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Cooperative control is both a crucial and hot research topic for multi-agent systems (MASs). However, most existing cooperative control strategies guarantee tracking stability under various non-ideal conditions, while the path decision capability is often ignored. In this paper, the integrated decision-execution (IDE) framework is newly presented for cooperative control of multi-agent systems (MASs) to accomplish the integrated task of path decision and cooperative execution. This framework includes a decision layer and a control layer. The decision layer generates a continuous trajectory for the virtual leader to reach the target from its initial position in an unknown environment. To achieve the goal of this layer, (1) the Step-based Adaptive Search Q-learning (SASQ-learning) algorithm is proposed based on reinforcement learning to efficiently find the discrete path, (2) an Axis-based Trajectory Fitting (ATF) method is developed to convert the discrete path into a continuous trajectory for mobile agents. In the control layer, this trajectory is used to regulate the following MASs to achieve cooperative tracking control with the presence of input saturation. Simulation experiments are presented to demonstrate the effectiveness of this framework. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of automated intersection control for connected vehicles in the presence of Denial-of-Service (DoS) attacks. DoS attacks can disrupt the vehicular communication network, leading to increased service time and additional transmission delays, thereby raising a risk of collisions at intersections. To mitigate this problem, we propose a resilient automated intersection control system that ensures safe passage of connected vehicles through non-signalized intersections. First, we establish a communication topology that captures the information transmission between vehicles using a vehicle conflict graph. This topology serves as the basis for designing a distributed control protocol that enables conflict-free cooperation at intersections, taking into account the stochastic delays caused by DoS attacks. To synthesize a resilient controller for the automated intersection control system, we employ the polytopic overapproximation technique based on the real Jordan form. This technique allows us to model the system dynamics and uncertainties, enabling the derivation of a set of sufficient conditions for system stability. By solving a series of linear matrix inequalities, we obtain the controller gains that satisfy these stability conditions. Numerical examples are presented to demonstrate the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

This paper investigates the issue of finite-time cooperative depth control of a multi-autonomous underwater vehicle (AUV) system with heterogeneous dynamical structures and heterogeneous time-varying parameters. Initially, we introduce a hierarchical control architecture to decouple communication from the local dynamics effectively, thus mitigating the complexities introduced by such heterogeneity. The architecture leverages the Internal Model Principle (IMP) to transform the arduous task of finite-time cooperative depth control for the heterogeneous multi-AUV system into tractable individual finite-time observation and tracking problems, which simplifies the overall control problem substantially. Subsequently, in response to both finite-time observing and tracking problems, a cooperative observer is designed based on a potential function, and a distributed local feedback controller is developed to achieve finite-time cooperative control. Stability parameter conditions for both the observer and controller are derived rigorously to facilitate control design. The proposed control approach relaxes the constraints on communication topology and dynamics, enhancing its practical applicability in multi-AUV systems. The feasibility of the hierarchical architecture and the finite-time stability of the upper observer and lower controller are rigorously analyzed. Comparative numerical simulation experiments are conducted to demonstrate the effectiveness of the proposed methods. • A multi-AUV system with heterogeneous dynamical structures is studied. • A hierarchical control framework is introduced to simplify the control design. • The potential function is introduced to design a finite-time observer for AUVs. • A finite-time controller is designed considering the time-varying parameters. [ABSTRACT FROM AUTHOR]

This study focuses on addressing optimal bipartite consensus control (OBCC) problems in heterogeneous multi-agent systems (MASs) without relying on the agents' dynamics. Motivated by the need for model-free and optimal consensus control in complex MASs, a novel distributed scheme utilizing reinforcement learning (RL) is proposed to overcome these challenges. The MAS network is randomly partitioned into sub-networks where agents collaborate within each subgroup to attain tracking control and ensure convergence of positions and speeds to a common value. However, agents from distinct subgroups compete to achieve diverse tracking objectives. Furthermore, the heterogeneous MASs considered have unknown first and second-order dynamics, adding to the complexity of the problem. To address the OBCC issue, the policy iteration (PI) algorithm is used to acquire solutions for discrete-time Hamilton-Jacobi-Bellman (HJB) equations while implementing a data-driven actor-critic neural network (ACNN) framework. Ultimately, the accuracy of our proposed approach is confirmed through the presentation of numerical simulations. • A novel approach is proposed to investigate the OBCC of heterogeneous MASs in competition-cooperation relationship. • To avoid the use of system dynamics, a model-free RL algorithm is proposed, utilizing available input-output data to construct a new system and achieve the OBCC of heterogeneous MASs. • The challenge of dimensional discrepancies in heterogeneous MASs is overcome by the incorporation of estimated velocities, thereby converting the heterogeneous systems into homogeneous systems. • A distributed actor-critic neural network based on PI is proposed to obtain the optimal control policy, and the convergence analysis of the PI algorithm is conducted. [ABSTRACT FROM AUTHOR]

This paper primarily examines group consensus (GC) in heterogeneous multi-agent systems (HMASs) within the context of cooperative-competitive networks and input delay. The agent dynamics are modeled using single and double integrators, while the complex network encompassing these agents demonstrate both cooperative and competitive relationships. We first propose control protocols incorporating delays to address GC in HMASs. Secondly, we establish algebraic criteria employing frequency-domain analysis techniques to ensure the achievement of GC in directed network topologies for HMASs, which is equally applicable to undirected networks. Following this, we conduct an extensive discussion to determine the upper boundary for permissible input delays in HMASs. Finally, simulations are performed to validate our theoretical findings and the algorithms. [ABSTRACT FROM AUTHOR]

This paper is concerned with the multi-player stochastic differential pursuit-evasion game problem based on directed graphs. To solve the challenges posed by limited communication and ubiquitous noise, a novel Riccati equation is proposed based on the linear–quadratic exponential cost function under both complete and partial observations. The optimal strategies for pursuers and evaders are obtained based on the direct method of completing the square and Radon–Nikodym derivative, without the need to solve the complex Hamilton–Jacobi–Isaacs equation. The strategy presented in this paper is distributed and can be implemented without requiring any global information, under the constraints of a directed topology. Additionally, the proposed partial algorithm introduces an information filter to estimate partial observations. We also demonstrate that the proposed strategy constitutes a Nash equilibrium. Numerical simulations confirm the effectiveness of our strategy in both complete and partial observation scenarios. [ABSTRACT FROM AUTHOR]

This paper presents a cooperative control method for connected and automated vehicle (CAV) platooning, thus specifically addressing the challenge of sensor measurement errors that can disrupt the stability of the CAV platoon. Initially, the state-space equation of the CAV platooning system was formulated, thereby taking into account the measurement error of onboard sensors. The superposition effect of the sensor measurement errors was statistically analyzed, thereby elucidating its impact on cooperative control in CAV platooning. Subsequently, the application of a Kalman filter was proposed as a means to mitigate the adverse effects of measurement errors. Additionally, the CAV formation control problem was transformed into an optimal control decision problem by introducing an optimal control decision strategy that does not impose pure state variable inequality constraints. The proposed method was evaluated through simulation experiments utilizing real vehicle trajectory data from the Next Generation Simulation (NGSIM). The results demonstrate that the method presented in this study effectively mitigates the influence of measurement errors, thereby enabling coordinated vehicle-following behavior, achieving smooth acceleration and deceleration throughout the platoon, and eliminating traffic oscillations. Overall, the proposed method ensures the stability and comfort of the CAV platooning formation.

This study introduces novel stochastic distributed cooperative control (SDCC) in the context of island microgrids (MGs). A proportional resonant (PR) controller and virtual impedance droop control in stationary reference frames are employed in cooperation with distributed averaging secondary control optimized by the dragonfly algorithm (DA). The suggested approach demonstrates the capability to achieve mean-square synchronization for the voltage and frequency restoration of distributed generators (DGs) to ensure efficient active power sharing. Therefore, a sparse communication network has been used to avoid data congestion and reduce the need for extensive communication and information exchange. The proposed system offers an instinctive compromise between voltage regulation and reactive power sharing. A conventional centralized secondary control with PR droop control is simulated for performance evaluation and comparison purposes. In this study, empirical evidence is demonstrated to support the MG’s ability to confront communication failure and its ability to work reliably during plug-and-play operations.

In this work, we propose two cooperative passivity-based control methods for networks of mechanical systems. By cooperatively synchronizing the end-effector coordinates of the individual agents, we achieve cooperation between systems of different types. The underlying passivity property of our control approaches ensures that cooperation is stable and robust. Neither of the two approaches rely on the modeling information of neighbors, locally, which simplifies the interconnection of applicable systems and makes the approaches modular in their use. Our first approach is a generalized cooperative Interconnection-and-Damping Assignment passivity-based control (IDA-PBC) scheme for networks of fully actuated and underactuated systems. Our approach leverages the definition of end-effector coordinates in existing single-agent IDA-PBC solutions for underactuated systems to satisfy the matching conditions, independently of the cooperative control input. Accordingly, our approach integrates a large set of existing single-agent solutions and facilitates cooperative control between these and fully actuated systems. Our second approach proposes agent outputs composed of their end-effector coordinates and velocities to guarantee cooperative stability for networks of fully actuated systems in the presence of communication delays. We validate both approaches in simulation and experiments.

Efficient logistics and transport at the port heavily relies on efficient AGV scheduling and planning for container transshipment. This paper presents a comprehensive approach to address the challenges in AGV path planning and coordination within the domain of intelligent transportation systems. We propose an enhanced graph search method for constructing the global path of a single AGV that mitigates the issues associated with paths closely aligned with obstacle corner points. Moreover, a centralized global planning module is developed to facilitate planning and scheduling. Each individual AGV establishes real-time communication with the upper layers to accurately determine its position at complex intersections. By computing its priority sequence within a coordination circle, the AGV effectively treats the high-priority trajectories of other vehicles as dynamic obstacles for its local trajectory planning. The feasibility of trajectory information is ensured by solving the online real-time Optimal Control Problem (OCP). In the trajectory planning process for a single AGV, we incorporate a linear programming-based obstacle avoidance strategy. This strategy transforms the obstacle avoidance optimization problem into trajectory planning constraints using Karush-Kuhn-Tucker (KKT) conditions. Consequently, seamless and secure AGV movement within the port environment is guaranteed. The global planning module encompasses a global regulatory mechanism that provides each AGV with an initial feasible path. This approach not only facilitates complexity decomposition for large-scale problems, but also maintains path feasibility through continuous real-time communication with the upper layers during AGV travel. A key advantage of our progressive solution lies in its flexibility and scalability. This approach readily accommodates extensions based on the original problem and allows adjustments in the overall problem size in response to varying port cargo throughput, all without requiring a complete system overhaul.

This paper investigates the problem of cooperative standoff tracking using multiple fixed-wing unmanned aerial vehicles (UAVs) with control input constraints. In order to achieve accurate moving target tracking in the presence of unknown background wind, a coordinated standoff target tracking algorithm is proposed. The objective of the research is to steer multiple UAVs to fly a circular orbit around a moving target with prescribed intervehicle angular spacing. To achieve this goal, two control laws are proposed, including relative range regulation and space phase separation. On one hand, a heading rate control law based on a Lyapunov guidance vector field is proposed. The convergence analysis shows that the UAVs can asymptotically converge to a desired circular orbit around the target, regardless of their initial position and heading. Through a rigorous theoretical proof, it is concluded that the command signal of the proposed heading rate controller will not violate the boundary constraint on the heading rate. On the other hand, a temporal phase is introduced to represent the phase separation and avoid discontinuity of the wrapped space phase angle. On this basis, a speed controller is developed to achieve equal phase separation. The proposed airspeed controller meets the requirements of the airspeed constraint. Furthermore, to improve the robustness of the aircraft during target tracking, an estimator is developed to estimate the composition velocity of the unknown wind and target motion. The proposed estimator uses the offset vector between the UAV’s actual flight path and the desired orbit, which is defined by the Lyapunov guidance vector field, to estimate the composition velocity. The stability of the estimator is proved. Simulations are conducted under different scenarios to demonstrate the effectiveness of the proposed cooperative standoff target tracking algorithm. The simulation results indicate that the temporal-phase-based speed controller can achieve a fast convergence speed and small phase separation error. Additionally, the composition velocity estimator exhibits a fast response speed and high estimation accuracy.

The foundation of intelligent collaborative control of a shearer, scraper conveyor, and hydraulic support (three-machines) is to achieve the precise perception of the status of the three-machines and the full integration of information between the equipment. In order to solve the problems of information isolation and non-flow, independence between equipment, and weak cooperation of three-machines due to an insufficient fusion of perception data, a fusion method of the equipment’s state perception system on the intelligent working surface was proposed. Firstly, an intelligent perception system for the state of the three-machines in the working face was established based on fiber optic sensing technology and inertial navigation technology. Then, the datum coordinate system is created on the working surface to uniformly describe the status of the three-machines and the spatial position relationship between the three-machines is established using a scraper conveyor as a bridge so that the three-machines become a mutually restricted and collaborative equipment system. Finally, an indoor test was carried out to verify the relational model of the spatial position of the three-machines. The results indicate that the intelligent working face three-machines perception system based on fiber optic sensing technology and inertial navigation technology can achieve the fusion of monitoring data and unified expression of equipment status. The research results provide an important reference for building an intelligent perception, intelligent decision-making, and automatic execution system for coal mines.

Automatic train operation is an important part of the train control system. As train operating intervals continue to shorten and train speeds continue to increase, multiple train cooperative control is currently an important technology to further improve the efficiency of train operation and line passing capacity. However, considering various factors such as the nonlinearity and uncertainty of the train dynamics model and the complexity of the line conditions, this creates even greater demands on the design of the controller. In this study, we propose an adaptive cooperative tracking control method for multiple trains using adjacent information. For the multiple-train coordinated tracking control in the presence of model uncertainties, unknown parameters, and external disturbances, a distributed cooperative control scheme for multiple trains is designed using the displacement, velocity, and acceleration information of adjacent trains, combined with radial basis function neural networks and adaptive methods. A fast high-order sliding mode observer is used to estimate the train velocity and acceleration information. Stability and convergence are proved for single and multiple trains utilizing Lyapunov stability analysis. Simulation examples demonstrate the effectiveness of the proposed controller.

Effective control of an exoskeleton robot (ER) using a human-robot interface is crucial for assessing the robot's movements and the force they produce to generate efficient control signals. Interestingly, certain surveys were done to show off cutting-edge exoskeleton robots. The review papers that were previously published have not thoroughly examined the control strategy, which is a crucial component of automating exoskeleton systems. As a result, this review focuses on examining the most recent developments and problems associated with exoskeleton control systems, particularly during the last few years (2017–2022). In addition, the trends and challenges of cooperative control, particularly multi-information fusion, are discussed.

The consensus problem for a class of high-order nonlinear multi-agent systems (MASs) with external disturbance and system uncertainty is studied. We design an online-update radial basis function (RBF) neural network based distributed adaptive control protocol, where the sliding model control method is also applied to eliminate the influence of the external disturbance and system uncertainty. System consensus is verified by using the Lyapunov stability theorem, and sufficient conditions for cooperative uniform ultimately boundedness (CUUB) are also derived. Two simulation examples demonstrate the effectiveness of the proposed method for both homogeneous and heterogeneous MASs.

The work described in this paper proposes a new conflict minimisation strategy in shared driving control for lane keeping systems (LKS) in intelligent vehicles. This strategy takes into account a dynamic driver model, where the driver's parameters are identified online using the Lyapunov approach. The design of an adaptive shared controller is based on the dynamic parameters of the driver model which changes according to the driver and the situation encountered. Based on Lyapunov stability arguments, the overall asymptotic stability of the closed-loop control system with the adaptive driver model and the variation of the vehicle speed is proved and an LMI optimization is used to formulate the control design. The simulation results, conducted with the SHERPA dynamic car simulator under real-world driving situations, show the importance of integrating a dynamic driver model in the controller design in order to decrease the conflict between the driver and the lane keeping system and to ensure the safety of the vehicle as well as to increase the confidence and acceptability of the driver. [ABSTRACT FROM AUTHOR]

With the large-scale distributed PV connected to the grid, the random and intermittent nature of PV output, the non-linearity of the inverter, as well as the low daytime base-load and large-scale back feeding cause outstanding power quality problems such as overvoltage, three-phase unbalance, and high harmonic content at the end of the power supply system, which seriously affects the safe and stable operation of the grid and power efficiency. Based on the residual capacity of the inverter, this paper investigates a cooperative power quality control strategy that integrates active power filter, reactive power compensation, and inverter functions to achieve comprehensive power quality control and management without adding additional hardware equipment. The validation results show that the control strategy strongly improves the power quality, grid security, stability, and efficiency. This strategy indicates significant economic benefits. [ABSTRACT FROM AUTHOR]

This paper investigates the resilient distributed secondary control problem against FDI attacks for the seaport microgrid with a high proportion of renewable energy. Firstly, the polymorphic seaport microgrid containing a power layer, a control layer, a data layer and a service layer is constructed. It can achieve a software-defined function for control strategies based on a layered network and allows heterogeneous distributed generators (DGs) to exchange various types of data packets. Secondly, considering the unbounded attack generated by stolen estimator parameters can rapidly cause a large-scale power outage of the seaport microgrid, an estimation-dependent attack is designed from the perspective of attackers. Furthermore, a resilient distributed secondary control strategy using the virtual network is proposed to defend against the estimation-dependent attack. The virtual layer interconnects with the original control layer in the polymorphic network to generate an attack compensation vector, which can suppress the attack in the control layer. Furthermore, the stability analysis is completed by using the Lyapunov theory. Finally, the effectiveness of the proposed strategy is validated by a seaport microgrid test model with six DGs. [ABSTRACT FROM AUTHOR]

In this article, a novel distributed cooperative control framework is proposed to solve the problems of unbalanced load current sharing and bus voltage deviation in seaport DC microgrid. This framework without droop control, and a novel droop-free cooperative controller is constructed to achieve current sharing and voltage regulation goals. Each distributed generator (DG) transmits information based on a sparse communication network, and uses a discrete dynamic consensus algorithm (DCA) to complete the accurate estimation of a virtual average state variable containing voltage and current information. In addition, a current limiting function adapted to DCA is designed to avoid control failure and prevent the converter output currents from exceeding the rated value. Finally, the influence of boundary weight, communication delays, and calculation delays on the system stability is analyzed, and a seaport DC microgrid simulation model and StarSim HIL experimental platform are built. The results show that the proposed control scheme can achieve average bus voltage restoration and proportional load current sharing within the allowable range of communication delays and calculation delays, and has prominent plug-and-play capabilities. [ABSTRACT FROM AUTHOR]

This paper is concerned with the cooperative output tracking problem of a class of unknown heterogeneous discrete-time multi-input multi-output (MI-MO) linear multi-agent systems (MASs) under switching topologies. A novel design framework is developed based on a distributed observer and a distributed multivariable adaptive control approach. First, a distributed observer is constructed to estimate the leader's states by using the neighboring relative information under the influences of switching topologies. Then, to handle the unknown parameters of MIMO systems, a distributed multivariable model reference adaptive control approach is designed to follow the estimation states of the leader. It is shown that under the proposed design framework, the boundedness of all signals and variables in the closed-loop system is guaranteed, and the output tracking of the MASs is achieved. Moreover, the proposed design framework does not need the knowledge of global graph information. Finally, an example is provided to illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this article, the problem of distributed synchronization of networked systems with actuator bias faults is investigated. To effectively use the limited network bandwidth and avoid the requirement of global information, a novel adaptive event-triggered state feedback controller and a dynamic triggering law are designed jointly by employing a projection operator approach. The proposed synchronization scheme is different from existing ones that have focused on designing controllers and triggering laws independently. Besides, our scheme is extended to design an observer-based distributed adaptive event-triggered controller and corresponding dynamic triggering law when the system states are unmeasurable. Theoretical analysis shows that under the two different distributed event-triggered synchronization schemes, the following three results can be obtained: 1) fully distributed synchronization can be achieved without knowing global information associated with the underlying communication topology and node’s scale; 2) continuous communication among adjacent nodes can be avoided for both designed controllers and dynamic triggering laws; and 3) exclusion of Zeno phenomenon is shown by contradiction. Finally, the effectiveness of the proposed algorithms is verified through three numerical examples. [ABSTRACT FROM AUTHOR]

The superconducting electrodynamic suspension (EDS) train, which has reached a manned speed of 603 km/h in 2015, and is therefore regarded as one of the most attractive technologies for the higher speed train. In the superconducting EDS train, the discrete layout of ground null-flux coils causes the additional electromagnetic ripples towards the bogies, making the vibration control more essential to ensure the running performance. In this work, a fourteen-degree-of-freedom dynamics model of superconducting EDS train, was established to explore the effective strategy for vibration reduction. This model uses the time domain wave of track irregularity spectrum as the input of vehicle system. MATLAB/Simulink and "Zhai's method" are both adopted to solve the vehicle dynamic model and validate the model. Using these models, the effectiveness of the proportional force and maximum force for the primary suspension was examined. The results show that the maximum force strategy has advantage over the proportional force strategy in terms of energy consumption and vibration sensitiveness. However, the electromagnetic damping control can only suppress the vibration of bogie. To achieve an overall vibration control, the "ON-OFF" sky-hook damping was further introduced to the secondary suspension to suppress the vibration of car body. We find that, the control effect is preferable with the damping value being between 5 kN·s/m and 10 kN·s/m. Therefore, the cooperative control, considering both the primary and secondary suspensions, is necessary to meet the Urban Tracked Aircushion Vehicle Standard and provide theoretical supports for the suspension design of superconducting EDS train. [ABSTRACT FROM AUTHOR]

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This paper proposes a hybrid distribution transformer integrating photovoltaics (HDT-PV) with local and remote cooperative control in an active distribution network (ADN). HDT-PVs, regarded as promising devices with high reliability and easy engineering in ADNs, can not only realize the on-site maximum utilization of distributed solar energy but also solve the various power quality problems that follow. Moreover, the reactive power flow in the ADN can be optimized through remote cooperative control of multiple HDT-PVs. In this situation, the two objectives of voltage regulation and network power loss decrease are achieved in the ADN. If the power electronic device in the HDT-PV fails, the HDT-PV is able to work as a conventional distribution transformer by cutting off all power electronic devices and PV systems. Even when a small number of HDT-PVs malfunction, the system can still run stably. Finally, the simulation and experimental results demonstrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

The suspension system is a significant part of a vehicle because it transmits the torque and force between the wheels and the frame, meeting the requirements of ride comfort. In this paper, a novel interconnected air suspension was introduced and a cooperative control between interconnected mode activation (IMA) and outsourced mode activation (OMA) was designed. To improve ride comfort in a bus at a minimal energy consumption, this cooperative control based on model predictive control (MPC) for computing the best force and the rule was defined to distribute the best suspension force generated by IMA and OMA. The simulation and experimental results showed that the proposed control strategy significantly improved ride comfort in different conditions. Compared with traditional control in the test vehicle, the RMS of the front-left sprung mass and unsprung vertical acceleration decreased by 24.2% and 14.5%, respectively, under a straight condition; the RMS of the sprung (unsprung) mass vertical acceleration reduced by 22.38% and 15.43%, respectively, under a bump condition. [ABSTRACT FROM AUTHOR]

Holographic optical tweezers have unique non-physical contact and can manipulate and control single or multiple cells in a non-invasive way. In this paper, the dynamics model of the cells captured by the optical trap is analyzed, and a control system based on a novel self-organizing fuzzy cerebellar model neural network (NSOFCMNN) is proposed and applied to the cell manipulation control of holographic optical tweezers. This control system consists of a main controller using the NSOFCMNN with a new self-organization mechanism, a robust compensation controller, and a higher order sliding mode. It can accurately move the captured cells to the expected position through the optical trap generated by the holographic optical tweezers system. Both the layers and blocks of the proposed NSOFCMNN can be adjusted online according to the new self-organization mechanism. The compensation controller is used to eliminate the approximation errors. The higher order sliding surface can enhance the performance of controllers. The distances between cells are considered in order to further realize multi-cell cooperative control. In addition, the stability and convergence of the proposed NSOFCMNN are proved by the Lyapunov function, and the learning law is updated online by the gradient descent method. The simulation results show that the control system based on the proposed NSOFCMNN can effectively complete the cell manipulation task of optical tweezers and has better control performance than other neural network controllers. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article develops protocols for the consensus of an uncertain nonlinear multi‐agent system that satisfies the one‐sided Lipschitz and quadratic‐inner bounded conditions. Fully distributed consensus protocols with observer‐based and observer‐less schemes are investigated. For the observer‐based system, a full order Luenberger observer is designed. To overcome the difficulties of designing the controller and observer‐based controller gains, sufficient conditions are provided with a complete linear matrix inequality characterization. Finally, two simulation examples are provided to illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, the problem of cooperative state feedback control for a class of large-scale linear systems described by delta operator is studied. First, according to the independent subsystem described by delta operator, an interconnected closed-loop control system is given through cooperative state feedback controller. Then, based on linear matrix inequality, the sufficient conditions for the design of coordinated state feedback stabilizing controller and cooperative state feedback guaranteed cost controller are given, and the validity of the proposed method is proved by Lyapunov stability theory. Finally, simulation examples show the effectiveness and superiority of the pro-posed algorithm. [ABSTRACT FROM AUTHOR]

In traditional power systems, the positive sequence fault component (PSFC) based criteria is often used to identify the fault directions. If the PSFC impedance angle of the inverter‐interfaced distributed generator (IIDG) is between 0 and 90°, the traditional PSFC‐based criteria will remain applicable in active distribution networks (ADNs). However, due to the diversity of the IIDG fault control mode and the uncertainty of the positive sequence voltage variation caused by faults, the PSFC impedance angle of IIDG is difficult to determine. To solve this problem, this paper proposes a two‐stage fault control scheme for IIDG. At different stages, IIDGs select different power references to meet different requirements. In the first stage, the PSFC impedance angle of IIDG is fixed to a given value, thereby maintaining the applicability of the PSFC‐based criteria in ADN. The detailed power reference calculation method of IIDG is given for this stage. In the second stage, IIDGs can select different fault control modes to support the fault voltage flexibly. By using the proposed two‐stage IIDG fault control scheme, the protection devices can accurately identify the fault direction in ADNs, which is verified by the MATLAB simulation results. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In this article, the cooperative fuzzy hierarchical fault-tolerant containment control (FTCC) method is proposed for nonlinear multiagent systems (MASs) over switching directed topologies. Based on the hierarchical mechanism, the problem of the cooperative FTCC for nonlinear MASs under switching directed topologies is decoupled into the cooperative containment control problem of the virtual reference system (upper layer) under switching directed topologies and the tracking control problem of the actual system (lower layer). Due to the existence of switching topologies, the state of the virtual reference system may jump, which further affects the transient performance of the lower tracking system; moreover, the actuator faults may also cause a sudden change in actual system. In order to mitigate the influence of switching topologies and actuator faults on the lower tracking system, the generalized restricted potential function is introduced to ensure the norm of the lower tracking error within the predefined performance function. Finally, a simulation example is introduced to illustrate the theoretical results obtained. [ABSTRACT FROM AUTHOR]

We have been conducting research and development to implement the intent AI (artificial intelligence) mediator called Mintent, a technology concept that enables cooperation among application control, network control, and cloud server control on the basis of user requirements (intent) to increase user satisfaction. To implement Mintent, we propose a technology to enable cooperation between application control and network control by using intent as a common index for video-streaming services. This article provides an overview of this technology and discusses its effectiveness determined through simulation. [ABSTRACT FROM AUTHOR]

This article introduces the intent AI (artificial intelligence) mediator called Mintent that coordinates and cooperatively controls networks, cloud servers, and applications on the basis of the respective requirements (intents) of service providers and users. This technology can be used to provide services on the basis of such intents. [ABSTRACT FROM AUTHOR]

Copyright of Aero Weaponry is the property of Aero Weaponry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Featured Application: This study introduces ramp control into parking management and proposes intelligent control strategies for vehicle departure in urban complex parking lots. Specifically, in these strategies, the frequency of gate lever lift is optimized using timing control and inductive control. The proposed intelligent control strategies are applied in the Jinding area in Shanghai, China, with large underground parking lots. Compared to non-controlled situations, the driving efficiency of the ground-level traffic is improved. The entrances and exits of underground parking lots of large complexes are the key nodes for the conversion between ground-level dynamic traffic and underground static traffic. Since congestion is caused by a large number of vehicles leaving parking lots at peak hours, the departure control strategy can effectively manage vehicle departure and reduce the congestion of ground-level traffic. In this study, we introduce cooperative control in ramp control into parking lot exit management. The frequency of parking lot exit gate lever lift is used as the control and optimization variable. To ensure the efficiency of regional traffic, we designed timing and inductive control strategies to control the speed of departing vehicles. In an experimental model, we took Shanghai Jinding super-large underground parking lot as an example. The changes in the external road network were simulated when different strategies were implemented on the Simulation of Urban Mobility (SUMO) simulation platform. The experimental results show that the proposed control strategies can significantly ease the congestion of the regional road network, improve the average speed of dynamic traffic, and reduce the queue length at intersections. [ABSTRACT FROM AUTHOR]

When an unexpected obstacle occupies some of the lanes on a multi-lane highway, connected vehicles (CVs) may be able to avoid it cooperatively. For example, a CV that detects the obstacle first can immediately notify the following vehicles of the obstacle by using vehicle-to-vehicle (V2V) communication. In turn, the following vehicles can take action to avoid the obstacle smoothly using wide range behind the obstacle without sacrificing safety and ride comfort. In this study, we propose a method to realize safe, smooth, and fair wide-range cooperative lane changing, reacting to a sudden obstacle on the road. The proposed method is based on the authors' previous work, which utilizes multi-hop communication to share the obstacle position and controls the inter-vehicular distance of vehicles away from the obstacle to assist in a smooth lane changing operation, while existing lane-changing methods for CVs focus on microscopic operation around the obstacle. Though the previous work treats only a two-lane road, the proposed method is extended to work on a three- or more lane road assuming only one lane is blocked. In the proposed scheme, each vehicle approaching the obstacle selects a lane to change to in accordance with the obstacle's location and the vehicle density in each lane estimated from the beacon messages broadcast by each CV, thereby improving traffic fairness among all lanes without degrading safety or ride comfort. We confirmed the effectiveness of the proposed scheme on realizing fairness among lanes, safety, ride comfort, and traffic throughput through comprehensive simulations of a two-lane road and a three-lane road with various traffic scenarios. [ABSTRACT FROM AUTHOR]

This article proposes an optimal-distributed control protocol for multivehicle systems with an unknown switching communication graph. The optimal-distributed control problem is formulated to differential graphical games, and the Pareto optimum to multiplayer games is sought based on the viability theory and reinforcement learning techniques. The viability theory characterizes the controllability of a wide range of constrained nonlinear systems; and the viability kernel and the capture basin are the pillars of the viability theory. The capture basin is the set of all initial states, in which there exist control strategies that enable the states to reach the target in finite time while remaining inside a set before reaching the target. In this regard, the feasible learning region is characterized by the reinforcement learner. In addition, the approximation of the capture basin provides the learner with prior knowledge. Unlike the existing works that employ the viability theory to solve control problems with only one agent and differential games with only two players, the viability theory, in this article, is utilized to solve multiagent control problems and multiplayer differential games. The distributed control law is composed of two parts: 1) the approximation of the capture basin and 2) reinforcement learning, which are computed offline and online, respectively. The convergence properties of the parameters’ estimation errors in reinforcement learning are proved, and the convergence of the control policy to the Pareto optimum of the differential graphical game is discussed. The guaranteed approximation results of the capture basin are provided and the simulation results of the differential graphical game are provided for multivehicle systems with the proposed distributed control policy. [ABSTRACT FROM AUTHOR]