Cooperative Control Strategy Research Articles

An online cooperative control strategy for enhancing microgrid participation into electricity market

This paper develops a cooperative control methodology for the online energy management of grid-connected microgrids. The main aim of this methodology is to actively manage the active power outputs of all dispatchable energy sources available within the microgrid as well as the power exchanged with the utility grid so as to match the total load demand at the minimum operating cost. This implies to solve a constrained multi-objective dynamic optimization problem aimed at minimizing the total microgrid operating costs and ensuring the real-time balance within the microgrid in compliance with its technical-operational constraints. Lyapunov's theorem using sensitivity theory is adopted to solve this optimization.To test the performances of the proposed control methodology, several computer simulations corresponding to different operating scenarios have been conducted on the PrInCE Lab experimental microgrid built at the Polytechnic University of Bari.

Failure Characteristics and Cooperative Control Strategies for Gob-Side Entry Driving near an Advancing Working Face: A Case Study

Gob-side entry driving near an advancing working face can improve the recovery rate of coal resources and keep the balance between mining and development. However, the large displacement of the gob-side entry caused by the mining dynamics of abutment pressure challenges the safety and processes of coal mining. This article takes the 15102 tailentry of Xizhang Coal Mine in Changzhi City, Shanxi Province, as an example to study the stability of the coal pillar and the failure characteristics of the surrounding rock and proposes cooperative control strategies of surrounding rock stability. Field tests indicated that when the coal pillar width was 15 m, the displacements of the entry floor, roof, coal pillar side, and solid coal side were 1121 mm, 601 mm, 783 mm, and 237 mm, respectively. A meticulously validated numerical model, incorporating a double-yield model for the gob materials and calibrated parameters, was developed to investigate the stress changes and yield zone distribution across the coal pillar with different sizes. The results of the simulation indicate that the influence range of the dynamic abutment pressure caused by mining in the upper section of gob-side entry driving is 30 m ahead and 70 m behind. When the coal pillar width increases from 7 m to 20 m, the internal stress of the coal pillar increases continuously, while the internal stress of the solid coal decreases continuously. It is estimated that the reasonable coal pillar width should be 7 m, which is subjected to a lower load. The cooperative control strategies comprising a narrow coal pillar, hydraulic fracturing roof cutting for pressure relief, and entry dynamic support were proposed and applied in the 15103 tailentry. The final displacements of the floor, roof, coal pillar side, and solid coal side were 66.01%, 62.06%, 61.05%, and 63.30% lower than that of the 15102 tailentry in the same period, respectively, which effectively controlled the stability of surrounding rock. In addition, this finding for the gob-side entry driving near an advancing working face in this study can potentially be applied to other similar projects.

An artificial potential field approach for event-triggered cooperative control of multiple high-speed trains with free initial states

ABSTRACT In this paper, a multi-algorithm-fusion-based cooperative tracking control strategy is proposed for high-speed trains to achieve the control objectives of reference speed trajectory tracking and adjacent safety distance maintenance simultaneously. First, the multiple high-speed train (MHST) system is formulated as a multi-agent system (MAS) with a virtual leader. Second, a coordination controller that considers the dynamic tracking performance and input saturation is designed by combining the MAS leader-follower consensus algorithm and the improved artificial potential field (APF) method. In addition, within the train-to-train (T2T) communication network, an event-triggered mechanism is introduced in the continuous state transmission process to address the communication channel capacity protection limitation issue. Third, the closed-loop stability of the MHST system is ensured by the comprehensive analysis of a novel log-type Lyapunov function. Finally, the effectiveness of the proposed event-triggered cooperative tracking control strategy is verified through a numerical example of the MHST system on a typical line.

Cooperative control of variable damping error port Hamiltonian and backstepping nonsingular terminal sliding mode control for manipulators driven by PMSMs

AbstractTo 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.

Enhancing vehicular platoon stability in the presence of communication Cyberattacks: A reliable longitudinal cooperative control strategy

This study proposes a reliable longitudinal distributed control strategy for connected and automated vehicles (CAVs) in the presence of communication cyberattacks. The proposed strategy is designed to utilize dependable measurements from onboard sensors to evaluate the reliability of vehicle-to-vehicle (V2V) information and enhance the platoon string stability in an integrated manner. Specifically, this paper proposes a real-time reliability evaluation mechanism for the ego vehicle based on sensing and communication information from multiple predecessors, employing an upper-tailed chi-squared test. The mechanism is then incorporated into a distributed multi-predecessor linear feedback controller to adaptively weigh the preceding vehicles’ information, ensuring robustness against cyberattacks and maintaining the string stability of the vehicular platoon. To better understand the whole framework, sufficient conditions of true string stability and pseudo string stability are theoretically derived, unveiling the disturbance amplification jointly impacted by measurement accuracy of onboard sensors, cyberattack severity in V2V communication, and control parameters. Numerical experiments are conducted to validate the controller across various types of communication cyberattacks. The results suggest that the proposed control strategy can significantly alleviate the impact of cyberattacks and simultaneously dampen traffic oscillations effectively.

Multi‐event‐based distributed cooperative predictive control for the multi‐agent system

AbstractIn this paper, we provide the detailed distributed cooperative predictive control scheme for the multi‐agent system (MAS) affected by the network delays. Firstly, we restate the distributed cooperative predictive control problem by introducing iterative learning‐based MAS, which can guarantee the cooperative predictive control of the MAS. To alleviate the unfavorable network constrains, the multi‐event‐triggered conditions are formulated via considering the predictive and error data sufficiently. With these preparations, the well‐posed Roesser‐type two‐dimensional (2D) system is achieved equivalently to the distributed iterative learning predictive system. On this basis, we employ 2D predictive system analysis and control theory to realize the predictive control for the equivalent 2D system and then obtain the collaborative predictive control for the MAS under multi‐event‐triggered mechanism. The corresponding stability criteria, controller, and observer gains are provided by the executable matrix constraints and algorithms design. To conclude the paper, the numerical example is proposed to illustrate the effectiveness and practicability of the provided methods and algorithms.

Robust cooperative control strategy for a platoon of connected and autonomous vehicles against sensor errors and control errors simultaneously in a real-world driving environment

In a real-world driving environment, a platoon of connected and autonomous vehicles (CAVs) is subject to many internal and external disturbances, resulting in uncertain vehicle dynamics. In general, the disturbances can be categorized into two types: disturbances due to vehicle sensor errors (e.g., GPS error) and disturbances due to vehicle control errors (e.g., actuator delay). In the literature, many control strategies have been proposed to improve the robustness of the CAV platoon against uncertain vehicle dynamics induced by these disturbances. However, most of these strategies only consider one type of disturbance and cannot tackle both types of disturbances simultaneously. Furthermore, they are designed to maximize the benefits of each vehicle in the platoon independently, which can deteriorate the performance of the platoon. To address these problems, this study proposes a robust cooperative control (RCC) strategy to maneuver the vehicles in the platoon cooperatively to counteract the impacts of both types of disturbances. The RCC strategy is developed based on a minimax problem, where the maximization subproblem seeks to find the worst inputs for the uncertainty terms in the vehicle dynamics equation to minimize the platoon performance, while the minimization subproblem seeks to find the optimal control decisions for all subsequent vehicles to maximize the platoon performance in the worst case. To solve the minimax problem, this study proposes a globally convergent solution algorithm. It can solve the minimax problem very efficiently to enable real time deployment of the RCC strategy. Numerical application indicates that compared to the existing methods, the RCC strategy can dramatically improve the robustness of the CAV platoon against the uncertain vehicle dynamics induced by both vehicle state detection errors and vehicle control errors. Therefore, it can maneuver the CAV platoon safely and efficiently in a real-world driving environment.

Mutual Inductance Identification and Bilateral Cooperation Control Strategy for MCR-BE System

Considering that the excitation method of an electric excitation synchronous motor has the disadvantages of the brush and slip ring, this article proposes a new brushless excitation system, which includes two parts: a wireless charging system and a motor. To meet the requirements of maximum transmission efficiency and constant voltage output of the system, a bilateral cooperation control strategy is proposed. For the strategy, the buck converter in the receiving side of the system can maintain maximum transmission efficiency through impedance matching, while the inverter in the transmitting side can keep the output voltage constant through phase shift modulation. In the control process, considering that the offset of coupling coils will affect the control results, a grey wolf optimization–particle swarm optimization algorithm is proposed to identify mutual inductance. Simulation and experimental results show that this identification algorithm can improve the identification accuracy and maximize the avoidance of falling into local optima. The final experimental result shows that the bilateral cooperation control strategy can maintain the output voltage around 48 V and the transmission efficiency around 84.5%, which meets the expected requirements.

Neural-Network-Based Adaptive Fault-Tolerant Cooperative Control of Heterogeneous Multiagent Systems With Multiple Faults and DoS Attacks.

In this article, the issue of adaptive fault-tolerant cooperative control is addressed for heterogeneous multiple unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) with actuator faults and sensor faults under denial-of-service (DoS) attacks. First, a unified control model with actuator faults and sensor faults is developed based on the dynamic models of the UAVs and UGVs. To handle the difficulty introduced by the nonlinear term, a neural-network-based switching-type observer is established to obtain the unmeasured state variables when DoS attacks are active. Then, the fault-tolerant cooperative control scheme is presented by utilizing an adaptive backstepping control algorithm under DoS attacks. According to Lyapunov stability theory and improved average dwell time method by integrating the duration and frequency characteristics of DoS attacks, the stability of the closed-loop system is proved. In addition, all vehicles can track their individual references, while the synchronized tracking errors among vehicles are uniformly ultimately bounded. Finally, simulation studies are given to demonstrate the effectiveness of the proposed method.

Human–Robot Cooperation Control Strategy Design Based on Trajectory Deformation Algorithm and Dynamic Movement Primitives for Lower Limb Rehabilitation Robots

Compliant physical interactions, interactive learning, and robust position control are crucial to improving the effectiveness and safety of rehabilitation robots. This paper proposes a human–robot cooperation control strategy (HRCCS) for lower limb rehabilitation robots. The high-level trajectory planner of the HRCCS consists of a trajectory generator, a trajectory learner, a desired trajectory predictor, and a soft saturation function. The trajectory planner can predict and generate a smooth desired trajectory through physical human–robot interaction (pHRI) in a restricted joint space and can learn the desired trajectory using the locally weighted regression method. Moreover, a triple-step controller was designed to be the low-level position controller of the HRCCS to ensure that each joint tracks the desired trajectory. A nonlinear disturbance observer is used to observe and compensate for total disturbances. The radial basis function neural networks (RBFNN) approximation law and robust term are adopted to compensate for observation errors. The simulation results indicate that the HRCCS is robust and can achieve compliant pHRI and interactive trajectory learning. Therefore, the HRCCS has the potential to be used in rehabilitation robots and other fields involving pHRI.

A frequency cooperative control strategy for multimicrogrids with EVs based on improved evolutionary-deep reinforcement learning

The construction of microgrid has fully promoted the large-scale access of distributed power and the rapid development of electric vehicles (EVs). And microgrid system faces operational issues such as strong random disturbances from distributed power sources and loads, as well as unexpected events during operation. These issues can lead to unstable microgrid frequency, excessive discharge of EVs, and increased control costs. To solve the issues, a frequency cooperative control strategy for multimicrogrids with EVs based on improved evolutionary-deep reinforcement learning (EDRL) is proposed in this paper. First, consider the effect of the vehicle-to-grid (V2G) process on the minimum time required to fully charge an EV, and the impact of the output distribution of micro turbines (MTs) on the regulation cost, and based on the coupling relationship between generator terminal voltage regulation and system frequency control, a multimicrogrid comprehensive control model is constructed. Second, in order to deal with engineering tasks with deceptive rewards and sparse rewards such as integrated frequency control, evolutionary algorithms and deep reinforcement learning algorithms are combined to effectively assist the training process to jump out of local optimal solutions and approach optimal control strategies. Meanwhile, the algorithm is improved based on the novelty search and intelligent partition strategy, so that the strategy has better convergence characteristics and can reduce the cost of information transmission and computational complexity under the premise of ensuring the control effect. Furthermore, the state space, action space and reward function of the controller are defined. Finally, the simulation results show that the proposed controller has the ability of coordinated control, can effectively reduce the adjustment cost of MT unit and the unnecessary discharging of EVs while ensuring the frequency regulation requirements of each submicrogrid, which is far superior to PID control, fuzzy control, and traditional deep reinforcement learning control.

Flight analysis and optimization design of vectored thrust eVTOL based on cooperative flight/propulsion control

The rise of electric vertical take-off and landing (eVTOL) aircraft has sparked research interest, but attention to modeling and evaluation methods, particularly for vectored thrust eVTOL, lags behind. The intricate flight/propulsion coupling and structure/control complexity contribute to the gap. Moreover, integrated modeling and evaluation processes for vectored thrust eVTOL should fully account for the tilting characteristics of the fan/propeller. This paper proposes a flight/propulsion cooperative control modeling and evaluation method for vectored thrust eVTOL. The methodology uses the Gaussian pseudospectral trajectory optimization method to obtain the overall characteristics and cooperative control strategy of flight/propulsion under strict take-off and landing trajectory constraints. Besides, this paper yielded the parameterized analysis and matching optimization design of the propulsion system. Results indicated that for an aircraft with a take-off weight of 1.8 tons, equipped with four 1.6 m diameter tilting ducted fans and a single discharge rate 5C battery, its cruising range is 135.1 km based on the selected technical parameters. The power consumption accounts for merely 12.6% of the total battery capacity during the take-off phase with high-power demand but short duration. By employing a pack of high-power-density and high-energy-density batteries to drive the ducted fans jointly, the aircraft's cruising range, fan size, and payload can be optimized. The 20C+2C battery scheme can either increase the cruising range by 27.3% without changing the fan diameter or increase the cruising range by 6.1% when the fan diameter is reduced to 1.1 m. Wherein without changing the fan diameter, the battery mass can be reduced from 720 kg to 571 kg while satisfying the takeoff and cruising requirements. The results demonstrated the effectiveness of improving the cruising range and optimizing the fan size and payload.

A Versatile Embedded Platform for Implementation of Bio Cooperative Control in Upper-Limb Neuro Motor Rehabilitation Scenario ,

This paper presents a versatile embedded platform designed for the implementation of bio-cooperative control in upper-limb neuromotor rehabilitation scenarios. With an increasing demand for effective rehabilitation techniques, particularly in cases of upper-limb impairments, there is a growing need for innovative technologies that can seamlessly integrate with existing rehabilitation protocols. The proposed platform leverages embedded systems technology to offer real-time feedback and control, facilitating personalized and interactive rehabilitation exercises. By incorporating bio- cooperative control strategies, which enable seamless collaboration between the user and the rehabilitation system, the platform aims to enhance engagement and effectiveness of rehabilitation sessions. The platform's versatility allows it to be easily customized and adapted to different rehabilitation scenarios, making it a promising tool for improving outcomes in upper-limb neuromotor rehabilitation.

In-Flight Calibration of Lorentz Actuators for Non-Contact Close-Proximity Formation Satellites with Cooperative Control

The non-contact close-proximity formation satellite (NCCPFS) is one of the technical solutions to improve the attitude performance, consisting of a payload module (PM) and a support module (SM). The non-contact Lorentz actuator (NCLA), as the core components of the NCCPFS, directly affect the attitude control performance of the entire satellite. In order to ensure the ultra-high attitude pointing performance and stability of the PM, an in-flight calibration method for the NCLAs based on minimum model error (MME) algorithm and Kalman filtering (KF) with cooperative control strategy is proposed in this article. In this method, the NCLAs generate a periodic nominal torque that causes the attitude of the PM to be periodically deflected. This periodic torque also reacts on the SM, and the SM counteracts this periodic torque through the flywheel to realize the cooperative tracking relative to the PM. Then, the gyroscope data are substituted into the MME algorithm to obtain the angular acceleration of the two modules, and the KF algorithm is adopted to observe the actual output torque of the NCLAs to complete the in-flight calibration of the NCLAs. Numerical simulation results show that the accuracy of the proposed calibration algorithm can reach about 8%, which proves the effectiveness of the proposed method.

Arc Length Spacing-Driven Cooperative Elliptical Encircling Control of Robots: Design and Experiments

Most studies of cooperative enclosing control are centered around moving along the shared circle with available position information. Note that Global Positioning System (GPS) is typically inaccessible under hostile scenar- ios, and elliptical circumnavigation may provide a more flexible and high-efficiency entrapping solution, whilst the non-orthogonality of axial and tangential velocities and the lack of GPS enable elliptical entrapping to be more challeng- ing, and the related outcomes are still rare. Following this line, a novel coordinated error in terms of arc length spacing is skillfully formulated and included in elliptical encircling control to characterize cooperative behaviors without trigg- ering frequent speed variation and unstable observation. Particularly, to remedy the dependence on accessible global position, a new bearing-based estimator under a local frame is proposed to recover the relative coordinate, then a modified cooperative elliptical encircling control strategy is formulated without entailing global position data. Lyapunov stability analysis demonstrates that all error variables are ultimately bounded. Both simulations and experiments are implemented to illustrate the efficacy of proposed control algorithm.

Event-Triggered-Based Adaptive NN Cooperative Control of Six-Rotor UAVs With Finite-Time Prescribed Performance

This paper studies the disturbance-observer-based adaptive neural network cooperative event-triggered control problem for six-rotor unmanned aerial vehicles with finite-time prescribed performance. Six-rotor unmanned aerial vehicle systems are divided into the position subsystem and the attitude subsystem. The switching threshold event-triggered mechanism that considers the influence of event triggering on the consensus control performance is designed of each six-rotor unmanned aerial vehicle to save the transmission resources, which excludes the Zeno behavior. Neural networks are introduced for estimating the uncertainties and solving the algebraic loop problem. In addition, for the position subsystem, by combining the prescribed performance with the velocity function, a finite-time control scheme is proposed, which can guarantee that the consensus errors converge to a prespecified neighborhood of the origin, and all closed-loop signals are bounded. Based on the designed control input signal of position subsystem, an adaptive neural network event-triggered control mechanism is designed to stabilize the attitude subsystem. Finally, some verification results are given to test the rationality of the proposed control strategy. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The purpose of this paper is to design an event-triggered cooperative control scheme to save the transmission resources for six-rotor unmanned aerial vehicle systems with performance limitations and lumped disturbances. In practical applications, if the impact of the event-triggered mechanism on the system performance is not taken into account, the system performance may be significantly degraded while the information transmission resource is saved. Thus, this paper designs an event-triggered mechanism that considers the system performance, which can effectively weigh the relationship between system performance and transmission resource consumption and better meet the practical requirements. Moreover, a finite-time specified performance control strategy is introduced to avoid the problem of difficult determination of residual set and improve the transient-state and steady-state performances of unmanned aerial vehicle systems, which is more in line with the actual demand.

A novel redundant cooperative control strategy for robotic pollination

This paper proposes a novel Redundant Cooperative Control (RCC) strategy to address the pollination sequence planning problem for a mobile pollination robot in the greenhouse. Firstly, the RCC strategy divides the robot’s workspace into subspaces based on the distribution of flowers through clustering. Subsequently, the RCC strategy plans the traveling salesman problem within the visual field of these subspaces, seeking a compromise solution for an optimal path. Finally, the RCC strategy utilizes the redundant motion capabilities of the mobile chassis and robotic arm to accomplish the pollination task during the movement. The RCC strategy exploits the available redundancy in both the agricultural robot’s mobile platform and end-effector, carefully balancing computational complexity and path quality. To validate the effectiveness of the RCC strategy, we designed a series of experiments conducted on our pollination robot platform, measuring different strategies. The results demonstrate that the RCC strategy achieves an average pollination rate of 7.5 s per flower, representing a 36.4% improvement compared to the most common intermittence strategy. Furthermore, the RCC strategy can be extended to tasks such as harvesting and pruning, significantly enhancing the efficiency of robot task execution even in industrial and service sectors.

Cooperative control strategy of trajectory tracking and driving stability for distributed-drive vehicles under extreme conditions

Active chassis dynamics control has become increasingly important in recent years for achieving high-level automated driving of intelligent electric vehicles. To improve tracking performance of the commonly-used model predictive control (MPC) in extreme scenarios, such as emergency lane changing, an improved MPC motion stability controller (MPC_ISMC) based on sliding mode variable structure active steering control and differential torque yaw control is proposed. Firstly, an MPC-based trajectory tracking controller is designed to track the desired trajectory. Subsequently, active front-wheel steering (AFS) and direct yaw moment (DYC) controllers are designed by using the backstepping sliding mode variable structure and the adaptive super-twisting sliding mode variable structure, respectively. Moreover, a cooperative control law for the AFS and the DYC is formulated by the smooth switching function and intervention intensity factor with respect to vehicle stable state and trajectory tracking error. After that, a torque distribution scheme of four wheels is optimized to achieve the additional yaw moment target. Finally, the effectiveness of the proposed cooperative control strategy is validated using a hardware-in-the-loop platform. The results indicate that the proposed controller has better performance in trajectory tracking and vehicle stability under extreme conditions compared with traditional MPC and other methods.

Bio-Inspired Cooperative Control Scheme of Obstacle Avoidance for UUV Swarm

The complex underwater environment poses significant challenges for unmanned underwater vehicles (UUVs), particularly in terms of communication constraints and the need for precise cooperative obstacle avoidance and trajectory tracking. Addressing these challenges solely through position information is crucial in this field. This study explores the intricate task of managing a group of UUVs as they navigate obstacles and follow a given trajectory, all based on position information. A new dynamic interactive topology framework utilizing sonar technology has been developed for the UUVs. This framework not only provides position information for the UUV swarm but also for the surrounding obstacles, enhancing situational awareness. Additionally, a bio-inspired cooperative control strategy designed for UUV swarms utilizing sonar interaction topology is introduced. This innovative method eliminates the need for velocity data from neighboring UUVs, instead relying solely on position information to achieve swarm cooperative control, obstacle avoidance, and trajectory adherence. The effectiveness of this method is validated through extensive simulations. The results show that the proposed method demonstrates improved sensitivity in obstacle detection, enabling faster trajectory tracking while maintaining a safer distance compared to traditional methods. Ultimately, this innovative strategy not only enhances operational efficiency but also enhances safety measures in UUV swarm operations.

Dual-Stage Heterogeneous Multiagent Systems Surrounding Control for a Motional Target

This paper addresses the dynamic target surrounding control problem for a class of heterogeneous multi-agent systems (MASs). To surround a motional target with a time-varying velocity, a distributed observer is established by employing perturbation system analysis, which can simultaneously retrieve both target state and dynamics. Then a distributed dual-stage cooperative control scheme based on output regulation principle is proposed to fulfill the evenly surrounding control mission, i.e., all the heterogeneous agents moving along a common circle around the dynamic target with an identical desired radius and evenly distributed phases. Afterwards, an optimal selection protocol of switching instant between the collective chasing and surrounding stages is provided as well. Significantly, sufficient conditions are derived to guarantee the asymptotical stability of such closed-loop heterogeneous MASs. Finally, numerical simulations are conducted to verify the effectiveness of the proposed surrounding control scheme.