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To improve the disadvantages of signal control and energy control, a cooperative control strategy combining signal control and energy control is proposed for manipulators driven by permanent magnet synchronous motors (PMSMs) in this article. The cooperative control strategy is achieved by way of the convex combination of signal controller and energy controller, and Gaussian function is selected as cooperative function. The energy controller applies variable damping error port Hamiltonian and the signal controller uses backstepping nonsingular terminal sliding mode control. The PMSM with loss model is applied to obtain higher efficiency. To deal with modeling errors and joint friction, a nonlinear disturbance observer is used for manipulators system. Simulation and experiment results display that the designed strategy not only increases efficiency, but also enhances dynamic and steady‐state performance of manipulators. [ABSTRACT FROM AUTHOR]

The optimal leaderless and leader-following time-varying formation (TVF) control problems for second-order multiagent systems (MASs) are investigated, where two optimal TVF control protocols are proposed to achieve the desired formations as well as minimize the comprehensive optimization function that contain the cooperative performance index and the control energy index. For leaderless case, the optimal formation control problem is reformulated as an infinite-time state regulator problem by employing the state space decomposition method, which is subject to specified constraints on energy and performance indices, and the analytic criterion for optimal TVF achievability is subsequently proposed. Then, the results of optimal leaderless TVF control are extended to the leader-following case with switching topologies, where the main challenge is changed to find the optimal controller rather than the optimal gain matrix, and the optimal value of the comprehensive index is accurately determined. Finally, two simulation cases are proposed to validate the effectiveness of the theoretical results, and comparisons with previous works are presented to expound the optimality of the proposed formation control method. • The specific gain matrix of optimal leaderless/leader-following formation is deduced, which can not only make the multiagent system achieve time-varying formation control, but also minimize the value of the objective optimization function. • The relationships among the gain matrix, the Laplacian matrix, the regulation parameters and the minimum value of the objective optimization function are revealed. • Sufficient conditions for optimal time-varying formation achievability are proposed, which are analytic. [ABSTRACT FROM AUTHOR]

The formation transformation in intelligent connected autonomous vehicles (CAVs) enhances platoon versatility and significantly improves traffic efficiency. Current formation control strategies for CAV platoons often focus on fixed formation scenarios. This paper proposes a three-layer architecture for platoon reconfiguration, encompassing discrete, offline, and online layers. CAV platoons utilize this architecture to transform their existing formation into a specified target formation from the Intelligent Transportation System (ITS). In the discrete layer, we propose a formation representation scheme and design A* and cooperative sorting algorithms to achieve the optimal intermediate formation sequence. Moving to the offline layer, we design a Signal Temporal Logic-based model predictive control algorithm (MPC). This algorithm plans continuous, dynamically feasible, and collision-free safe trajectories, which are stored in an offline trajectory database. In the online layer, we design a successive linearization-based MPC to track the offline trajectories in real-time traffic environments and accomplish the platoon reconfiguration task. We implement single-lane and multi-lane platoon reconfiguration tasks in the MATLAB platform, comparing them with two advanced platoon reconfiguration algorithms. The experimental results, demonstrating the effectiveness of the proposed approach, are presented and discussed. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Jiaotong University (Science) is the property of Springer Nature 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.)

Purpose: Skullbase surgery demands exceptional precision when removing bone in the lateral skull base. Robotic assistance can alleviate the effect of human sensory-motor limitations. However, the stiffness and inertia of the robot can significantly impact the surgeon's perception and control of the tool-to-tissue interaction forces. Methods: We present a situational-aware, force control technique aimed at regulating interaction forces during robot-assisted skullbase drilling. The contextual interaction information derived from the digital twin environment is used to enhance sensory perception and suppress undesired high forces. Results: To validate our approach, we conducted initial feasibility experiments involving a medical and two engineering students. The experiment focused on further drilling around critical structures following cortical mastoidectomy. The experiment results demonstrate that robotic assistance coupled with our proposed control scheme effectively limited undesired interaction forces when compared to robotic assistance without the proposed force control. Conclusions: The proposed force control techniques show promise in significantly reducing undesired interaction forces during robot-assisted skullbase surgery. These findings contribute to the ongoing efforts to enhance surgical precision and safety in complex procedures involving the lateral skull base. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Guangdong University of Technology is the property of Journal of Guangdong 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.)

This paper focuses on a time-varying distributed convex optimization problem for multi-agent systems, with the objective of preserving their desired rigid formation. The core challenge is driving all agents to minimize the global objective function cooperatively with limited information each agent can share. To tackle the dual objective problem, here we propose a single-integrator and a double-integrator dynamic systems to solve the distributed optimization problem with desired shape achievement. It is proved that all agents can achieve the rigid formation while optimizing the global objective function asymptotically in two algorithms by using the Lyapunov theorem. Finally, the good performance of the control algorithms is demonstrated through numerical experiments and computer simulations. [ABSTRACT FROM AUTHOR]

This article presents a cooperative controller that is specifically designed to enhance the stability of a distributed-drive vehicle during steering. The controller focuses on improving lateral stability during steering and achieving optimal torque allocation to meet numerous objectives. The article proposes a novel approach to improve the performance of the sliding mode controller for transverse stability control during steering. This is achieved by designing a fractional-order non-singular fast terminal sliding mode surface function, a fractional-order double-power exponential convergence law, and introducing a weighted integration term. Furthermore, the vehicle's torque was fine-tuned by employing an ant colony optimization (ACO) technique within the acceptable range defined by the lateral and longitudinal control requirements. To prevent the ACO algorithm from being stuck in local optima, a pseudo-random rule was implemented based on the original state transfer probability. This rule helps accelerate the convergence of the algorithm. Additionally, an elite approach and a dynamic change strategy for pheromone concentration were devised. Ultimately, the performance of the co-controller that was built is evaluated by simulation experiments conducted under both accelerated and decelerated driving situations. The test findings indicate that the technique effectively improves the lateral stability, tracking control, and energy economy of electric cars, with promising potential for practical use. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of managing state constraints in vehicle platoons, including maintaining safe distances and aligning velocities, which are key factors that contribute to performance degradation in platoon control. Traditional platoon control strategies, which rely on a constant time-headway policy, often lead to deteriorated performance and even instability, primarily during dynamic traffic conditions involving vehicle acceleration and deceleration. The underlying issue is the inadequacy of these methods to adapt to variable time-delays and to accurately modulate the spacing and speed among vehicles. To address these challenges, we propose a dynamic adjustment neural network (DANN) based cooperative control scheme. The proposed strategy employs neural networks to continuously learn and adjust to time varying conditions, thus enabling precise control of each vehicle’s state within the platoon. By integrating a DANN into the platoon control system, we ensure that both velocity and inter-vehicular spacing adapt in response to real-time traffic dynamics. The efficacy of our proposed control approach is validated using both Lyapunov stability theory and numeric simulation, which confirms substantial gains in stability and velocity tracking of the vehicle platoon.

Aiming at the problem of multi-UAV pursuit-evasion confrontation, a UAV cooperative maneuver method based on an improved multi-agent deep reinforcement learning (MADRL) is proposed. In this method, an improved CommNet network based on a communication mechanism is introduced into a deep reinforcement learning algorithm to solve the multi-agent problem. A layer of gated recurrent unit (GRU) is added to the actor-network structure to remember historical environmental states. Subsequently, another GRU is designed as a communication channel in the CommNet core network layer to refine communication information between UAVs. Finally, the simulation results of the algorithm in two sets of scenarios are given, and the results show that the method has good effectiveness and applicability.

Coal pillars in soft and thick coal seam are often tall and have large exposed coal walls, and the coal quality is soft with some developed cracks, which can easily cause an instability of coal pillars and make roadway maintenance difficult. This paper takes the No. 3 coal seam in the Fuyan Coal as the engineering background, based on the shear sliding block theory, explores the mechanism of coal pillar deformation and failure under mining influence, analyzes the width of the coal pillar’s limit equilibrium zone and stress distribution rules, and reveals the movement mechanism of the coal pillar’s shear sliding block. Based on this, the cooperative control strategies for coal pillar stability are proposed and verified through engineering practice. The results show that: ① by using the limit equilibrium theory and the superposition theory, the range of shear sliding block movement of the coal pillar and the vertical stress distribution law of the coal pillar are determined, and the distribution law of the safety factor of the coal pillar shear sliding block is clarified. The safety factor of the upper part of the coal pillar is small at a depth of 0−1.26 m. In the area of 1.26−3.95 m, the central part of the coal pillar has a large area of small safety factor and is vulnerable to damage from the roof. ② A cooperative control technology for coal pillar is proposed, which is based on “reinforcement anchor cable-grouting reinforcement-advance cutting roof”, and it effectively controls the deformation and bottom bulging of the coal pillar. The side cracks of the coal pillar without supporting conditions and those with original supporting conditions are reduced by 62.89% and 46.26%, respectively. The integrity of the surrounding rock of the roadway is greatly improved, forming a strong bearing structure. ③ According to the conditions of coal pillar in the thick and soft coal seam section, the design parameters of coordinated control are reasonably determined, and the prevention and control effect of coal pillar is evaluated by test monitoring. The field test results show that the cracks in coal pillar are fully filled, and the strength of coal body is increased by more than 63% after grouting. The roadway displacement, anchor cable stress, and separation layer are all within the controllable range, indicating that the cooperative control technology significantly improves the bearing capacity of the coal pillar, and effectively controls the deformation of the surrounding rock of the mine roadway, providing space guarantee for safe and efficient mining.

Abstract Transit operation efficiency and service quality can be enhanced through the implementation of signal and speed control. Previous studies prefer to change driving speed in priority to alleviate the adverse effects of signal timing adjustment on social vehicles. The driving safety and fuel consumption of transit are ignored. To this end, a cooperative control method consisting of three models is proposed. The cooperative control strategy model provides optimal schemes for allocating transit priority time. Based on this, the adjustment of phase time and the transit speed trajectory with the lower fuel consumption are calculated by signal control model and speed control model, respectively. Especially, the signal control model is established in the background of green wave coordinated control to further protect the travelling benefits of social vehicles. The simulation is performed in SUMO to demonstrate the effectiveness of the proposed method. The results show that the cooperative control method improves the crossing efficiency and enhances the fuel economy of transit under different arrival speeds and lengths of control area. Compared with the general signal control, the proposed method can minimize traffic interference, which is particularly obvious in a higher degree of saturation.

Copyright of Power Generation Technology is the property of Power Generation Technology 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.)

This paper proposes a novel free-weighting matrix (FWM) approach for the event-triggered (ET) consensus and formation control of multi-agent systems over a directed graph. A new FWM equality for the consensus error is proposed. This FWM is applied to attain coherent behavior of systems via a fully distributed ET approach. The proposed approach is also extended for ET formation control to attain a specific pattern of agents. Furthermore, an FWM-based consensus controller design with practical ET cooperative control is considered to address consensus and formation control problems by using the in-degree information of a node rather than the algebraic connectivity of a graph or adaptation of parameters. Additionally, the controller gain is formulated using a more general transformation compared to that used in classical approaches. Moreover, the proposed ET scheme for the designed controller is applied on the transmission side to preserve computational resources by eliminating the Zeno behavior. In contrast to the existing methods, the proposed approach (i) provides a fully distributed protocol without using central information of network or adaptive gain; (ii) provides a practical ET mechanism to assure effective bandwidth and fewer energy consumption; and (iii) can be applied to the ET formation problem for mobile agents. Simulations involving six aerial vehicles are performed to demonstrate the efficacy of the proposed cooperative control strategy. [ABSTRACT FROM AUTHOR]

In today's cyber–physical microgrid systems, the consensus-based secondary control is generally utilized to settle the voltage deviation and rough current allocation issues at the primary control level. However, time delays follow inevitably the introduction of sparse communication networks, and most existing works adopt passive tolerance approaches. To actively alleviate the unavoidable delay effect in microgrids' communication networks, a networked predictive control (NPC) strategy is proposed for an islanded DC microgrid subject to time delays in this paper. Firstly, the predictive approaches for both voltage and current are developed based on the cyber–physical microgrid model. Unlike the practice of passively tolerating time delays, the NPC strategy is proposed to actively compensate for the effect of communication delays by estimating real-time voltage and current values using the previously obtained prediction models. Moreover, to prove the generality of the developed method, the microgrid systems' stability can be derived from the Schur stability of the closed-loop system, thus the DC microgrid can achieve voltage regulation and proportional current sharing simultaneously. Finally, the performance of our method against the time delay effect is validated by extensive experiments on an islanded 48-V DC microgrid system, in terms of its feasibility, delay tolerance ability, and robustness to load changes and communication faults. Experimental results demonstrate the effectiveness and superiority of the NPC strategy. • To actively compensate delays, a networked predictive control strategy is proposed. • Unlike passive schemes, a proactive multi-step prediction mechanism is developed. • The NPC method is demonstrated to have certain universality and generalizability. [ABSTRACT FROM AUTHOR]

This article investigates distributed optimization of high‐order nonlinear multi‐agent systems (MASs) with disturbance under switching topologies. First, we propose a class of new distributed control algorithms for high‐order MASs under switching topologies, such that all agents asymptotically reach consensus at the unique minimizer of the sum of cost functions. Then, by constructing disturbance observer, the proposed algorithms are extended to address distributed optimization problems for high‐order nonlinear MASs with disturbance under switching topologies. In particular, both of new presented methods do not require optimal reference signal generator, which can save the computational resources of systems. In final, simulations are conducted to validate the theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel coordinated method tailored to address the dynamic multi-target hunting control problem in multi-agent systems, offering significant practical value. Our approach encompasses several key components: initially, we introduce a task allocation model that integrates a fuzzy inference system with a particle swarm optimization algorithm. This hybrid model efficiently allocates hunting tasks for scattered evading targets, effectively transforming the dynamic multi-target hunting problem into multiple dynamic single-target-hunting problems. This transformation enhances the speed and efficacy of task allocation. Subsequently, we propose an attraction/repulsive model grounded in potential field theory. This model facilitates the coordinated hunting of each target by organizing agents into subgroups. Relying solely on relative position and velocity information between agents and targets, our model simplifies computation, while maintaining effectiveness. Furthermore, the coordination of hunting activities for each target is achieved through a series of agent subgroups, guided by our proposed motion model. This systematic approach ensures a cohesive and efficient hunting strategy. Finally, we validate the effectiveness and feasibility of our proposed method through simulation results. These results provide empirical evidence of the method's efficacy and potential applicability in real-world scenarios. [ABSTRACT FROM AUTHOR]

Transit operation efficiency and service quality can be enhanced through the implementation of signal and speed control. Previous studies prefer to change driving speed in priority to alleviate the adverse effects of signal timing adjustment on social vehicles. The driving safety and fuel consumption of transit are ignored. To this end, a cooperative control method consisting of three models is proposed. The cooperative control strategy model provides optimal schemes for allocating transit priority time. Based on this, the adjustment of phase time and the transit speed trajectory with the lower fuel consumption are calculated by signal control model and speed control model, respectively. Especially, the signal control model is established in the background of green wave coordinated control to further protect the travelling benefits of social vehicles. The simulation is performed in SUMO to demonstrate the effectiveness of the proposed method. The results show that the cooperative control method improves the crossing efficiency and enhances the fuel economy of transit under different arrival speeds and lengths of control area. Compared with the general signal control, the proposed method can minimize traffic interference, which is particularly obvious in a higher degree of saturation. [ABSTRACT FROM AUTHOR]

This research focuses on a cooperative control problem for networked multi-agent systems (NMASs) under time-variant communication constraints (containing time-variant communication delays and time-variant data losses) in the forward and feedback channels. From the perspective of high-order fully actuated (HOFA) system theory, a HOFA system model is adopted to describe the NMAS, which is called the networked HOFA multi-agent system (NHOFAMAS). Because of complicated working scenarios over the network, the states of NMASs are immeasurable and the communication constraints are always present, such that an observer-based HOFA predictive control (OB-HOFAPC) method is designed to implement the cooperative control when existing the immeasurable states and time-variant communication constraints. In this method, a HOFA observer is established to estimate the immeasurable states for constructing a consensus control protocol. Then, an incremental prediction model (IPM) in a HOFA form is developed via a Diophantine equation to take the place of a reduced-order prediction model. Through this IPM, multi-step output ahead predictions are derived to optimize the cooperative control performance and compensate for time-variant communication constraints in real-time. The depth discussion gives a sufficient and necessary criterion to analyze the simultaneous consensus and stability for closed-loop NHOFAMASs. The capability and advantage of OB-HOFAPC method are illustrated via numerical simulation and experimental verification on a cooperative flying-around task of three air-bearing spacecraft simulators. • The dynamic model of NMAS is described in a HOFA form to reduce the design process. • A Diophantine equation is used to construct an IPM in HOFA form for predictive control. • The observer is designed by using a HOFA form to ensure the full actuation feature. [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.)

The starter generator, characterized by controllable starting torque and disturbance in generator load torque, poses challenges for the multi-electric aero engine control. The key to addressing this issue lies in multi-electric aero engine control with the collaboration of a starter generator. Firstly, a multi-electric aero engine model is established, comprising a full-state turbofan engine model to enhance low-speed simulation capability and an external characteristic model of a starter generator to improve real-time simulation capability. Subsequently, the control methods for a multi-electric aero engine with starter generator coordination are proposed in three processes, including the starting process, acceleration/deceleration process, and steady-state process. During the starting process, the acceleration is controlled by coordinating the torque of the starter generator and the fuel of the aero engine. During the acceleration/deceleration process, the fuel limit value is adjusted based on the electrical load of the starter generator. During the steady-state process, the fuel is compensated based on the q-axis current of the starting generator in response to load torque disturbance. Finally, hardware-in-the-loop simulation experiments are conducted for the control of a multi-electric aero engine. The results show that the coordination reduces the oscillation of the acceleration during the startup of a multi-electric aero engine, enhancing its ability to resist disturbances from electrical load fluctuations during power generation. [ABSTRACT FROM AUTHOR]

The mixed planting, as one of the diversification strategies, provides the substitute for many costly inputs, such as fertilizers, pesticides, imported pollinators and irrigation. However, the lack of the farming technology and the expansion of the farming scale have resulted in extremely mismatch between desired yield and manpower. This paper considers groups of agents orderly moving into precise regions, which is suitable for simultaneously planting of different crops. The hierarchical containment control strategy with group-aggregation behavior is proposed under the transformed three-layer topology. Particularly, the dynamic hierarchical containment control protocol is designed such that a novel group-aggregation cooperation in the convex hull can be achieved. A new coupling strength coefficient based on the transformed three-layer topology is defined in the proposed control protocol. Finally, two examples are provided to demonstrate the effectiveness of the proposed dynamic hierarchical containment control protocol for agricultural settings. [ABSTRACT FROM AUTHOR]

Multiple DC microgrids are interconnected to form a DC microgrid cluster, which can effectively improve the renewable energy consumption capacity and power supply reliability of the microgrid cluster through sharing of energy storage among the participating DC microgrids. However, the existing methods are not able to flexibly coordinate the power flows between DC microgrids connected by a multiport converter. In this paper, a distributed cooperative control method is proposed for a DC microgrid cluster with multiple voltage levels connected by a multi-port interconnected converter. A multi-active-bridge (MAB) converter acts as a bridge for power exchange among microgrids, enabling the sharing of distributed energy storage among microgrids under a sparse communication network. The improved secondary control overcomes the drawbacks of droop control and achieves deviation-free voltage regulation and accurate load/source sharing of the energy storage converters within the cluster. The proposed secondary control can be used to retrofit the existing DC distribution grids to permit access of renewable energy. In addition, the improved voltage observer provides the voltage information of other nodes in a microgrid in addition to the energy storage information, reducing the voltage estimation error caused by the uneven distribution of energy storages. Finally, hardware-in-the-loop (HiL) experiments of the cluster with three DC microgrids verify the control performance, the flexible plug-and-play capability, and the high tolerance of communication failure.

As intelligent technologies and unmanned systems develop rapidly, the concept of cross-domain cooperative technology of intelligent unmanned swarm systems has emerged, received widespread attention, and gradually become the high ground in the competition of unmanned system technologies among countries worldwide. Based on the development demand for the cross-domain cooperative technology of intelligent unmanned swarm systems in China, this study summarizes the research status of the crossdomain cooperative technology in typical unmanned scenarios such as sea – air, air – ground, and sea – ground/sea – ground – air, and thoroughly analyzes the current status, technological demand, and key research directions of the technology. Additionally, countermeasures and suggestions are proposed to promote the steady and rapid development of the cross-domain cooperative technology from the perspectives of overall concept, system architecture, theoretical innovation, and technological breakthroughs, with the aim of facilitating the sustained development of unmanned systems in China.

This paper considers the distributed cooperative control problem for a linear stochastic multi-agent system (MAS). The optimal cooperative control design for each agent is challenging due to the limited neighboring information, contingent upon the MAS network topology. A synthesized distributed control-estimation framework is proposed to address the computationally tractable suboptimal solution. In particular, a distributed estimator extends MAS information beyond neighboring agents, allowing interactions with non-neighboring agents. The proposed control-estimation law is theoretically verified and further validated using numerical simulations.

Energy management strategies (EMSs) for hybrid vehicles have been extensively studied to achieve high system efficiency. EMSs usually focus on the torque split between the electric motor and the main power source. Other powertrain components, such as the gearbox or battery management system, are optimized individually. However, the cooperation between different powertrain components has been studied for specific hybrid architectures and demonstrated to be highly beneficial. A modular EMS that ensures the cooperation of multiple components with different characteristics, shared constraints and objectives, while taking advantage of predictive information will be highly beneficial. To address this research gap, a modular cooperative EMS is proposed using parametric controllers with parameter updates realized in the background using available predictive information. The strategy emphasizes modularity, feasibility, and systematically takes advantage of any available predictive information to improve the overall vehicle objectives, hence considering all the components playing a role in the EMS. The proposed cooperative strategy is first detailed for a generic EMS and then demonstrated for the control of the torque split and gear selection of a hybrid electric vehicle. A numerical study is presented to compare the proposed method with the optimal strategy derived from dynamic programming. The results are detailed for different available predictive information, both in terms of quantity and quality. The proposed method is revealed to be robust against incomplete predictive information and guarantees feasibility with low computational effort, making it real-time capable.

The separation of transmission and distribution systems raises a variety of questions concerning the integration of many distributed generators (DGs) into future grids. They are difficult to solve by using current energy management methods. Especially, the cooperative control between transmission system operator (TSO) and distribution system operator (DSO) has been increasingly emphasized to manage the line congestion. This paper proposes a new cooperative control of TSO-DSO based on the generation-load power sensitivity analysis. To minimize the required computational effort and data communication, the information of TSO and DSO is processed separately in the generation-load power sensitivity matrix of power system. The proposed cooperative control is implemented by three-step process, which is aggregation, specification, and local distribution. Firstly, the DSOs aggregate the flexibility area of DGs in their networks, and they inform the TSO of the feasible operation regions (FORs). Then, the TSO sends the power references at the boundary buses to many DSOs. Thereafter, the DSOs use these references as loads to calculate the detailed power references for their generators. As the result, the net power of DSOs satisfies the power references requested by the TSO. The case studies are carried out to verify the effectiveness of proposed control. The results show that when the load is increased by 20%, the overall average of line loading is decreased by 4.34% with the proposed control. Also, when the generator of 634 MW is disconnected, the frequency nadir is increased by 0.1 Hz compared with the $P$ - $f$ droop control.

This paper introduces a distributed secondary control scheme for achieving current sharing and average voltage regulation objectives in a DC microgrid. The proposed scheme employs a dynamic diffusion algorithm (DDA) instead of the consensus algorithm to enable distributed communication among converters. To help understand DDA, the relation of DDA and other diffusion algorithms is discussed in detail and its superiority is shown by comparison with diffusion and consensus algorithms. Furthermore, considering the discrete nature and different sampling time of the digital controller and communication network, a z-domain model of the entire DC microgrid is established. The influence of communication and secondary control parameters on the system stability is investigated. Based on the established model, the tolerable communication rates are obtained. Real-time simulations conducted on the OPAL-RT platform validate the effectiveness of the proposed scheme, showcasing its advantages in terms of convergence speed and stability.

Frequency regulation of voltage source converter-based multi-terminal high-voltage direct current (VSC-MTDC) system with offshore wind farms enhances the frequency stability by compensating the power for a disturbed AC system. However, it is difficult to reasonably allocate frequency-regulation resources due to a lack of coordination mechanisms between wind farms and the MTDC system. Moreover, it is difficult for the frequency control of the wind farms to manage changes in wind speed; and the risk of wind-turbine stalls is high. Thus, based on the kinetic energy of wind turbines and the power margin of the converters, the frequency-regulation capability of wind turbines is evaluated, and a dynamic frequency-support scheme considering the real-time frequency-support capability of the wind turbines and system frequency evolution is proposed to improve the frequency-support performance. A power adaptation technique at variable wind speeds is developed; the active power in the frequency-support stage and restoration stage is switched according to the wind speed. A hierarchical zoning frequency-regulation scheme is designed to use the frequency-regulation resources of different links in the MTDC system with wind farms. The simulation results show that the novel frequency-regulation strategy maintains frequency stability with wind-speed changes and avoids multiple frequency dips.

The cooperative adaptive tracking control based on full states quantization is investigated in this paper for multiple uncertain surface vessel systems. Firstly, an uncertain surface vessel system model based on disturbance accelerations is presented. Next, using the quantized system states, a series of cooperative tracking controllers and adaptive laws are constructed such that the closed-loop system of every follower is practically stable and the tracking error between every follower and the leader can be adjusted arbitrarily small by parameter adjustment technique. Finally, a simulation example is given to verify the effectiveness of the proposed control strategy.

To enhance the path tracking capability and driving stability of intelligent vehicles, a controller is designed that synergizes active front wheel steering (AFS) and direct yaw moment (DYC), specifically tailored for distributed-drive electric vehicles. To address the challenge of determining the weight matrix in the linear quadratic regulator (LQR) algorithm during the path tracking design for intelligent vehicles on conventional roads, a genetic algorithm (GA)-optimized LQR path tracking controller is introduced. The 2-degree-of-freedom vehicle dynamics error model and the desired path information are established. The genetic algorithm optimization strategy, utilizing the vehicle's lateral error, heading error, and output front wheel steering angle as the objective functions, is employed to optimally determine the weight matrices Q and R. Subsequently, the optimal front wheel steering angle control (AFS) output of the vehicle is calculated. Under extreme operating conditions, to enhance vehicle dynamics stability, while ensuring effective path tracking, the active yaw moment is crafted using the sliding mode control with a hyperbolic tangent convergence law function. The control weights of the sliding mode surface related to the center-of-mass lateral declination are adjusted based on the theory of the center-of-mass lateral declination phase diagram, and the vehicle's target yaw moment is calculated. Validation is conducted through Matlab/Simulink and Carsim co-simulation. The results demonstrate that the genetic algorithm-optimized LQR path tracking controller enhances vehicle tracking accuracy and exhibits improved robustness under conventional road conditions. In extreme working conditions, the designed path tracking and stability cooperative controller (AFS+DYC) is implemented to enhance the vehicle's path tracking effect, while ensuring its driving stability. [ABSTRACT FROM AUTHOR]