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The availability of cyber-physical systems (CPS) in modern manufacturing networks provides a multitude of interesting opportunities from a manufacturing control perspective. Providing sensors, data gathering, local computation and communication capabilities modern CPS fulfil the technical requirements to act completely autonomously in a manufacturing network. While the distribution of decision-making authority to autonomous entities is feasible given such requirements, practice often sees the monopolisation of decision-making authority for centralised control. However, distributed production control approaches might be better suited given current manufacturing challenges, ranging from unreliable supply chains over highly volatile markets, to the demand for increasingly efficient and highly customisable production. In this article, we extend an existing scheduling complexity framework which enables practitioners and researchers alike to assess the aptitude of given manufacturing networks for both centralised and distributed control. In particular, we study the influence of a manufacturing network's topology ranging from assembly line to job shops on the aforementioned aptitude, with total production costs as objective. We utilise a multi-agent-based discrete-event simulation comparing an MILP-based centralised control approach and an autonomy based distributed control approach with weighted costs as decision function to evaluate this framework. Our results provide novel insights regarding the influence of manufacturing network topologies on the scheduling complexity of manufacturing networks. [ABSTRACT FROM AUTHOR]

ABSTRACT The 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. [ABSTRACT FROM AUTHOR]

For cooperation among distributed generations in a DC microgrid (MG), distributed control is widely applied. However, the delay in distributed communication will result in steady‐state bias and the risk of instability. This paper proposes a novel distributed control for time‐delayed DC MGs to achieve accurate current proportional sharing and weighted average voltage regulation. Firstly, by utilizing an advanced observer based on the PI consensus algorithm, the steady‐state bias problem is addressed. Then, using the passivity theory, stability analysis is conducted to reveal the principle of system instability caused by communication delay. On this basis, to offset the adverse effects of communication delay on the system stability, scattering transformation is introduced in the observer‐based distributed control. Moreover, considering the potential delay from the measurement stage in real‐life scenarios, the sufficient condition of the system stability is concluded by constructing the Lyapunov–Krasovskii functional. Finally, the performance of the proposed control and conclusions of stability analysis are verified by hardware‐in‐loop tests. [ABSTRACT FROM AUTHOR]

This article addresses the multiple symmetric task control issue of networked robot systems modeled by the Euler–Lagrange equation under a switching communication network topology. A distributed coordinated control protocol is presented with the aid of multiple virtual leaders. Based on the special structure of the network topology, some geometric convergence criteria under which the network can realize multiple symmetric consensus is given. Finally, two simulations conducted on seven two‐link revolute arms and eleven mass agents are proposed to illustrate our design, respectively. [ABSTRACT FROM AUTHOR]

The current paper presents a comprehensive review analysis of Multi-agent control methodologies for Integrated Building Energy Management Systems (IBEMSs), considering combinations of multi-diverse equipment such as Heating, Ventilation, and Air conditioning (HVAC), domestic hot water (DHW), lighting systems (LS), renewable energy sources (RES), energy storage systems (ESS) as well as electric vehicles (EVs), integrated at the building level. Grounded in the evaluation of key control methodologies—such as Model Predictive Control (MPC) and reinforcement learning (RL) along with their synergistic hybrid integration—the current study integrates a large number of impactful applications of the last decade and evaluates their contribution to the field of energy management in buildings. To this end, over seventy key scholarly papers from the 2014–2024 period have been integrated and analyzed to provide a holistic evaluation on different areas of interest, including the utilized algorithms, agent interactions, energy system types, building typologies, application types and simulation tools. Moreover, by analyzing the latest advancements in the field, a fruitful trend identification is conducted in the realm of multi-agent control for IBEMS frameworks, highlighting the most prominent solutions to achieve sustainability and energy efficiency. [ABSTRACT FROM AUTHOR]

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This paper studies the learning-based finite-time distributed formation control problem for a group of fixed-wing unmanned aerial vehicles (UAVs). Using position information obtained from sensors, a novel learning-based finite-time distributed controller with a saturation function is developed. Firstly, a six-degree-of-freedom fixed-wing UAV model is established and transformed into a double-integrator model using linear feedback linearization. For practical implementations, the velocities and overloads of the controlled fixed-wing UAVs are constrained within desirable limits by injecting appropriate saturations into the loops. The finite-time formation control objective is demonstrated if the connectivity topology meets the spanning tree criterion. The Lyapunov theory is used as a fundamental tool to guarantee the stability of the closed-loop system. Furthermore, an improved learning-based finite-time distributed control scheme is proposed to obtain the approximately optimal control laws for the error systems. Finally, two numerical examples are employed to verify the theoretical results. [ABSTRACT FROM AUTHOR]

This paper investigates region tracking and perimeter surveillance of second-order multiagent systems, where all agents move within a star-shaped set. First, by coordination transformations, the region tracking problem is converted from the star-shaped sets to a circular region. The authors employ communication and collaboration to complete region tracking and perimeter surveillance tasks, and then revert back to the star-shaped set by using inverse transformations. Second, the authors propose a distributed control strategy based on attractive and interaction potential functions, under which all agents can quickly track a given circular region and move around the perimeter. Finally, the authors validate the effectiveness and performance advantages of the proposed method through simulation experiments. [ABSTRACT FROM AUTHOR]

Adaptive control strategies have been developed in response to more advanced complex systems and to deal with uncertain systems while maintaining the desired conditions. This paper addresses the networked unknown and unstable heterogeneous systems following a stable reference (leader), which is related to network synchronization. We deliver two different scenarios; each agent both fully communicates to the leader and shares communication among neighborhood agents and the leader. The communication among agents and the leader are weighted using Laplacian-like matrix and the model weight matrix in turn. Also, the state and input delays are induced to the systems to capture the real limited communication while the prediction of the reference signals and the augmented systems are proposed to deal with them. Moreover, the rigorous mathematical foundations of two adaptive laws, the stability analysis, the threshold of network, and the communication network are thoroughly presented. Also, the numerical illustrations of the two scenarios are given to show the effectiveness of the proposed method in the networked system. The results show that for both scenarios working on the required setting, the perfect tracking to the leader is guaranteed. Beyond that, the future research would implement the distributed adaptive control-oriented learning of networked system under some faults. [ABSTRACT FROM AUTHOR]

The deployment of distributed energy storage on the demand side has significantly enhanced the flexibility of power systems. However, effectively controlling these large-scale and geographically dispersed energy storage devices remains a major challenge in demand-side management. In this paper, we propose a CPS-based framework for controlling a distributed energy storage aggregator (DESA) in demand-side management. Within this framework, a distributed power tracking control algorithm is designed to ensure both power tracking and state-of-charge (SoC) balancing among the energy storage units (ESUs) within the DESA. The proposed algorithm utilizes a distributed observation-based approach that relies solely on local communication. It is demonstrated that the algorithm achieves power tracking convergence within a fixed time, while asymptotically achieving SoC balancing when assuming a connected communication network among the storage units. To validate the theoretical analysis and demonstrate the effectiveness of the proposed control strategy, an example scenario comprising six ESUs is presented. [ABSTRACT FROM AUTHOR]

The formation obstacle avoidance control problem of multi-agent systems was studied for multiple anonymous agents in the two-dimensional plane. The goal is to make the anonymous agents randomly distributed in the plane complete the obstacle avoidance progressive convergence to the target circle within the specified time, and then complete the specified formation on the circle according to the formation control law. Based on the artificial potential field method, the function of avoiding obstacles was realized, and the problem that the agent cannot move in the local minimum area was solved by adding random disturbance. The Lyapunov function was introduced and the stability of the control law designed was proved by theorem. Finally, the effectiveness of the obstacle avoidance function and the accuracy of the control law were proved by simulation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This article addresses the issue of collaborative wildfire monitoring using a group mobile mecanum‐wheeled omnidirectional vehicles (MWOVs) affected by nonlinear uncertainties and external disturbances. By integrating finite‐time extended state observers (FTESO) and backstepping nonsingular fast terminal sliding mode (BNFTSM) control method, an observer‐based finite‐time time‐varying elliptical formation control scheme is proposed for a group of MWOVs tasked with monitoring the propagation of wildfires in an elliptical pattern. First, the FTESO is employed to estimate the unavailable velocity system states and the lumped disturbances. Then, a novel nonsingular fast terminal sliding surface, enhanced with an exponential term, is introduced to improve the convergence rate. Through the Lyapunov theorem, the convergence of position and velocity cooperative tracking errors to zero in fast finite‐time is demonstrated. To showcase the effectiveness of the proposed control scheme, comparative simulation results are presented. [ABSTRACT FROM AUTHOR]

This paper studies the prescribed-time relative motion control problem of spacecraft formation flying under input saturation. Using prescribed-time theory, a prescribed-time sliding mode is designed such that the states on the sliding mode converge to the equilibrium in the prescribed time. Based on the prescribed-time sliding mode, a prescribed-time relative motion tracking controller is developed, which guarantees fast formation maneuvers with feasible fuel consumption and strong robustness under input saturation. Furthermore, a simulation example is carried out to verify the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

Due to the widespread penetration of wind energy, large-capacity wind turbines (WTs) are becoming more conspicuous. In this paper, a linear control-oriented large-capacity wind turbine (WT) model is redesigned based on the 15 MW WT in openFAST. Compared with the original model, the redesigned model is more efficient and conducive to the design of linear controllers. To verify the validity of the redesigned WT model, we design a distributed control scheme with deloading operation of the wind farm (WF) to fairly share the load. Simulations are performed to verify the effectiveness of the application in the WF. [ABSTRACT FROM AUTHOR]

This paper proposes a distributed synchronization control method and an accelerated backstepping tracking control scheme for the multi-motor driving system (MMDS). In the first step, we create a dynamic model of the MMDS with complex nonlinear dynamics, encompassing elements such as the dead zone, frictions, and disturbances. Next, in order to tackle the challenge of load tracking, we fuse a speed function, a cosine barrier function, a second-order tracking differentiator (TD), and a disturbance compensator into the backstepping approach. Lastly, to address potential issues related to diverse torque inputs, which could result in the overload occurrences, we put forward a novel distributed synchronization control scheme. This scheme aims to achieve torque synchronization for the MMDS while simultaneously ensuring superior load tracking performance. In the distributed synchronization control, a communication network is built to achieve the local coupling and improve the synchronization efficiency, and a corresponding mean deviation coupling synchronization control scheme is designed. Lyapunov theory is utilized to demonstrate the stability of the introduced control scheme. The simulation experimental results for the MMDS show the effectiveness of the proposed scheme. • A dynamic model of the MMDS encompassing elements like the dead zone and frictions is built. • An accelerated backstepping tracking method is proposed to address load tracking concerns. • A distributed synchronization control scheme is designed for the large-scale complex MMDS. • Our scheme guarantees both the load tracking and synchronization performance of the MMDS. [ABSTRACT FROM AUTHOR]

This paper addresses the dynamic output feedback leader-following consensus control for a class of high-order stochastic multi-agent systems characterized by unknown time-varying delays. The agents are modeled as a class of stochastic strict feedback nonlinear systems with unknown time-varying delays. Additionally, the states of the agents are unknown for control design. To address these challenges, observers are designed firstly to estimate the unknown states of each agent. Subsequently, a distributed observer-based output feedback consensus protocol, relying solely on the outputs of neighboring agents, is introduced. It is shown that the followers can effectively track the leader's output with a 1st-moment exponential rate. The effectiveness of the proposed control scheme is validated through simulation examples. [ABSTRACT FROM AUTHOR]

This paper mainly investigates linear elastic optimal control problems with two constraints: distributed load control and boundary load control. The gradient of the objective functional is derived via an adjoint problem. We obtain the H 1 -regularity for the distributed control solution. Moreover, we establish a suitable finite element interpolation to deal with the non-optimal regularity of the boundary control solution. The error estimates for the fully discretized problems is given. Then, together with the interior point method, a new numerical method for the optimal control of linear elasticity is exhibited. Finally, the effectiveness of presented scheme is demonstrated by various numerical simulations in two and three-space dimensions. [ABSTRACT FROM AUTHOR]

Islanded DC microgrids are poised to become a crucial component in the advancement of smart energy systems. They achieve this by effectively and seamlessly integrating multiple renewable energy resources to meet specific load requirements through droop control, which ensures fair distribution of load current across the distributed energy resources (DERs). Employing droop control usually results in a DC bus voltage drop. This article introduces a secondary distributed control approach aimed at concurrently achieving current distribution among the DERs and regulating the voltage of the DC bus. The proposed secondary control approach eradicates voltage fluctuations and guarantees equitable current allocation by integrating voltage and current errors within the designed control loop. A novel hybrid particle swarm optimization–grey wolf optimization (HPSO-GWO) has been proposed, which assists in selecting the parameters of the distributed control technique, enabling the achievement of the proposed control objectives. Eigenvalue observation analysis has been utilized through the DC microgrid state-space model designed to assess the influence of the optimized distributed secondary control on the microgrid stability. A real-time testing system was constructed within MATLAB/Simulink® and deployed on Speedgoat™ real-time equipment to validate the operations of the proposed technique for practical applications. The results indicated that the proposed secondary control effectively enhances voltage recovery and ensures proper current distribution following various disturbances, thereby maintaining a continuous power supply. The outcomes also demonstrated the capabilities of the control approach in accomplishing the control objectives within the DC microgrid, characterized by minimal oscillations, overshoots/undershoots, and rapid time responses. [ABSTRACT FROM AUTHOR]

This article investigates the fixed‐time rotating consensus problem of second‐order multi‐agent systems in both the leaderless and leader‐following cases. Specifically, in the leaderless case, a distributed controller is developed to drive all agents to reach rotating consensus in fixed‐time. In the leader‐following case, a distributed fixed‐time observer is first proposed to estimate the position of a rotating leader. Then, a local fixed‐time output feedback controller without any velocity measurements is proposed to achieve the leader‐following motion. Furthermore, an integral sliding mode control technique is applied to handle the case where the multi‐agent system is subject to time varying external disturbances. Based on the bi‐limit homogeneity method and Lyapunov theory, we prove that all the agents reach the rotating consensus within a fixed time independent of initial conditions. Finally, simulations illustrate the effectiveness of the proposed controllers. [ABSTRACT FROM AUTHOR]

A distributed voltage and current coordination control strategy based on consensus algorithm was proposed to address the issue of traditional droop control in DC microgrids, which failed to simultaneously ensure voltage quality and power sharing accuracy. In the primary control, droop controllers were utilized to distribute power among distributed energy resources (DERs). Secondary control employed proportion integral controllers for voltage compensation to ensure voltage quality and current compensation for load current sharing accuracy. Moreover, a current-limiting function suitable for discrete dynamic consensus algorithms was designed to prevent control failures caused by converter of distributed energy resources exceeding their rated capacity during system operation. Finally, the effectiveness of the proposed strategy was verified through stability analysis and simulation using the MATLAB / Simulink platform. [ABSTRACT FROM AUTHOR]

An important group of decision-making problems is decision-making under uncertainty, including with incomplete or linguistically described data. Command and control systems, fitting into the multi-sensor paradigm of Industry 4.0/5.0, are becoming increasingly multifactorial. This trend will intensify, requiring uncertainty and incompleteness to be considered and mathematical description and data-processing systems better adapted to them. Aggregators are a group of tools used in solving the aforementioned decision problems, including within fuzzy systems. Aggregating functions are a useful tool mainly in those artificial intelligence systems with problems arising from incomplete data. The aim of this article is to review and describe existing aggregators used in fuzzy control in terms of their usefulness and limitations of their use. Particular attention is paid to the criteria for matching a suitable aggregator to a particular computational problem. This represents an important step towards the further use of this group of technologies in electronic devices and IT systems. [ABSTRACT FROM AUTHOR]

Despite the structure of road environments, imposed via geometry and rules, traffic flows exhibit complex multiagent dynamics. Reasoning about such dynamics is challenging due to the high dimensionality of possible behavior, the heterogeneity of agents, and the stochasticity of their decision-making. Modeling approaches learning associations in Euclidean spaces are often limited by their high sample complexity and the sparseness of available datasets. Our key insight is that the structure of traffic behavior could be effectively captured by lower-dimensional abstractions that emphasize critical interaction relationships. In this article, we abstract the space of behavior in traffic scenes into a discrete set of interaction modes, described in interpretable, symbolic form using topological braids. First, through a case study across real-world datasets, we show that braids can describe a wide range of complex behavior and uncover insights about the interactivity of vehicles. For instance, we find that high vehicle density does not always map to rich mixing patterns among them. Further, we show that our representation can effectively guide decision-making in traffic scenes. We describe a mechanism that probabilistically maps vehicles' past behavior to modes of future interaction. We integrate this mechanism into a control algorithm that treats navigation as minimization of uncertainty over interaction modes, and investigate its performance on the task of traversing uncontrolled intersections in simulation. We show that our algorithm enables agents to coordinate significantly safer traversals for similar efficiency compared to baselines explicitly reasoning in the space of trajectories across a series of challenging scenarios. [ABSTRACT FROM AUTHOR]

A PID‐inspired accelerated distributed optimal control algorithm is proposed for the economic dispatch problem of a multi‐bus DC microgrid, which contains both conventional generators (CGs) and renewable generators (RGs). Firstly, a constrained optimization problem with the aim of minimizing the power generation cost of the DC microgrid is established. To solve the optimization problem, an accelerated distributed optimal control algorithm in the discrete‐time domain is proposed. The convergence speed of the proposed algorithm is significantly improved compared to the existing distributed optimization algorithms without acceleration terms. More importantly, the communication cost is greatly reduced. The proposed algorithm is in a fully distributed manner, which means each controller only relies on the limited information from neighbouring controllers to achieve optimal cooperative control and bus voltage regulation across multiple buses. Finally, the effectiveness of the proposed algorithm is validated through numerical simulations.A PID‐inspired accelerated distributed optimal control algorithm is proposed for the economic dispatch problem of a multi‐bus DC microgrid, which contains both conventional generators and renewable generators. The convergence speed of the proposed algorithm is significantly improved compared to the existing distributed optimization algorithms without acceleration terms. [ABSTRACT FROM AUTHOR]

This paper studies the distributed output feedback consensus control for a class of uncertain nonlinear multi-agent systems (MASs). The dynamics of subsystems are heterogeneous and contain unknown control gains. A novel distributed fuzzy consensus control protocol is constructed by employing variable separation technology and adaptive backstepping technique. The main advantage of the protocol is that it is distributed only depending on the relative output information. Moreover, it does not require the observer information exchange, which can avoid network attacks. A numerical example is given to illustrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

In this article, we study the cooperative target‐fencing problem of multiple double‐integrator systems over a state‐dependent communication network. We propose a distributed control law to asymptotically fence a moving target while achieving both collision avoidance and connectivity preservation of the vehicles. In particular, a distributed observer is employed by each vehicle to estimate the position and the velocity of the target. Compared with the existing results, our approach can accommodate a larger class of target's trajectories. Furthermore, our control law is more practical since we do not require all vehicles to know the trajectory of the target. The effectiveness of our approach is illustrated by a numerical example. [ABSTRACT FROM AUTHOR]

For the position synchronization control problem of multiple nonlinear manipulator systems, a bounded distributed cooperative controller is designed in this paper. Based on a fixed‐time position observer, a distributed finite‐time synchronization controller is designed by using the saturated control technique and homogenous system theory. Compared with the existing results, the proposed distributed cooperative control algorithm not only guarantees finite‐time convergence but also satisfies the input saturation. Finally, some numerical simulation results are presented to verify the feasibility of the theory results with six‐degree‐of‐freedom robots. [ABSTRACT FROM AUTHOR]

In this article, we investigate the problem of differential graphical games for multiagent systems (MASs) with external disturbances. In particular, the system considered in this article allows the leader to have an unknown bounded control input, and the communication topology between agents is allowed to be a weakly restricted structure with only containing a directed spanning tree. We propose a novel dynamic sliding mode control strategy to dispose the presence of leader's nonzero control input and external disturbances. Furthermore, a nominal controller based on the worst‐case control strategies of neighbors is designed to provide a distributed solution for differential graphics games of MASs. Finally, our algorithms are applied to solve multiple unmanned surface vessels games. Simulation studies are provided to test the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper investigates a distributed adaptive finite‐time containment control scheme for multiple unmanned aerial vehicles subject to external disturbances and input saturation. Combined with a novel indicator of the saturation degree designed by the hyperbolic tangent function, an adaptive method is proposed to deal with the input saturation issue, which considers both symmetry and asymmetry saturation. Moreover, to address the problem of the "explosion of terms" inherent in the traditional backstepping controller design, a fixed‐time sliding mode differentiator is utilized to approximate the derivative of the virtual signal in the command‐filtered backstepping method. Finally, the convergence of the errors and the practicability of the control law are verified by Lyapunov stability analysis and numerical simulations. [ABSTRACT FROM AUTHOR]

The output regulation (OR) problem for linear heterogeneous networked multi-agent systems (NMAS) with communication constraints is investigated in this paper. A cost function is given to measure the coordinated control between NMAS. The objective of this paper is to design controllers such that not only the cost function is optimized, the asymptotic tracking and disturbance rejection are achieved but also the communication constraints are actively compensated. It is assumed that only a part of agents can receive the information of the exosystem, which can generate the reference signal and the disturbance signal. First, distributed observers are proposed to estimate the states of the subsystems and the state of the exosystem, which can guarantee the exponentially convergence of the estimation errors. Then, an observer-based predictive controller is proposed using feedforward method and networked predictive control method. With the help of OR technique, it indicates that the proposed predictive control scheme can deal with the OR problem. Finally, a numerical example is presented to illustrate our results. [ABSTRACT FROM AUTHOR]

Projections for future air mobility envisage intensely utilized airspace that does not simply scale up from existing systems with centralized air traffic control. This paper considers the implementation and test of a software and hardware framework for decentralized control of aerial vehicles within intensely used airspace. Up to 10 rotary wing vehicles of maximum all up mass of 1 kg are flown in an outdoor volume with length scale of 100 m with GPS and WiFi connectivity. Flight control is implemented using a Pixhawk 4 flight controller running the PX4 firmware with guidance algorithms run on a separate onboard companion computer. Deconfliction is implemented using a simple elastic repulsion model with a guidance update rate of 10 Hz . Traffic structures are constructed from a path of directed waypoints and associated cross sectional geometry. Junctions are implemented when two paths converge into one or when one path diverges into two. Agents engage with structures through execution of flow, merge and swirl velocity rules. Calibration experiments showed that the worst case latency in agents sharing position information was of the order of 0.5 s made up from delays due to finite guidance update rate, WiFi processing and centralized message processing. A choice of vehicle cruise speed of 2 m∕s and conflict radius of 2.5 m provided an acceptable compromise between experiment time efficiency (speed) and spatial efficiency (resolution) within the test volume. Results from recirculating junction experiments show that peak deconfliction activity occurs at the junction node, however biased distribution of agents within a corridor means the peak intensity is pushed ahead of the node. Use of meshed helical junction structures significantly reduces the intensity of conflict at the expense of reduced junction time efficiency. [ABSTRACT FROM AUTHOR]

The demand for electric vehicle supply equipment (EVSE) is increasing because of the rapid shift toward electric transport. Introducing EVSE on a large scale into the power grid can increase power demand volatility, negatively affecting frequency stability. A viable solution to this challenge is the development of smart charging technologies capable of performing frequency regulation. This paper presents an experimental proof of concept for a new frequency regulation method for EVSE utilizing a distributed control architecture. The architecture dynamically adjusts the contribution of electric vehicles (EVs) to frequency regulation response based on the charging urgency assigned by the EV users. The method is demonstrated with two Renault ZOEs responding to frequency fluctuation with a combined power range of 6 kW in the frequency range of 50.1 to 49.9 Hz. The results confirm consistent power sharing and effective frequency regulation, with the system controlling the engagement of the EVs in frequency regulation based on priority. The delay and accuracy analyses reveal a fast and accurate response, with the cross-correlation indicating an 8.48 s delay and an average undershoot of 0.17 kW. In the conclusions, the paper discusses prospective improvements and outlines future research directions for integrating EVs as service providers. [ABSTRACT FROM AUTHOR]

Compared with a single unmanned surface vessel (USV), multiple USVs have the advantages of strong maneuverability, high reliability, and low cost. Multiple USVs have potential oceanic transportation, resource exploration, and maritime rescue applications. However, cooperative formation control of multiple USVs is a significant yet challenging problem. The main difficulties arise from the limited communication and the existence of model uncertainties and external perturbations. Therefore, this paper proposes a fixed-time adaptive distributed control strategy consisting of a fixed-time distributed observer and local controller for cooperative formation control of multiple USVs under local communication in complex marine environment. First, a fixed-time distributed observer is constructed for each follower USV to estimate the virtual leader’s position and velocity in fixed time by considering that only a portion of follower USVs can directly access the state information from the virtual leader. Based on the estimated information, a fixed-time local controller is designed for each follower USV to track the desired trajectory generated by the virtual leader in a fixed time using the backstepping method. In particular, in the local controller design process, a parameter adaptive mechanism is introduced to estimate the square of the upper bound of the lumped disturbance term, including model uncertainty and external disturbance to compensate for them. Thus, the proposed controller is smooth to guarantee the control accuracy, and the chattering phenomenon can be avoided simultaneously. Stability analysis of the proposed fixed-time observer and local controller is also presented. Mathematical proofs show that the proposed observer and controller can ensure that each USV can complete the position estimation and virtual leader velocity in a fixed time, thereby making the tracking error converge to the zero neighborhood in a fixed time. Finally, USVs with the proposed control strategy can follow the virtual leader and maintain the formation as shown in the simulation. Additionally, a comparative simulation is designed, where the control performance of the proposed strategy is compared with that of the commonly used PD controller under the same condition. The simulation results show that the proposed controller has superior control accuracy and convergence rate in controlling the USVs to track the desired position and velocity of the demanded formation. Moreover, the chattering phenomenon of the control inputs is significantly suppressed.

Abstract For cooperation among distributed generations in a DC microgrid (MG), distributed control is widely applied. However, the delay in distributed communication will result in steady‐state bias and the risk of instability. This paper proposes a novel distributed control for time‐delayed DC MGs to achieve accurate current proportional sharing and weighted average voltage regulation. Firstly, by utilizing an advanced observer based on the PI consensus algorithm, the steady‐state bias problem is addressed. Then, using the passivity theory, stability analysis is conducted to reveal the principle of system instability caused by communication delay. On this basis, to offset the adverse effects of communication delay on the system stability, scattering transformation is introduced in the observer‐based distributed control. Moreover, considering the potential delay from the measurement stage in real‐life scenarios, the sufficient condition of the system stability is concluded by constructing the Lyapunov–Krasovskii functional. Finally, the performance of the proposed control and conclusions of stability analysis are verified by hardware‐in‐loop tests.

Abstract This article addresses the issue of collaborative wildfire monitoring using a group mobile mecanum‐wheeled omnidirectional vehicles (MWOVs) affected by nonlinear uncertainties and external disturbances. By integrating finite‐time extended state observers (FTESO) and backstepping nonsingular fast terminal sliding mode (BNFTSM) control method, an observer‐based finite‐time time‐varying elliptical formation control scheme is proposed for a group of MWOVs tasked with monitoring the propagation of wildfires in an elliptical pattern. First, the FTESO is employed to estimate the unavailable velocity system states and the lumped disturbances. Then, a novel nonsingular fast terminal sliding surface, enhanced with an exponential term, is introduced to improve the convergence rate. Through the Lyapunov theorem, the convergence of position and velocity cooperative tracking errors to zero in fast finite‐time is demonstrated. To showcase the effectiveness of the proposed control scheme, comparative simulation results are presented.

This paper introduces a distributed control approach for optimizing the operation of islanded DC microgrids (µGs), accounting for cyber delays and network failures. Initially, an integrated network emulation platform is utilized to calculate realistic cyber delays for islanded DC µGs employing IEC 61850 communication standard. To address the instability issues arising from cyber delays, the Artstein-Kwon-Pearson reduction technique is employed to convert the system with cyber delays into one without delays. The Subsequently, a consensus-based generation-cost optimization algorithm is designed for maintaining the economic operation of the µG. This algorithm efficiently equalizes the incremental costs (ICs) of all distributed generators (DGs) in a fully distributed manner, while adhering to the DGs capacity limits. Additionally, a fully distributed voltage regulation control is developed to stabilize µG average voltage, ensuring the balance between generation and demand. The efficacy of the formulated control technique is substantiated through diverse case studies, thereby underscoring its superior operational performance.

Abstract Grid‐forming inverters are used for voltage regulation and frequency control in autonomous hybrid microgrids and multi‐microgrid systems by imitating synchronous generators. However, in microgrids with weak grids including low inertia levels and small X/R ratios, these inverters interact with each other, and as a result low‐frequency oscillations (LFO) arise. LFO impacts the frequency stability of multi‐microgrid systems. Nevertheless, LFO can be mitigated by the load‐frequency control system, which serves as a secondary control mechanism. However, the presence of wind turbines and photovoltaic systems in hybrid microgrids adds complexity to the operation of the load‐frequency control due to the uncertainty associated with these renewable energy resources, and various controllers have been employed. This paper proposes a novel approach to enhance the performance of the load‐frequency control system and suppress LFO. The presented technique reduces the complexity of the hybrid microgrid structure by reducing the number of controllers. The model predictive control (MPC) technique is utilized for load‐frequency control and the weight parameters of the MPC are determined using the rain optimization algorithm. The proposed method demonstrates improved dynamic response, reduced overshoot and undershoot responses, decreased controller complexity, and effective LFO suppression. The simulation results verify the effectiveness of the method.

A droop-free distributed energy storage control strategy based on the diffusion algorithm is proposed to address the inability of droop control to simultaneously achieve bus voltage stability and accurate power distribution among energy storage systems when the line impedance is mismatched and bus voltage is inconsistent. First, the diffusion algorithm is applied to the distributed estimation of direct current (DC) microgrid to obtain the global average, and the difference between the voltage rating and the average voltage is used as a compensation term to restore the bus voltage deviation. Then, in order to achieve the accurate distribution of power between energy storage systems with different rated capacities and different states of charge (SOC), a standard power of energy storage is designed and the difference between the standard power and the average standard power is used as a compensation term for the equilibrium of SOC between energy storage systems. Finally, a model based on RT-LAB is built to verify the effectiveness of the designed control strategy in four different operating modes. The experimental results show that the proposed control strategy can achieve the bus voltage recovery and the accurate distribution of energy storage power in the isolated DC microgrid.

Abstract Distributed dynamic event‐triggered control (ETC) method is proposed to solve the stochastic and intermittent problem caused by distributed renewable energy generation unit (DG) in microgrid (MG). It aims at reducing communication cost while achieving proportional current sharing among DGs. Firstly, the state space function model of the MG system is established, considering the unknown disturbance caused by DGs and the uncertainty caused by the constant power load (CPL). Also, the system model is transformed into the standard linear heterogeneous multiagent systems (LHMAS). Subsequently, the distributed dynamic ETC method with consensus algorithm is proposed to achieve proportional current sharing among DGs. Compared with conventional ETC approaches, the authors’ method is based on directed topology, which reduces the controller updating frequency and the communication frequency among DGs. At the same time, the Zeno behaviour can be avoided. Additionally, the authors’ method is much easier to be implemented, which can also handle the uncertain loads and CPLs. Finally, the simulation results demonstrate the effectiveness of the proposed method.

Due to time‐varying external disturbances and uncertain system models, distributed cooperative controllers with poor adaptability are unable to meet the cooperative control requirements of multiple permanent magnetic maglev trains in virtual coupling mode. In this work, a new effective distributed auto disturbance rejection resilient controller based on the optimized deep deterministic policy gradient algorithm (DDPG) is proposed. The DDPG algorithm is used to improve the adaptability of the controller against the time‐varying disturbances. An adaptive particle swarm optimization method (APSO) is also proposed to optimize the hyperparameters of DDPG in the search space. The simulation results show that, compared to the particle swarm optimization (PSO)‐actor‐critic (AC), PSO‐policy gradient (PG), and PSO‐DDPG algorithms, the proposed APSO‐DDPG algorithm performs better during training and verification. The proposed method achieves adaptive online adjustment of the controller parameters effectively and greatly improves the stability of cooperative control. [ABSTRACT FROM AUTHOR]

Grid‐forming inverters are used for voltage regulation and frequency control in autonomous hybrid microgrids and multi‐microgrid systems by imitating synchronous generators. However, in microgrids with weak grids including low inertia levels and small X/R ratios, these inverters interact with each other, and as a result low‐frequency oscillations (LFO) arise. LFO impacts the frequency stability of multi‐microgrid systems. Nevertheless, LFO can be mitigated by the load‐frequency control system, which serves as a secondary control mechanism. However, the presence of wind turbines and photovoltaic systems in hybrid microgrids adds complexity to the operation of the load‐frequency control due to the uncertainty associated with these renewable energy resources, and various controllers have been employed. This paper proposes a novel approach to enhance the performance of the load‐frequency control system and suppress LFO. The presented technique reduces the complexity of the hybrid microgrid structure by reducing the number of controllers. The model predictive control (MPC) technique is utilized for load‐frequency control and the weight parameters of the MPC are determined using the rain optimization algorithm. The proposed method demonstrates improved dynamic response, reduced overshoot and undershoot responses, decreased controller complexity, and effective LFO suppression. The simulation results verify the effectiveness of the method. [ABSTRACT FROM AUTHOR]

In the islanded microgrid, the mismatch in line impedance hinders the traditional droop control from achieving an equal distribution of reactive power among Distributed Generation (DG) sources. To address this problem and enhance the flexibility and reliability of the controller, this paper analyzes the reasons behind the failure of traditional droop control and proposes an adaptive droop coefficient that can be dynamically adjusted to meet the conditions for reactive power sharing. Additionally, a dynamic distributed observer is designed and its convergence is proven, which allows DG sources to obtain necessary information flexibly and reliably in a distributed manner. Finally, the proposed control strategy is verified through simulations in four different scenarios, and the results demonstrate its superiority and effectiveness. [ABSTRACT FROM AUTHOR]

This article discusses the quasi-optimality of adaptive nonconforming finite element methods for distributed optimal control problems governed by 푚-harmonic operators for m = 1 , 2 . A variational discretization approach is employed and the state and adjoint variables are discretized using nonconforming finite elements. Error equivalence results at the continuous and discrete levels lead to a priori and a posteriori error estimates for the optimal control problem. The general axiomatic framework that includes stability, reduction, discrete reliability, and quasi-orthogonality establishes the quasi-optimality. Numerical results demonstrate the theoretically predicted orders of convergence and the efficiency of the adaptive estimator. [ABSTRACT FROM AUTHOR]

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In meeting the diverse and occasionally conflicting quality of service (QoS) requirements associated with modern communication networks, 5G technology has emerged as a pivotal player. In its architecture, 5G has adopted network function virtualization (NFV) and cloud-based approaches, aiming to simplify network and service deployment, operational processes, and management. The convergence of software defined networking (SDN) and NFV offers an effective solution, enabling scalable and high-performance 5G networks. However, this integration poses critical challenges, with the placement of SDN controllers being a central concern due to its significant impact on network performance, covering aspects such as latency, costs, and energy efficiency. This challenge is known as the controller placement problem (CPP). The central theme of this paper revolves around the intricate relationship between 5G core networks, virtualization technology, and the pressing concern of SDN controller placement, underscoring its significance in the modern networking landscape. We provide a survey of recent methodologies aimed at solving the CPP within the realm of SDN, with a particular focus on resiliency and high availability. [ABSTRACT FROM AUTHOR]

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We present a straightforward way to solve a model predictive control problem for a power network system given as a nonlinear differential‐algebraic equation (DAE) in a distributed way using the consensus alternating directions method of multipliers (consensus ADMM) algorithm. While no convergence‐ or stability results are available for fully nonlinear DAE models, this gives unprecedented experimental evidence that power network systems of the presented structure allow to be controlled in this way, unlocking the numerous combined advantages of distributed and predictive control schemes in the context of energy distribution networks, as well as broadening the field of use for the consensus ADMM algorithm to nonlinear DAE models. [ABSTRACT FROM AUTHOR]