Distributed Control Scheme Research Articles

Distributed control strategy of central heating system based on multi-agent consensus

Distributed control strategy of central heating system based on multi-agent consensus

A robust distributed model predictive control strategy for Leader–Follower formation control of multiple perturbed wheeled mobile robotics

This article studies the formation and trajectory tracking control of multiple mobile robots with kinematic sub-systems. We proposed an effective design of robust distributed model predictive control (MPC) strategy for Leader–Follower formation control scheme in a group of multiple perturbed Wheeled Mobile Robotics (WMRs) with the consideration of tracking performance in not only the position but also the orientation, as well as the distance between the center and head in each WMR. Furthermore, the relation between formation control objective and non-holonomic property in each agent is also discussed. For the purpose of achieving the desired formation, according to trajectory of leader WMR, the barycentric of the formation requirement is known as corresponding virtual followers, and a distributed tube-MPC scheme is applied to each follower WMR for tracking a reference trajectory with not only the position but also its orientation. In addition, the stability and the performance tracking of multiple perturbed WMRs are investigated by employing Lyapunov stability theory with the indirect comparison method to be implemented by pointing out precisely the proposed terminal controller and the equivalent terminal region. Comprehensive simulation results in several scenarios demonstrate the validity of the proposed control scheme.

Distributed optimal consensus control for discrete-time multi-agent systems with composite switching topologies via an iterative Q-learning method

This paper aims to achieve distributed optimal consensus control for discrete-time multi-agent systems (MASs) under composite switching topologies through utilising the iterative Q-learning approach. First, the optimal consensus control strategy, independent of system dynamics information, is developed by using the designed iterative Q-learning approach to guarantee that all follower states can synchronise to the desired trajectory formulated by the leader. Compared with the invariable algebraic or edge-fixed topology structures, the designed Q-learning control algorithm in this paper is implemented based on the composite switching topologies consisting of periodic and stochastic mechanisms. Then, an error dynamic system is established, which transforms the optimal consensus control issue into an optimal regulation issue and removes the influence of the two types of switching topologies. To conquer the problems of lack of model information and the inaccessible analytical solution of Hamilton-Jacobi-Isaacs (HJI) equation, an iterative Q-learning approach is designed according to the reconstructed Q-function Bellman equation. Through further iterative learning, the optimal control strategy can be acquired so as to realise consensus control and ensure the stability of the closed-loop systems. Finally, two simulation examples are provided to demonstrate the effectiveness of the proposed distributed optimal control scheme.

Function Placement for In-network Federated Learning

Federated learning (FL), particularly when data is distributed across multiple clients, helps reducing the learning time by avoiding training on a massive pile-up of data. Nonetheless, low computation capacities or poor network conditions can worsen the convergence time, therefore decreasing accuracy and learning performance. In this paper, we propose a framework to deploy FL clients in a network, while compensating end-to-end time variation due to heterogeneous network setting. We present a new distributed learning control scheme, named In-network Federated Learning Control (IFLC), to support the operations of distributed federated learning functions in geographically distributed networks, and designed to mitigate the stragglers with lower deployment costs. IFLC adapts the allocation of distributed hardware accelerators to modulate the importance of local training latency in the end-to-end delay of federated learning applications, considering both deterministic and stochastic delay scenarios. By extensive simulation on realistic instances of an in-network anomaly detection application, we show that the absence of hardware accelerators can strongly impair the learning efficiency. Additionally, we show that providing hardware accelerators at only 50% of the nodes, can reduce the number of stragglers by at least 50% and up to 100% with respect to a baseline FIRST-FIT algorithm, while also lowering the deployment cost by up to 30% with respect to the case without hardware accelerators. Finally, we explore the effect of topology changes on IFLC across both hierarchical and flat topologies.

Coordinated distributed model predictive control for multi energy carrier systems

Introducing Energy hubs (EHs) is a beneficial strategy for incorporating quickly expanding renewable energies. However, the stochastic nature of renewable energy sources (RESs) and fluctuating energy demand have produced a number of difficulties, including unstable voltage/frequency, challenging energy management, and difficult network interaction. Additionally, the changing in response time of electrical and heat demands will make control challenging. This paper proposes a distributed control system for use with dynamic EHs. The RESs and loads present in the multi-carrier system cause the dynamics considering here. In order to optimise system performance, this research suggests a distributed model predictive control strategy that considers expected behaviour and operational restrictions. The strategy’s potential is demonstrated via simulations in which the proposed scheme is applied to a benchmark system.

Robust Distributed Load Frequency Control for Multi-Area Power Systems with Photovoltaic and Battery Energy Storage System

The intermittent power generation of renewable energy sources (RESs) interrupts the balance between power generation and demand load due to the increased frequency fluctuation, which challenges the frequency stability analysis and control synthesis of power generation systems. This paper proposes a robust distributed load frequency control (DLFC) scheme for multi-area power systems. Firstly, a multi-area power system is constructed by integrating photovoltaic (PV) and battery energy storage systems (BESSs). Then, by employing the linear matrix inequality (LMI) technique, the sufficient condition capable of ensuring that the proposed controller satisfies H∞ robust performance in the sense of asymptotic stability is derived. Finally, testing is conducted on a four-area renewable power system, and results verify the strong robustness of the proposed controller against load disturbance and intermittence of RESs.

Distributed model predictive control for asynchronous multiagent systems with self‐triggered coordinator

AbstractThis paper investigates a distributed model predictive control strategy for asynchronous nonlinear multiagent systems (MASs) with external disturbances via self‐triggered generators and prediction horizon regulators. First, a shrinking constraint related to the error between the actual state and the predicted state is introduced into the optimal control problem to enhance the robustness of the system. Then, the triggering interval and the corresponding prediction horizon are determined by altering the expression of the Lyapunov function, thus achieving a trade‐off between control performance and energy loss. By implementing the proposed algorithm, the coordination objective of the MASs is achieved under asynchronous communication. Finally, the recursive feasibility and closed‐loop stability are proven successively. An illustrative example is conducted to demonstrate the merits of the presented approach.

Distributed model predictive control for consensus of nonlinear systems via parametric sensitivity

To handle the nonlinear consensus problem, a distributed model predictive control (DMPC) scheme is developed via parametric sensitivity. A two-stage input computation strategy is adopted for enhancing optimization efficiency. In the background stage, each agent first establishes its next-step optimization problem based on communication topology, and then performs distributed optimization to calculate the future inputs. In the online stage, all the agents build their sensitivity equations based on new information. Three variants of sensitivity equation are developed based on the level of communication load capacity, and the corresponding computation strategies are proposed. After solution, the background inputs are corrected and implemented. The optimality and robustness of the proposed algorithm are rigorously derived. Finally, the superiority of this DMPC scheme is demonstrated in the multi-vehicle system with two different topologies.

Distributed Autonomous Economic Control Strategy for the AC/DC Interconnected Microgrid Considering the Regulation Boundary of the Consensus Variable

ABSTRACTThe autonomous economic control for the islanded AC/DC interconnected microgrid has various modes due to the restriction of the feasible regions of inner and inter‐microgrid resources. To properly handle the cooperative relationship between resources with different regulation characteristics and realize the frequency, voltage stability, and economic operation of the AC/DC interconnected microgrid, a distributed control strategy considering the regulation boundary of the consensus variable is proposed. Firstly, the influence of the variable feasible regions on the consensus control objective is analyzed, and the fundamental principle of the consensus control algorithm considering the variable regulation boundary is expounded. Secondly, the trilevel control strategy consisting of the local equipment control and inner and inter‐microgrid cooperative control is constructed. The local equipment control adopts droop control, and the cooperative control adopts consensus control to realize the elimination of the frequency and voltage deviation and the economic dispatch of active power within and between microgrids. Finally, the AC/DC interconnected microgrid model is built based on PSCAD/EMTDC, and the simulation results verify the effectiveness of the proposed control strategy.

Resilient distributed secondary control for islanded AC microgrids under FDI attacks

For islanded AC microgrids under bounded and time-varying false data injection (FDI) attacks on actuators and communication links, a resilient distributed secondary control strategy is proposed to stabilize frequency and voltage at the reference values, and to achieve the optimal active power sharing. To mitigate the impact of FDI attacks on the stability of the islanded microgrid, extended-state observers are introduced to estimate the impact. Compared with the currently presented resilient distributed control strategies, which ensure the bounded stability of microgrids, the control objective that the synchronization of every distributed generator’s voltage and frequency to the reference values and the achievement of the optimal active power sharing is maintained by the proposed resilient control strategy. An example of an islanded AC microgrid is given to verify the proposed method.

МОДЕЛИРОВАНИЕ РЕЖИМОВ РАСПРЕДЕЛЕНИЯ ВОДНЫХ РЕСУРСОВ МЕЛИОРАТИВНЫХ СИСТЕМ ДВОЙНОГО РЕГУЛИРОВАНИЯ

The purpose of the research was to develop an algorithm for modeling water supply modes in the control loop "System section" in areas of reclamation systems of dual regulation for further development of software or information tools. The object of research was the pro-cess of water distribution on reclamation systems of dual regulation of the water regime. The information core of the work consisted of provisions on the rules of water distribution on rec-lamation systems and information technologies for automated systems, methods of processing and systematization of information. For modeling, a technique is applied with the allocation of control circuits for which standard modeling tools and algorithms are used. In the course of the research, a simulated water distribution control scheme was compiled on the reclamation systems of dual regulation with a regulating capacity in the control loop «System Section». Modeling functions are defined, such as calculation of water supply modes, modeling of wa-ter intake, transportation and supply modes, calculation of values of regulated levels and their modeling, as well as modeling of water discharge. Based on this and the determination of the initial information for modeling, an algorithm for modeling the distribution of water resources on the reclamation systems of dual regulation site was developed. The task of modeling in this "System section" circuit is to establish the modes of water supply from a regulating tank or a water source receiver by several groups of regulation «A group of water consumers». The de-veloped algorithm includes three stages: preparation for modeling, the modeling of water dis-tribution itself and the maintenance of the results of modeling results

Real-Time Power Regulation of Flexible User-Side Resources in Distribution Networks via Dual Ascent Method

Flexible user-side resources are of great potential in providing power regulation so as to effectively address the challenges of reverse power flow and overvoltage issues in distribution networks characterized by high photovoltaic (PV) penetration. However, existing distributed algorithms typically implement control signals after the convergence of the algorithms, making it difficult to track frequent and rapid fluctuations in PV power outputs in real time. Given this background, an online-distributed control algorithm for the real-time power regulation of flexible user-side resources is proposed in this paper. The objective of the established control model is to minimize network losses by dynamically adjusting active power outputs of flexible user-side resources and reactive power outputs of PV inverters while respecting branch power flow and voltage magnitude constraints. Furthermore, by deconstructing the centralized problem into a primal–dual one, a distributed control strategy based on the dual ascent method is implemented. With the proposed method, agents can achieve global optimality by exchanging limited information with their neighbors. The simulation results verify the good balance between economic efficiency and voltage control performance of the proposed method.

Equalized Distributed Control Strategy for AC Microgrid Energy Storage SOCs

Equalized Distributed Control Strategy for AC Microgrid Energy Storage SOCs

Resilient Neuroadaptive Distributed Fixed-Time Attitude Coordination Control for Multiple Spacecraft.

This work studies the attitude coordination tracking problem for multiple spacecraft with consideration of unintended faults (communication link faults and actuator faults), inertial uncertainties, and external disturbances under a directed communication graph. A resilient neuroadaptive distributed fixed-time control scheme is investigated to solve this challenging problem. First, an improved adaptive distributed observer is established for followers to estimate the states of the leader when considering communication link faults. The proposed observer improves the resilience against communication link faults. Subsequently, to further cope with the problem of actuator faults, inertial uncertainties, and external disturbances, based on the proposed observer and the technique of adding a power integrator, a neuroadaptive distributed fixed-time attitude coordination controller is developed. Unlike the existing controllers, the proposed one requires less information when dealing with faults and lumped uncertainties, and has a lower-computational cost. Moreover, the fixed-time stability of the closed-loop system is ensured under the designed resilient neuroadaptive distributed control scheme. Finally, comparative simulations are carried out to manifest the effectiveness of the investigated coordination control method.

Privacy-preserving distributed frequency control for AC microgrids with constrained communication

Distributed control of AC microgrids is one of the most popular methods in the islanded operation mode. Whereas, most of the existing studies either do not consider the potential threat of privacy security or rely on the assumption of ideal communication networks. To this end, this paper presents a novel privacy-preserving distributed secondary frequency control strategy for the privacy protection problem of an islanded AC microgrid with constrained communication. The key contributions of this paper are threefold. (1) Different from the existing privacy-preserving approaches used in AC microgrids, a time-varying function is introduced to mask interactive information such that the frequency cannot be reconstructed by malicious attackers. (2) An event-triggered communication scheme is employed to cope with the constrained communication environment. (3) A privacy-preserving distributed event-triggered control strategy with communication delay is developed such that the frequency restoration and active power sharing of the microgrid are guaranteed. Moreover, the maximum communication delay that the proposed control can withstand is analyzed. Simulation results show the properties of the privacy preservation, the decrease of communication load, and the bounded communication delay allowed in the proposed control strategy.

A Novel VSG with Adaptive Virtual Inertia and Adaptive Damping Coefficient to Improve Transient Frequency Response of Microgrids

This paper proposes a combined adaptive virtual Inertia and adaptive damping control of a virtual synchronous generator (AID-VSG) to improve the dynamic frequency response of microgrids. In the proposed control scheme, the VSG’s virtual inertia and damping coefficients adapt themselves during the transients to, respectively, reduce frequency deviations and increase the oscillations’ damping. In addition, as an important feature, the proposed AID-VSG is suitable for distributed control scheme applications and is designed to not rely on phase-locked loop (PLL) measurements, which avoids PLL stability issues on weak grids. The control parameters of the proposed AID-VSG are tuned by the particle swarm optimization (PSO) algorithm to minimize the overshoot and settling time of the microgrid’s frequency during an islanding event. The AID-VSG is validated by a comparative analysis with three existing VSG control schemes, also tuned by the stated optimization algorithm. The performance of each compared VSG strategy is evaluated through the simulation of a set of 10,000 initial conditions, using the islanded microgrid’s nonlinear model. The best response among the VSG strategies was achieved by the proposed AID-VSG control for both the optimization problem and the set of initial conditions’ simulations.

The Distributed Adaptive Bipartite Consensus Tracking Control of Networked Euler–Lagrange Systems with an Application to Quadrotor Drone Groups

Actuator faults and external disturbances, which are inevitable due to material fatigue, operational wear and tear, and unforeseen environmental impacts, cause significant threats to the control reliability and performance of networked systems. Therefore, this paper primarily focuses on the distributed adaptive bipartite consensus tracking control problem of networked Euler–Lagrange systems (ELSs) subject to actuator faults and external disturbances. A robust distributed control scheme is developed by combining the adaptive distributed observer and neural-network-based tracking controller. On the one hand, a new positive definite diagonal matrix associated with an asymmetric Laplacian matrix is constructed in the distributed observer, which can be used to estimate the leader’s information. On the other hand, neural networks are adopted to approximate the lumped uncertainties composed of unknown matrices and external disturbances in the follower model. The adaptive update laws are designed for the unknown parameters in neural networks and the actuator fault factors to ensure the boundedness of estimation errors. Finally, the proposed control scheme’s effectiveness is validated through numerical simulations using two types of typical ELS models: two-link robot manipulators and quadrotor drones. The simulation results demonstrate the robustness and reliability of the proposed control approach in the presence of actuator faults and external disturbances.

Distributed fixed-time formation tracking control for multiple underactuated USVs with lumped uncertainties and input saturation

A fixed-time distributed formation control strategy is investigated for multiple underactuated unmanned surface vehicles (USVs) with unmeasured velocities and input saturation. Initially, a necessary coordinate transformation is applied to the mathematical model of USVs to address the underactuated issue. Subsequently, a fixed-time extended state observer (FESO) is constructed to estimate unmeasured velocities and lumped disturbances of USVs based on input and output data in the control loop. Meanwhile, the leader–follower approach is applied to achieve a preset formation. A fixed-time differentiator is utilized to compute real-time differential signals for virtual control laws, which simplifies the complexity of controller design. Furthermore, a fixed-time distributed formation controller is designed based on an asymmetric differentiable saturation model. The effects of input saturation are eliminated by a designed auxiliary system. Finally, the fixed-time stability of the closed-loop system is analyzed through the Lyapunov stability theory. The comparison simulation results verify the effectiveness and superiority of the proposed formation control scheme.

Fixed-time consensus problem for uncertain multi-agent systems with continuous and intermittent control

This paper addresses the fixed-time consensus issue in uncertain multi-agent systems (MAS). It introduces novel estimation methods for the maximum settling time, which considers all controller components but may also have less conservatism than traditional methods. Based on these results, a distributed control scheme and the corresponding consensus criterion are proposed, enabling MAS to achieve a fixed-time consensus and provide a more accurate estimation of the maximum settling time than the traditional method. Additionally, a distributed intermittent control scheme is also provided to enable the MAS to solve the issue of practical fixed-time consensus and be controlled only in a series of disjoint periods. Moreover, considering the case where the MAS has no external disturbance, another distributed intermittent control scheme is introduced to achieve fixed-time consensus under a directed topology. Finally, the results are supported by numerical examples.

Distributed delay control strategy for leader-following cyber secure consensus in Riemann–Liouville fractional-order delayed multi-agent systems under denial-of-service attacks

Considered distributed delay control strategy for Leader-Following cyber secure consensus in Riemann–Liouville fractional-order delayed Multi-Agent system under Denial-of-Service Attacks, where the associated topology is a switching weighted digraph. By using the classical Lyapunov technique and algebraic graph theory some algebraic criteria are offered. The new aspect of this paper is how thoroughly it discusses denial-of-service (DoS) attacks in the context of fractional-order delayed systems and how to create defences to keep cybersecure consensus maintained. It suggests a distributed delay control approach that ensures agents can reach a consensus even with these difficulties and in the case of DoS (denial-of-service) attacks. This work is significant because it incorporates cybersecurity techniques to strengthen the system’s resilience against disruptive assaults, in addition to addressing the technical issues related to delays and fractional-order behaviours. This contribution is essential for enhancing the security and dependability of distributed systems in applications such as networked sensors and autonomous vehicles. To further demonstrate the applicability of our method, two instances are given.