Cooperative Control Research Articles

Cooperative Control of Multiple PM Actuators in PMLSM With Segmented Windings

Cooperative Control of Multiple PM Actuators in PMLSM With Segmented Windings

Decentralized Adaptive Event-Triggered Fault-Tolerant Cooperative Control of Multiple Unmanned Aerial Vehicles and Unmanned Ground Vehicles with Prescribed Performance under Denial-of-Service Attacks

Decentralized Adaptive Event-Triggered Fault-Tolerant Cooperative Control of Multiple Unmanned Aerial Vehicles and Unmanned Ground Vehicles with Prescribed Performance under Denial-of-Service Attacks

Completely distributed collision-free cooperative control for underactuated unmanned surface vessels with edge weighted interaction

Completely distributed collision-free cooperative control for underactuated unmanned surface vessels with edge weighted interaction

Practical time‐varying formation cooperative control for high‐order nonlinear multi‐agent systems avoiding spatial resource conflict via safety constraints

AbstractThe operation of multi‐agent systems (MAS) in space necessitates considerations for obstacle avoidance, collision prevention, and connectivity among agents. This coupling conflict, stemming from spatial resource utilization, poses a significant and non‐negligible threat to the safe operation of MAS. This article proposes a consensus control scheme for the time‐varying formation of MAS, incorporating functions for maintaining connectivity, collision avoidance, and obstacle dodging. This scheme effectively constrains the time‐varying formation tracking error within an arbitrarily small range. The considered system model takes a high‐order nonlinear form, with uncertainties and disturbances present in each order. This grants the control scheme with high generality and practicability. By employing barrier Lyapunov function to delineate the safe operational space of MAS, conflicts in spatial resource utilization are avoided. This approach simultaneously fulfills the requirements for connectivity maintenance, collision avoidance, and obstacle dodging in the safe operation of MAS. An additional rotation operator is integrated into the controller to smoothly address the “minima” problem, eliminating the need for external intervention. Gaussian radial basis function is used to estimate the nonlinear terms, uncertainties, and unknown perturbation online in the system. The stability of the MAS under the proposed control scheme is analyzed through Lyapunov function. Finally, numerical simulation results are demonstrated to explain the effectiveness of the control scheme.

The supernumerary robotic limbs of brain-computer interface based on asynchronous steady-state visual evoked potential

Brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) have attracted much attention in the field of intelligent robotics. Traditional SSVEP-based BCI systems mostly use synchronized triggers without identifying whether the user is in the control or non-control state, resulting in a system that lacks autonomous control capability. Therefore, this paper proposed a SSVEP asynchronous state recognition method, which constructs an asynchronous state recognition model by fusing multiple time-frequency domain features of electroencephalographic (EEG) signals and combining with a linear discriminant analysis (LDA) to improve the accuracy of SSVEP asynchronous state recognition. Furthermore, addressing the control needs of disabled individuals in multitasking scenarios, a brain-machine fusion system based on SSVEP-BCI asynchronous cooperative control was developed. This system enabled the collaborative control of wearable manipulator and robotic arm, where the robotic arm acts as a "third hand", offering significant advantages in complex environments. The experimental results showed that using the SSVEP asynchronous control algorithm and brain-computer fusion system proposed in this paper could assist users to complete multitasking cooperative operations. The average accuracy of user intent recognition in online control experiments was 93.0%, which provides a theoretical and practical basis for the practical application of the asynchronous SSVEP-BCI system.

Formation cooperative trajectory tracking control for unmanned aerial vehicles via differential game and reinforcement learning

Formation cooperative trajectory tracking control for unmanned aerial vehicles via differential game and reinforcement learning

Secure control design for cooperative adaptive cruise control with a time-varying input delays under false data injection attacks

ABSTRACT Connected and Autonomous Vehicles (CAVs) are shaping the future of transportation systems; however, their future development requires consideration of the upcoming challenges with cyber-physical attacks. Cooperative Adaptive Cruise Control (CACC) is one of the features used in CAVs. Using CACC increases the vulnerability of such systems to False Data Injection (FDI) attacks. To address this, this paper proposes a novel mitigation approach to compensate for FDI attacks and noise in real-time. In addition to FDI attacks, the current CACC algorithm suffers from input delay and disturbance. Here, we develop a nonlinear controller and an FDI attacks estimation technique to mitigate the effects of FDI attacks, noise, input delay and additive disturbance. Lyapunov–Krasovskii functionals are used in the Lyapunov-based stability analysis to ensure semi-global uniformly ultimately bounded tracking. In order to demonstrate the performance of the proposed approach, we validate it through simulation analysis.

The exploration of multifunctional liquid robotics with ferrofluids: Fabrication, control, operation and sensing

Liquid robotics with arbitrary deformability and rich functionality can adapt to various environmental constraints and perform many significant tasks. To turn this vision into reality, an intelligent liquid system, ferrofluid, which possesses excellent macroscopic and microscopic responsiveness to external magnetic fields, is explored as the rudiment of liquid robotics. Focus is given not only to its improved fabrication and cooperative programming control, but also its underexplored object manipulation and signal transmission. The prepared ferrofluid consists of sterically stabilized ultra-small magnetic nanoparticles suspended in carrier liquids, showing strong stability, flexible mobility, and substantial magnetization. Intelligent locomotion and complex morphology control are successfully achieved by a programmable platform that generates sequential pre-designed magnetic fields through the timing activation of specific electromagnets. Meanwhile, by utilizing the magneto-Archimedes effect, this ferrofluid system can perform multi-dimensional manipulation of objects with densities ranging from 1.1 to 8.9 g/cm3 in cooperation with varying magnetic fields. Furthermore, this system can sense vibrations due to its intrinsic liquid feature and send corresponding signals through detecting the changes in magnetic flux by external coils. Similarly, the flow, concentration, and speed of ferrofluids can also be detected. These advancements in fabrication, control, operation, and sensing are crucial for the development of liquid robotics.

Cooperative control of virtually coupled trains considering train-to-train communication cycle: a tube-based model predictive control approach

In this paper, a distributed tube-based model predictive control (TMPC) method is proposed to address the cooperative control of virtually coupled high-speed train (HSTs), which consists of an optimisation control part and a feedback control part. The novelty of this work lies in its pioneering consideration of the inconsistency between the train-to-train (T2T) communication cycle and the train operation control cycle in the cooperative control of virtually coupled trains. Firstly, for the optimisation control part, a distributed MPC scheme is designed based on the nominal train dynamics model to address the cooperative control of trains with different initial states, state constraints and control input constraints. By solving the optimisation control problem of the MPC, the optimal control input sequence and operating state sequence of the train for the next T2T communication cycle can be obtained. Secondly, the recursive feasibility and convergence of the MPC are analysed theoretically. Thirdly, for the feedback control part, an adaptive feedback control law is designed based on the actual train dynamics model, auxiliary dynamic system (ADS) and neural network techniques. In the train operation control cycle, the feedback control part utilises the optimal state sequence from the MPC as a reference for feedback control to deal with the external disturbance and train dynamics modelling inaccuracy. Fourthly, it is theoretically proved that the actual operating state of the train can converge to a small region around the optimal state of the MPC in a finite time. Finally, the effectiveness of the proposed distributed cooperative control scheme is verified by simulations.

Examining Psychological Conflict-Handling Strategies for Highly Automated Vehicles to Resolve Legal User-Vehicle Conflicts

As vehicle automation progresses, with SAE levels 3 to 5 introducing varying degrees of control transfer from driver to vehicle, a key challenge emerges: aligning driver interests with the automated vehicle's (AV) operational goals. Despite technical feasibility, drivers' tendencies to intervene due to distrust in or dissatisfaction with AVs necessitate consideration of control mechanisms in future AV designs. This study investigates the potential of persuasion strategies inspired by Human-Robot Interaction research to harmonize driver actions with AV goals in legal conflict situations. We conducted a Virtual Reality driving simulator study with 36 participants, comparing 11 persuasive conflict-handling strategies and a baseline of no persuasion. Our research helps understand the effects of persuasion on users' compliance with law-abiding AV goals, trust, and acceptance towards the AV and evaluates the effectiveness of these strategies based on their working mechanism (cognitive, emotional, social, politeness) and valence (negative, neutral, positive). Results show that none of the strategies could substantially increase compliance with the AV, but neutral and positive persuasion strategies did not decrease acceptance and trust towards the AV compared to no persuasion. These findings contribute to the discourse on cooperation design and control allocation in AVs, particularly in scenarios involving legal conflicts.

Distributed cooperative control for global power sharing management of interconnected microgrids in flexible multi-terminal DC connection scheme

Flexible multi-terminal direct current (MTDC) connection scheme is an ideal way to integrate multiple adjacent microgrids and form interconnected microgrids (IMGs). However, under the traditional control mode, MTDC will lead to frequency/voltage decoupling among all the microgrids. Moreover, improper control scheme design of bidirectional interlinking converters (BICs) in MTDC will make global active/reactive power sharing management impossible, which is considered as the significant task in IMGs. Therefore, a novel distributed cooperative control strategy is designed in this article. In the proposed control strategy, frequency/voltage regulations and local active/reactive power sharing management are achieved through distributed secondary control within each MG. Accurate global active/reactive power sharing management is realized by coordinating the power exchange instructions of each BIC in a distributed manner. The control dynamics of these two levels are designed to be decoupled. The stability of the proposed control strategy is demonstrated by the Lyapunov method, and its effectiveness is verified by simulating a test IMG system in MATLAB/Simulink.

Optimal control model for the spread of Streptococcus suis in human population

Streptococcus suis infection is a zoonotic disease that can spread from pigs to humans. The infection can cause permanent deafness in people. Although it is considered as a neglected zoonotic pathogen, the prevalence of this infection has not measurably decreased in recent years. In this research, we proposed mathematical models of S. suis infection in both human and pig populations. Disinfectant sterilization in swine farms and educational campaigns were considered for cooperative control strategies. The aim of this study was to derive an optimal control strategy by using optimal control techniques associated with an existing epidemic model. Equilibrium points and the basic reproduction numbers were analyzed to determine the effect of control parameters in the constant case. For the optimal control problem, we showed numerically that the optimal control functions effectively reduced the number of infectious individuals within a finite time. However, the suggested control functions had more impact on the human group than the swine group. The sensitivity analysis implied that the control of swine population should be focused on the surveillance of weaning piglets.

Model test of extreme deformation and failure mechanism of automatically formed roadway under thick-hard roof

Automatically formed roadways (AFR) are highly susceptible to extreme deformation and damage under thick-hard roof geological conditions. Taking the thick-hard roof of the Yushuquan coal mine as a case study, this paper mainly studies the extreme deformation, failure mechanism, and control technology of the AFR. First of all, through on-site monitoring, the maximum vertical convergence rate of the AFR in thick-hard roof reaches 44.8 mm/d. The failure of the AFR mainly focuses on the roof sinking, coal rib rupture, and deformation of gangue prevention structure. Then, a model test was employed to analyze the deformation characteristics of the AFR, including the roadway roof, roadway floor, solid coal rib, and gangue rib. Furthermore, the failure mechanism of the AFR in thick-hard roof is explored. The model test shows that the rotation and subsidence of the roadway roof strata are the main factors causing the AFR damage. In turn, the subsidence of the roadway roof strata is controlled by the movement and distribution characteristics of the hard rock blocks within the cutting range. Eventually, cooperative control technology was proposed, including increasing the gangue volume in the goaf and increasing the coal rib strength to control the extreme deformation of the AFR in thick-hard roof conditions. Field engineering application demonstrates that the deformation of AFR surrounding rock is effectively controlled under thick-hard roof. The research findings can serve as a valuable reference for the prevention and control of the stability of AFR under complex geological conditions.

Data‐driven cooperative adaptive cruise control for unknown nonlinear vehicle platoons

Data‐driven cooperative adaptive cruise control for unknown nonlinear vehicle platoons

Neural network-based distributed consensus tracking control for uncertain Euler–Lagrange systems over directed topologies

This paper proposes a neural network-based robust control approach for driving disturbed Euler–Lagrange systems (e.g., robot manipulator) in a directed network to perform consensus tracking of a heterogeneous leader’s output signal. The parameter matrices and external disturbances in each follower’s Euler–Lagrange dynamics are unmeasurable, thus we unify them into one lumped uncertainty term. Neural networks are then employed to online estimate these uncertainties, with adaptive update laws ensuring the boundedness of the estimation errors. Subsequently, a set of distributed observers are developed to adaptively estimate the leader’s states as well as the unidentified parameter vector and output matrix in the leader’s model. With their help, the robust cooperative controller is designed. Its efficacy on directed networks is theoretically justified by designing a Lyapunov function with a special structure. This function produces symmetric positive definite matrices when its first derivative terms interact with the specified parameters in the observer and Laplacian matrices for directed graphs. Finally, numerical simulations based on two-link robot manipulators are carried out to verify that the tracking errors converge to zero when applying the neural network-based robust controller.

Finite-time error constraint control for multi-AUV systems with unknown uncertainties based on observer and tan-type barrier Lyapunov function

PurposeAutonomous underwater vehicle (AUV) is widely used in resource prospection and underwater detection due to its excellent performance. This study considers input saturation, nonlinear model uncertainties and external ocean current disturbances. The containment errors can be limited to a small neighborhood of zero in finite time by employing control strategy. The control strategy can keep errors within a certain range between the trajectory followed by AUVs and their intended targets. This can mitigate the issues of collisions and disruptions in communication which may arise from AUVs being in close proximity or excessively distant from each other.Design/methodology/approachThe tracking errors are constrained. Based on the directed communication topology, a cooperative formation control algorithm for multi-AUV systems with constrained errors is designed. By using the saturation function, state observers are designed to estimate the AUV’s velocity in six degrees of freedom. A new virtual control algorithm is designed through combining backstepping technique and the tan-type barrier Lyapunov function. Neural networks are used to estimate and compensate for the nonlinear model uncertainties and external ocean current disturbances. A neural network adaptive law is designed.FindingsThe containment errors can be limited to a small neighborhood of zero in finite time so that follower AUVs can arrive at the convex hull consisting of leader AUVs within finite time. The validity of the results is indicated by simulations.Originality/valueThe state observers are designed to approximate the speed of the AUV and improve the accuracy of the control method. The anti-saturation function and neural network adaptive law are designed to deal with input saturation and general disturbances, respectively. It can ensure the safety and reliability of the multiple AUV systems.

Intelligent Vehicle Path Tracking and Stability Cooperative Control Strategy Based on Stable Domain

Intelligent Vehicle Path Tracking and Stability Cooperative Control Strategy Based on Stable Domain

Attitude collaborative control strategy for space gravitational wave detection

This paper proposes a high-precision attitude cooperative control strategy for three spacecraft in space gravitational wave detection. Firstly, based on the layout of optical components on the spacecraft and factors such as external interference and model uncertainty, a relative attitude model based on the Euler angle is constructed for the characteristics of small angle motion in differential wavefront sensing mode. Secondly, aiming at the cooperative control problem of multi drag-free attitude control system after the laser link capture between two spacecraft is completed, an attitude control method based on consensus mode cooperative control strategy is proposed. It combines local neighbourhood error from star sensing and differential wavefront sensing with an adaptive controller for equilateral triangle configuration control. Additionally, a cooperative control strategy based on laser measurement information is established to improve control accuracy by avoiding star sensors with low accuracy. Simulation results indicate that the control accuracy of the consistency mode and its sub-mode laser measurement mode is superior to the master-slave and cyclic modes, achieving better than 5×10−6 rad. The laser measurement mode demonstrates the highest accuracy, with relative attitude control accuracy reaching 1.5×10−7 rad, meeting the requirements for space gravitational wave detection.

Energy-Oriented Hybrid Cooperative Adaptive Cruise Control for Fuel Cell Electric Vehicle Platoons.

Given the complex powertrain of fuel cell electric vehicles (FCEVs) and diversified vehicle platooning synergy constraints, a control strategy that simultaneously considers inter-vehicle synergy control and energy economy is one of the key technologies to improve transportation efficiency and release the energy-saving potential of platooning vehicles. In this paper, an energy-oriented hybrid cooperative adaptive cruise control (eHCACC) strategy is proposed for an FCEV platoon, aiming to enhance energy-saving potential while ensuring stable car-following performance. The eHCACC employs a hybrid cooperative control architecture, consisting of a top-level centralized controller (TCC) and bottom-level distributed controllers (BDCs). The TCC integrates an eco-driving CACC (eCACC) strategy based on the minimum principle and random forest, which generates optimal reference velocity datasets by aligning the comprehensive control objectives of the platoon and addressing the car-following performance and economic efficiency of the platoon. Concurrently, to further unleash energy-saving potential, the BDCs utilize the equivalent consumption minimization strategy (ECMS) to determine optimal powertrain control inputs by combining the reference datasets with detailed optimization information and system states of the powertrain components. A series of simulation evaluations highlight the improved car-following stability and energy efficiency of the FCEV platoon.

Distributed event-triggered cooperative saturation control for nonlinear multi-agent systems with time-delay and output constraint

Distributed event-triggered cooperative saturation control for nonlinear multi-agent systems with time-delay and output constraint