blockchain-based system not only significantly enhances data security and privacy by leveraging decentralized control but also improves the efficiency and reliability of data sharing in emergency situations. Specifically, our findings demonstrate the system's ability to dynamically adapt access control policies based on changing emergency scenarios, effectively mitigating risks associated with single-point failures and unauthorized access. These outcomes underscore the viability and reliability of our approach, providing a robust framework for secure, scalable, and flexible data sharing in the demanding field of emergency management.

Observer-based self-triggered adaptive decentralized control for uncertain interconnected nonlinear systems

In this paper, an observer-based self-triggered adaptive decentralized control method is presented for uncertain interconnected nonlinear systems (UINS) with unmeasured states. First, an adaptive state observer is constructed to estimate unmeasured system states. Then, the observer-based self-triggered controller is developed by applying the famous backstepping technique, in which the controller signal is updated based on the well-designed self-triggered protocol. Moreover, parameter adaptive laws are designed to address unknown parameters of UINS. Lyapunov theory is employed to prove that all signals of the UINS are semi-globally uniformly ultimately bounded (SGUUB) and the Zeno behavior will not appear. Simulation results manifest the efficacy of the derived self-triggered control solution.

Blockchain-based UAV Continuous Authentication

With the growing utilization of unmanned aerial vehicles (UAVs), low-altitude airspace is becoming increasingly congested, posing significant challenges to Air Traffic Control (ATC). The Automatic Dependent Surveillance-Broadcast (ADS-B) system is used to report the location of UAVs. However, due to channel noise and adversarial interference, ADS-B data may be inaccurate. To address this issue, a position estimation method based on Bidirectional Long Short-Term Memory Multi-Layer Perceptron (BiLSTM-MLP) was proposed. In this method, ADS-B signal power is used to estimate the position of UAVs as a baseline approach. Experimental results demonstrate that the BiLSTM-MLP based method significantly improves estimation accuracy. Moreover, a blockchain-based dynamic reputation mechanism, aligned with the position estimation method, was proposed. The mechanism integrates information from the third-party UAV and the location data provided by the UAV itself to dynamically update the credibility of the UAV, and thus achieves decentralized access control of UAVs in low-altitude airspace.

Adaptive Fuzzy Resilient Decentralized Control for Nonlinear Large-Scale CPSs Under DoS Attacks

Adaptive Fuzzy Resilient Decentralized Control for Nonlinear Large-Scale CPSs Under DoS Attacks

A decentralized control approach in hypergraph distributed optimization decomposition cases

We study the main decomposition approaches (primal, dual and primal–dual) for a distributed optimization problem from a dynamical system perspective where the couplings among the variables of the optimization problem are described by an undirected, unweighted hypergraph. We conduct stability analysis for the respective dynamical systems of the decomposition cases by using non linear decentralized control theoretic techniques and spectral properties of the respective communication matrices, i.e., the incidence and the Laplacian matrices of the hypergraph. Finally, we provide numerical simulations under a specific coalitional setting that demonstrate the superiority of the hypergraph compared to its respective graph analogue, the clique expansion graph, for the given decomposition algorithms in terms of convergence rate and information transmission efficiency.

Reinforcement Learning Control of Hypersonic Vehicles and Performance Evaluations

This work presents a new framework for model-based continuous-time reinforcement learning (CT-RL) control of hypersonic vehicles (HSVs). The predominant classes of CT-RL methods for general nonlinear systems in adaptive dynamic programming (ADP) and deep RL tend to either present substantial theoretical results but lack practical synthesis capability (ADP) or show empirical promise without offering theoretical guarantees (deep RL). Meanwhile, RL control frameworks developed directly for HSVs tend to require a simplified model and complicated control structure, and they lack the substantial numerical evaluations essential for real-world flight implementation. To directly address these challenges, we propose a new decentralized excitable integral reinforcement learning framework within which the reference input-based exploration improves persistence of excitation. Together with new insights on prescaling and established decentralized control structure for HSVs, we demonstrate the resulting controller for significant performance improvement over classical Linear Quadratic Regulator (LQR) and feedback linearization methods. Additionally, we provide convergence, optimality, and closed-loop stability guarantees of the proposed method. We demonstrate these performance guarantees over a set of substantial and systematic numerical evaluations on an unstable, nonminimum phase HSV model subject to varying modeling errors and initial conditions.

A modified droop-based decentralized control strategy for accurate power sharing in a PV-based islanded AC microgrid

This paper introduces a novel droop-based decentralized control scheme to address the power-sharing challenges within a PV-fed islanded AC microgrid. This novel approach integrates both conventional (P-f/Q-V) and virtual impedance concepts to optimize and manage the precise distribution of active and reactive power among parallel operating inverters posing a significant research challenge. The conventional droop control methods encounter limitations such as voltage and frequency deviations and inaccuracies in power-sharing due to line impedance disparities. To overcome these limitations, the proposed solution integrates an enhanced virtual impedance control loop alongside the conventional control loop (P-f/Q-V). The efficacy of this approach is showcased through simulations conducted using the OPAL-RT OP4510 simulator within the MATLAB/Simulink platform. The Real-time simulation outcomes confirm the efficiency of the suggested control strategy, guaranteeing precise distribution of both active and reactive power while upholding stable voltage and frequency profiles within the system.

A wild horse-assisted decentralized control strategy for a PV-battery energy storage system in a DC microgrid

A wild horse-assisted decentralized control strategy for a PV-battery energy storage system in a DC microgrid

Simultaneous Policy Iteration for Decentralized Control of Multi-Machine Power Systems

Simultaneous Policy Iteration for Decentralized Control of Multi-Machine Power Systems

A modular decentralized control system of multi-connected processes in the presence of structural disturbances

The object of research is digital systems of decentralized control of quasi-stationary multi-connected processes taking into account structural disturbances. It was determined that the promising development of decentralized control of multi-connected processes is research related to considering the possibility of its application for real situations of partial disruption of interconnections between subsystems in multidimensional stochastic systems. Such studies are not sufficiently developed today, therefore the topic of the proposed work should be considered relevant from the theoretical and applied points of view. The problem of decentralized management of multi-connected stochastic objects is formalized, taking into account the internal relationships between their local subsystems of the system. A method of decentralization of a multi-connected system is proposed, the advantage of which is the use of simple computational procedures of transformation and permutation of matrix elements of the global model. The concept of c-robustness of a multi-connected dynamic system is introduced, which guarantees its asymptotic stability and suboptimality in the event of structural disturbances. Methods of robust decentralized control are proposed for cases of explicit and implicit tasks of interconnections between local subsystems. The proposed methods have a certain advantage in comparison with the method of local control coordination, which consists in increasing the efficiency of decentralized control with a guaranteed level of suboptimality. Experimental modeling of the considered method for models of multi-connected chemical-technological systems describing the relationship between the input and output parameters of the processes was carried out. During the simulation, the global system was decomposed into 4 local subsystems. Modeling of the task of decentralized management was also carried out according to the well-known method of coordination, based on finding the values of uncertain multipliers. A comparative analysis of the results obtained using this standard method and the proposed robust methods shows the superiority of the latter.

Decentralized control of forward and backward stochastic difference system with nested asymmetric information

This paper is concerned with the linear quadratic decentralized control of forward and backward stochastic difference system (FBSDS) with multiplicative noises and nested asymmetric information. The main contribution is to give the equivalent condition for the solvability of the problem and the explicit decentralized optimal controllers in terms of Riccati equations. The key technology is solving the forward and backward stochastic difference equations with partial information derived from stochastic maximum principle.

A multi-point decentralized control for mitigating vibration of flexible space structures using reaction wheel actuators

The vibration with large amplitude and low frequency of the flexible space structures is prone to affect the attitude stability and pointing precision of the spacecraft. To mitigate the vibration of the flexible space structures, a multi-point decentralized control strategy using reaction wheel (RW) actuators is proposed and investigated in this paper. The motion equations of the solar array with multiple RW actuators are derived in modal coordinate representation. To suppress the overall response of the structure, the decentralized control strategy using RW actuators is designed based on the natural frequencies and mode shapes. The stability and the effect of closed-loop dynamic system is theoretically proved. The comparative studies under sun-pointing of the solar array and the rest-to-rest orbital maneuver conditions are presented to show the control performance of the RW actuators. The results indicate that, with 2% increase in total mass from the addition of the actuators, the vibration attenuation time can be decreased by 85.25% and 94.16% for the vibration excited by the sun-pointing and the rest-to-rest orbital maneuver, respectively. The experimental results demonstrate the effectiveness of the proposed decentralized control method. Theoretical analysis, numerical simulation and experimental study are conducted to demonstrate the validity of the proposed vibration mitigation approach and its potential application in the spacecraft design.

Distributed Localization for UAV–UGV Cooperative Systems Using Information Consensus Filter

In the evolving landscape of autonomous systems, the integration of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) has emerged as a promising solution for improving the localization accuracy and operational efficiency for diverse applications. This study introduces an Information Consensus Filter (ICF)-based decentralized control system for UAVs, incorporating the Control Barrier Function–Control Lyapunov Function (CBF–CLF) strategy aimed at enhancing operational safety and efficiency. At the core of our approach lies an ICF-based decentralized control algorithm that allows UAVs to autonomously adjust their flight controls in real time based on inter-UAV communication. This facilitates cohesive movement operation, significantly improving the system resilience and adaptability. Meanwhile, the UAV is equipped with a visual recognition system designed for tracking and locating the UGV. According to the experiments proposed in the paper, the precision of this visual recognition system correlates significantly with the operational distance. The proposed CBF–CLF strategy dynamically adjusts the control inputs to maintain safe distances between the UAV and UGV, thereby enhancing the accuracy of the visual system. The effectiveness and robustness of the proposed system are demonstrated through extensive simulations and experiments, highlighting its potential for widespread application in UAV operational domains.

Multi-agent deep reinforcement learning for joint dynamic conservation voltage reduction and Q-sharing in inverter-based autonomous microgrids

Conservation voltage reduction (CVR) is implemented in power systems to mitigate power consumption in steady-state time scales. We propose dynamic-scale CVR (DCVR) as a potent solution to provide cost-effective frequency support in inverter-interfaced (micro) grids. The proposed DCVR reduces the voltage profile in dynamics and consequently power reduction helps to maintain instant production-consumption balance for dynamic frequency support. However, DCVR implementation in autonomous MGs (AMGs) faces critical control and stability problems. DCVR is implemented by controlling the voltage, which is a local variable, and makes it difficult to be realized through a decentralized structure. Besides, preserving accurate reactive power sharing (Q-sharing) through conventional droop controllers while employing the DCVR is another critical concern. In this light, this paper proposes a novel artificial intelligence (AI)-based decentralized control structure to implement the DCVR in AMGs for tackling the existing issues. The multi-agent deep reinforcement learning (DRL) model, with a deep Q-network (DQN) algorithm, is adopted to address the grid instabilities and inaccurate Q-sharing issues that arise due to incorporating the DCVR. The proposed method is able to handle the nonlinearity and complexity of the system while maintaining proper dynamic performance and AMG stability. Simulation results in MATLAB/Simulink prove the effectiveness of the proposed control method.

Agent-Based Model of Initial Token Allocations: Simulating Distributions post Fair Launch

With advancements in distributed ledger technologies and smart contracts, tokenized voting rights gained prominence within decentralized finance (DeFi). Voting rights tokens (a.k.a. governance tokens) are fungible tokens that grant individual holders the right to vote upon the fate of a project. The motivation behind these tokens is to achieve decentral control within a decentralized autonomous organization (DAO). Because the initial allocations of these tokens is often undemocratic, the DeFi project and DAO of Yearn Finance experimented with a fair launch allocation where no tokens are pre-mined and all participants have an equal opportunity to receive them. Regardless, research on voting rights tokens highlights the formation of timocracies over time. The consideration is that the tokens’ tradability is the cause of concentration. To examine this proposition, this article uses an agent-based model to simulate and analyze the concentration of voting rights tokens post three fair launch allocation scenarios under different trading modalities. The results show that regardless of the allocation, concentration persistently occurs. It confirms the consideration that the ‘disease’ is endogenous: the cause of concentration is the tokens’ tradability. The findings inform theoretical understandings and practical implications for on-chain governance mediated by tokens.

A Novel Decentralized Frequency Regulation Method of Renewable Energy Stations Based on Minimum Reserve Capacity for Renewable Energy-Dominated Power Systems

In this paper, we propose a novel decentralized frequency regulation method for renewable energy-dominated power systems. First, the system is modularized into unified frequency regulation modules composed of synchronous generators(SGs) and renewable energy stations. Then, based on the aggregation model of the module, a renewable energy station frequency regulation method based on robust control barrier functions(RCBFs) is proposed to ensure that the frequency deviation is limited, and an additional switching control strategy is complemented to avoid the long-term suspension of over-high quasi-steady-state frequency. The proposed method is still applicable under the coordination of multiple renewable energy stations. Each renewable energy station deploys decentralized control based on local information, which can support fast frequency response. The robustness of frequency response time constants of renewable energy stations and frequency dead zone are considered, which makes the control method easy to implement. A module transient frequency stability margin index based on reserve capacity is proposed, which can be used to guide the system planning and operation. Finally, simulation results validate the applicability of the proposed method, demonstrating its efficacy in regulating frequency in renewable energy-dominated power systems.

Transactive energy trading in distribution systems via privacy-preserving distributed coordination

This paper presents an approach to the transactive energy (TE) market that aims to improve the network efficiency and fairness of electricity market in distribution systems. The TE market is designed to allow subscribers to trade electricity in real-time based-on their energy consumption and generation forecast. The proposed framework utilizes distribution locational marginal pricing (DLMP), which accurately reflects the costs of electricity transmission and distribution, to fairly allocate these costs among subscribers. The Small Neighborhood Search Optimal Points (SNSOP) algorithm is then introduced to solve the challenge of finding optimal points for all participants in the TE market, as the maximum financial gain for one participant comes at the maximum financial loss of another participant. The results of the case study show that the proposed framework leads to a more efficient and reliable electricity network, by enabling decentralized control and reducing losses through optimal trades in the TE market.

Effectiveness of Information Transfer on Control Quality of CSMA/CA-Based Autonomous Decentralized Control

Effectiveness of Information Transfer on Control Quality of CSMA/CA-Based Autonomous Decentralized Control

Low computational cost convolutional neural network for smart grid frequency stability prediction

In the smart grid, it is critical to collect dynamic and time-dependent information on energy demand and consumption and compare it to current supply conditions. The decentral smart grid control (DSGC) system manages frequency, a Smart grid element. It connects energy costs to grid frequency, allowing access to both consumers and producers. This work proposes a pruning of the convolution layers and neurons of 1-dimensional time-aware convolutional neural network (1D CNN) analysis of grid frequency stability to determine efficient energy costs. The proposed solution evaluated augmented grid stability datasets in addition to two other publicly available datasets to ascertain the approach’s feasibility in various scenarios; the simulation demonstrated a minimal train and prediction time of 124.37 s and 17.67 s efficiency over compared models, with prediction accuracy of 99.79% and 0.01 MFLOPs. Matthew’s correlation coefficient was applied to evaluate further the performance of the proposed 1D CNN to ascertain its applicability in various scenarios.