Leader Follower Research Articles

Cooperative Adaptive Fuzzy Control for the Synchronization of Nonlinear Multi-Agent Systems under Input Saturation

This research explores the synchronization issue of leader–follower systems with multiple nonlinear agents, which operate under input saturation constraints. Each follower operates under a spectrum of unknown dynamic nonlinear systems with non-strict feedback. Additionally, due to the fact that the agents may be geographically dispersed or have different communication capabilities, only a subset of followers has direct communication with the leader. Compared to linear systems, nonlinear systems can provide a more detailed description of real-world physical models. However, input saturation is present in most real systems, due to various factors such as limited system energy and the physical constraints of the actuators. An auxiliary system of Nth order is introduced to counteract the impact of input saturation, which is then employed to create a collaborative controller. Due to the powerful capability of fuzzy logic systems in simulating complex nonlinear relationships, they are deployed to approximate the enigmatic nonlinear functions intrinsic to the systems. A distributed adaptive fuzzy state feedback controller is designed by approximating the derivative of the virtual controller by filters. The proposed controller ensures the synchronization of all follower outputs with the leader output in the communication graph. It is shown that all signals in the closed-loop system are semi-globally uniformly ultimately bounded, and the tracking errors converge to a small neighborhood around the origin. Finally, a numerical example is given to demonstrate the effectiveness of the proposed approach.

Fostering justice and ethical climate in the workplace: Leader–follower value congruence perspectives

Fostering justice and ethical climate in the workplace: Leader–follower value congruence perspectives

A new control method for leader–follower consensus problem of uncertain constrained nonlinear multi-agent systems

The design of Adaptive Fuzzy Sliding-Mode Control (AFSMC) is discussed in this study for the distributed leader–follower consensus problem of nonlinear uncertain multi-agent systems with input and state constraints. Avoiding the critical circumstances related to the violation of input and state constraints is an important issue in the design of consensus control systems. The limitations of existing adaptive consensus controllers developed for full-state constrained systems motivated the present novel method without restrictive structural assumptions. To convert the original problem to an unconstrained consensus control problem, the proposed control system uses a barrier function-based state transformation method and the input-state linearization methodology. As a result, each agent’s adaptive fuzzy sliding-mode control input, which consists of a fuzzy system and a robust term, is constructed based on the derived representation of the system dynamics. The fuzzy system is used to approximate the dynamic equations of the agent, its neighbors, and maybe the leader in each local feedback control law structure, and the robust term is designed to compensate for the fuzzy approximation error. The magnitudes of the robust terms and the output vectors of the fuzzy systems are also determined using the proposed adaptation laws. The second Lyapunov theorem and Barbalat’s lemma are used to verify the closed-loop system’s asymptotic stability. The effectuality of the proposed methodology is confirmed by numerical simulations for several Autonomous Underwater Vehicles (AUVs). Simulation results show that by means of the proposed AFSMC, the leader–follower consensus is achieved without state constraint violation and performance degradation.

Distributed adaptive robust containment control for reaction–diffusion neural networks with external disturbances under directed graphs

In this paper, the leader–follower robust synchronization issue is mainly addressed for reaction–diffusion neural networks (RDNNs) with multiple leaders and external disturbances under directed graphs. Based on the σ modification approach, we propose a novel distributed adaptive controller by adding a term −θi(ci−1)2 to avoid the phenomenon of parameter drift, that is, the adaptive parameters grow to infinity. Meanwhile, different from the adaptive control algorithm proposed in the undirected graph, we introduce a new function χi(t) to provide additional freedom for the design to achieve robust containment when confronted with external disturbances. Further, the robustness of tracking synchronization with one leader is guaranteed by the proposed adaptive controller when the external disturbances concerning L2 norm are bounded. Finally, relevant numerical simulation graphics are displayed separately to verify the correctness of the related theoretical results.

Trajectory Planning for Cooperative Double Unmanned Surface Vehicles Connected with a Floating Rope for Floating Garbage Cleaning

Double unmanned surface vehicles (DUSVs) towing a floating rope are more effective at removing large floating garbage on the water’s surface than a single USV. This paper proposes a comprehensive trajectory planner for DUSVs connected with a floating rope for cooperative water-surface garbage collection with dynamic collision avoidance, which takes into account the kinematic constraints and dynamic cooperation constraints of the DUSVs, which reflects the current collection capacity of DUSVs. The optimal travel sequence is determined by solving the TSP problem with an ant colony algorithm. The DUSVs approach the garbage targets based on the guidance of target key points selected by taking into account the dynamic cooperation constraints. An artificial potential field (APF) combined with a leader–follower strategy is adopted so that the each USV passes from different sides of the garbage to ensure garbage capturing. For dynamic obstacle avoidance, an improved APF (IAPF) combined with a leader–follower strategy is proposed, for which a velocity repulsion field is introduced to reduce travel distance. A fuzzy logic algorithm is adopted for adaptive adjustment of the desired velocities of the DUSVs to achieve better cooperation between the DUSVs. The simulation results verify the effectiveness of the algorithm of the proposed planner in that the generated trajectories for the DUSVs successfully realize cooperative garbage collection and dynamic obstacle avoidance while complying with the kinematic constraints and dynamic cooperation constraints of the DUSVs.

Formation Control of Multiple Autonomous Mobile Robots Using Turkish Natural Language Processing

People use natural language to express their thoughts and wishes. As robots reside in various human environments, such as homes, offices, and hospitals, the need for human–robot communication is increasing. One of the best ways to achieve this communication is the use of natural languages. Natural language processing (NLP) is the most important approach enabling robots to understand natural languages and improve human–robot interaction. Also, due to this need, the amount of research on NLP has increased considerably in recent years. In this study, commands were given to a multiple-mobile-robot system using the Turkish natural language, and the robots were required to fulfill these orders. Turkish is classified as an agglutinative language. In agglutinative languages, words combine different morphemes, each carrying a specific meaning, to create complex words. Turkish exhibits this characteristic by adding various suffixes to a root or base form to convey grammatical relationships, tense, aspect, mood, and other semantic nuances. Since the Turkish language has an agglutinative structure, it is very difficult to decode its sentence structure in a way that robots can understand. Parsing of a given command, path planning, path tracking, and formation control were carried out. In the path-planning phase, the A* algorithm was used to find the optimal path, and a PID controller was used to follow the generated path with minimum error. A leader–follower approach was used to control multiple robots. A platoon formation was chosen as the multi-robot formation. The proposed method was validated on a known map containing obstacles, demonstrating the system’s ability to navigate the robots to the desired locations while maintaining the specified formation. This study used Turtlebot3 robots within the Gazebo simulation environment, providing a controlled and replicable setting for comprehensive experimentation. The results affirm the feasibility and effectiveness of employing NLP techniques for the formation control of multiple mobile robots, offering a robust and effective method for further research and development on human–robot interaction.

The role of followership on organizational citizenship behavior in the service industry: A double mediation model

Competition in service industries is fierce, so operational efficiency and cost-effectiveness can be implemented by optimizing internal resources to increase competitive advantage. This study aims to investigate the role of followership in enhancing organizational citizenship behavior (OCB) with double mediating mechanisms, such as job satisfaction and organizational commitment. This study was conducted on food distributor service companies in Indonesia and employees as respondents. Questionnaires were used to collect data, which were using convenience sampling. Then, 131 data points were gathered and prepared for analysis using the G-Power minimum sample size. Data analysis was conducted using structural equation modeling with the SmartPLS application. The results showed that followership significantly influenced OCB, job satisfaction, and organizational commitment. Furthermore, it was also observed that job satisfaction showed an effect on OCB. However, organizational commitment does not affect OCB. Furthermore, during mediation testing, only job satisfaction was found to successfully mediate and act as a connecting variable between followership and OCB. This study has contributed to the cognitive resonance theory and suggests that service companies consider leadership followership in order to enhance OCB among their employees efficiently. It also recommended that employees should first exhibit job satisfaction before engaging in extra-role behaviors for the company. AcknowledgmentResearchers give great appreciation to Universitas Brawijaya for funding this research.

The Mask of Sanity? Leader Primary Psychopathy and the Effects of Leader Emotion Regulation Strategies on Followers

Individuals with psychopathic tendencies are sometimes quite capable of acquiring and maintaining leadership positions. One explanation could be that leaders with psychopathic personality profiles “hide behind the mask of sanity” by managing the public display of their emotions so that it positively affects other people's perceptions. We conducted a multisource team study (N = 306 teams) to investigate how leader primary psychopathy and emotion regulation strategies are related to follower perceptions of leader authenticity and follower trust in the leader. We found that leaders with stronger primary psychopathic tendencies would do better to adhere to a strategy of surface acting and refrain from deep acting in order to affect follower trust positively. Perceived authenticity explains the interactive effect of leader emotion regulation and psychopathy on follower trust. We also found that for leaders with higher levels of primary psychopathy deep acting is a less fruitful strategy because they lack the necessary empathic concern, and that the display of naturally felt emotions is a good strategy because it is positively associated with follower trust. We discuss whether the differential use of emotion regulation strategies might explain primary psychopaths’ upward mobility and how their use of emotion regulation strategies could help them to be perceived positively.

Three‐dimensional cooperative guidance for simultaneous attack with specified impact time based on fixed‐time convergence

AbstractIn this paper, distributed three‐dimensional (3D) cooperative guidance laws are designed for leader–follower multiple missiles to achieve simultaneous attack at a specified time. The guidance laws are derived from the biased proportional navigation guidance (PNG) law. Two fixed‐time distributed algorithms with time‐varying coefficients are proposed to track the state of the leader under undirected and directed topologies, respectively. The upper bounds of convergence times for the tracking algorithms are obtained. The biased guidance terms of followers are designed to track the leader with the proposed algorithms and the biased guidance term of the leader is designed to achieve specified impact time. Then, an auxiliary function is included into the biased guidance terms to meet the field‐of‐view (FOV) constraints. The leading angles' properties and convergence times under the proposed guidance laws are further analyzed. Numerical simulations are given to verify the effectiveness of the proposed cooperative guidance laws.

Control of multiconsensus in multi-agent systems based on eigenvector centrality

In multiconsensus (or group consensus) the units of a multi-agent system split into clusters, with the agents of the same group converging towards the same consensus value, which, in general, is different from cluster to cluster. In this brief paper, we develop a new technique to achieve multiconsensus among agents described by second-order dynamics. To do this, we introduce a communication protocol where interactions between agents are directed and weighted. Our approach consists of two steps. First, the weights of the interaction network are assigned so that the network adjacency matrix has a given leading eigenvector. This allows the formation of clusters of nodes that have the same value of eigenvector centrality. Second, the gains of the communication protocols are selected to guarantee the stability of the error dynamics. We study both the cases of leaderless and leader–follower multiconsensus, providing analytical conditions for multiconsensus, and discuss a few numerical examples to illustrate our theoretical results.

Integrated fault estimation and control for multi-agent systems with external disturbance using delayed relative information

This paper is dedicated to the problem of actuator fault estimation and fault-tolerant control for general linear leader–follower multi-agent systems subject to external disturbance and communication delay. First, the intermediate variable observer is constructed to estimate faults using relative information with symmetric time delays. A contradiction between the convergence speed of estimation error and the time-delay margin is revealed, where quick convergence implies small time-delay margin. As such, the augmented system observer is employed to improve the convergence speed. In the next, integrated scheme is adopted to handle the bi-directional interaction between fault estimation and fault-tolerant control arising from external disturbance and time-varying faults. In addition, we also consider the asymmetric delay case with only transmission delay. The delay-independent stability condition is derived with the use of augmented system observer by virtue of Lyapunov–Krasovskii function and [Formula: see text] robust theories. Finally, the simulation results are presented to verify the effectiveness of proposed method.

Leader–Follower Formation Control of Magnetically Actuated Millirobots for Automatic Navigation

Leader–Follower Formation Control of Magnetically Actuated Millirobots for Automatic Navigation

Optimized leader–follower consensus control for high-order nonlinear multi-agent system modeled in canonical dynamic form

This paper addresses the optimized leader–follower consensus control problem of nonlinear canonical dynamic multi-agent system (MAS). Since every agent is modeled in high-order dynamic, they contain the various state variables having derivative relations. Consequently, to attain the optimized consensus control, backstepping technique and reinforcement learning (RL) are combined together. In the first backstepping step, the virtual control is formulated to have the consensus error term consisting of neighbor agents’ output states. In the last backstepping step, since the nonlinearity dynamic is arisen, the optimized actual control is derived by performing the critic-actor RL. It is worth mentioning that, in the traditional RL optimal control, the critic and actor updating laws are obtained by performing the gradient descent algorithm to square of the approximated Hamilton–Jacobi-Bellman (HJB) equation, which includes multiple nonlinearity items, as a result, the algorithm is very intricate. However, in this optimized consensus control, since the RL training laws are yielded in the light of negative gradient of a simple positive function that is relevant to the HJB equation, its algorithm is very simple. At the same time, it can also avoid the persistence excitation conditions. Finally, theory and simulation confirm the validity of this optimized consensus method.

Research on A Global Path-Planning Algorithm for Unmanned Arial Vehicle Swarm in Three-Dimensional Space Based on Theta*–Artificial Potential Field Method

The current challenge in drone swarm technology is three-dimensional path planning and adaptive formation changes. The traditional A* algorithm has limitations, such as low efficiency, difficulty in handling obstacles, and numerous turning points, which make it unsuitable for complex three-dimensional environments. Additionally, the robustness of drone formations under the leader–follower mode is low, and effectively handling obstacles within the environment is challenging. To address these issues, this study proposes a virtual leader mode for drone formation flight and introduces a new Theta*–APF method for three-dimensional space drone swarm path planning. This algorithm optimizes the A* algorithm by transforming it into an omnidirectional forward Theta* algorithm. It also enhances the heuristic function by incorporating artificial potential field methods in a three-dimensional environment. Formation organization and control of UAVs is achieved using speed-control modes. Compared to the conventional A* algorithm, the Theta*–APF algorithm reduces the search time by about 60% and the trip length by 10%, in addition to the safer flight of the UAV formation, which is subject to artificial potential field repulsion by about 42%.

Fixed-time leader–follower consensus for second-order multi-agent systems: Variable exponent sliding mode control approach

This paper investigates the fixed-time leader–follower consensus (LFC) problem of second-order multi-agent systems (MASs) with bounded disturbance under directed interaction topology. First, a sliding mode manifold (SMM) with consensus errors–dependent variable exponential coefficient is given. Then, two sliding mode control (SMC) protocols are proposed. The proposed control protocols can eliminate the singularity, while guaranteeing the fixed-time reachability of the consensus errors to the sliding mode surface (SMS) and the fixed-time convergence of the consensus errors toward the origin along the SMS. Finally, the validity of the obtained methodology is confirmed by two simulation examples.

Does relationship conflict reduce novel idea communication through perceived leader openness? Power distance orientation as a moderator

PurposeThis paper aims to investigate why followers have low perceptions of leader openness and thus feel reluctant to communicate novel ideas by examining leader–follower relationship conflict (i.e. interpersonal incompatibility) and a follower’s power distance orientation (i.e. an acceptance of uneven power distribution in organizations) as antecedents.Design/methodology/approachThe research administrators conducted a three-wave work behavior survey in Study 1, a laboratory experiment in Study 2, and an online experiment in Study 3.FindingsThe results demonstrated that leader–follower relationship conflict reduced followers’ perceptions of leader openness. However, the negative impact of relationship conflict became non-significant when followers have high power distance orientations (i.e. an acceptance of uneven power distribution in organizations). The findings also showed an indirect interaction effect of leader–follower relationship conflict and followers’ power distance orientation on the followers’ communication of novel ideas through the followers’ perceptions of leader openness.Originality/valueThe research suggests that followers with higher power distance orientations are more likely to communicate novel ideas consistently because their relationship conflicts with their leaders do not negatively influence their perceptions of leader openness. Although researchers traditionally view cultures with a high level of power distance value as an obstacle to employee creativity, the present study reveals the benefits of an individual-level power distance orientation.

Integrated the Artificial Potential Field with the Leader–Follower Approach for Unmanned Aerial Vehicles Cooperative Obstacle Avoidance

For the formation and obstacle avoidance challenges of UAVs (unmanned aerial vehicles) in complex scenarios, this paper proposes an improved collaborative strategy based on APF (artificial potential field). This strategy combines graph theory, the Leader–Follower method, and APF. Firstly, we used graph theory to design formation topology and dynamically adjust the distances between UAVs in real time. Secondly, we introduced APF to avoid obstacles in complicated environments. This algorithm innovatively integrates the Leader–Follower formation method. The design of this attractive field is replaced by the leader’s attraction to the followers, overcoming the problem of unreachable targets in APF. Meanwhile, the introduced Leader–Follower mode reduces information exchange within the swarm, realizing a more efficient “few controlling many” paradigm. Afterwards, we incorporated rotational force to assist the swarm in breaking free from local minima. Ultimately, the stability of the integrated formation strategy was demonstrated using Lyapunov functions. The feasibility and effectiveness of the proposed strategy were validated across multiple platforms.

Adaptive virtual leader–leader–follower based formation switching for multiple autonomous tracked mobile robots in unknown obstacle environments

This article investigates the formation switching problem of a group of autonomous tracked mobile robots (ATMRs) traveling in unknown obstacle environments. Firstly, a novel formation structure model of virtual leader–leader–follower is designed, where the leader tracks the virtual leader determined by a reference trajectory, while the followers track their desired positions dictated by the virtual leader. Subsequently, a dynamic optimization formation switching strategy is developed, in which the leader calculates a formation adjustment factor in real-time to dynamically adjust the formation parameters according to the environmental constraints. Furthermore, a comprehensive performance evaluation index system is presented to assess formation switching efficiency, leveraging the inherent simplicity of the ATMR’s kinematics. Finally, extensive numerical simulations and physical experiments are carried out to validate the effectiveness of the proposed strategies.

Adaptive Sliding Mode Resilient Control of Multi-Robot Systems with a Leader–Follower Model under Byzantine Attacks in the Context of the Industrial Internet of Things

In this paper, an adaptive and resilient consensus control mechanism for multi-robot systems under Byzantine attack, based on sliding mode control, is proposed. The primary aim of the article is to develop a finite-time consensus control strategy even in the presence of a Byzantine attack. In the start, a finite-time consensus control mechanism is proposed to identify the essential conditions required for ensuring consensus accuracy in multi-robot systems, even when faced with Byzantine attacks using Lyapunov theory. Subsequently, a sliding mode control is combined with an adaptive technique for multi-robot systems that lack prior knowledge of Byzantine attack. Later, an attack observer is proposed to estimate the performance of multi-robot systems in the presence of a Byzantine attack. Additionally, chattering effects are mitigated by employing integral sliding mode control. As a result, resilient consensus performance of multi-robot systems can be achieved in a finite time interval. A simulation example is also presented to validate the effectiveness of the proposed model. Furthermore, we delve into the data structure of the proposed method and explore its integration with Artificial Intelligence for seamless incorporation into the Industrial Internet of Things applications.

A Novel Distributed Adaptive Controller for Multi-Agent Systems with Double-Integrator Dynamics: A Hedging-Based Approach

In this paper, we focus on designing a model reference adaptive control-based distributed control law to drive a set of agents with double-integrator dynamics in a leader–follower fashion in the presence of system anomalies such as agent-based uncertainties, unknown control effectiveness, and actuator dynamics. In particular, we introduce a novel hedging-based reference model with second-order dynamics to allow an adaptation in the presence of actuator dynamics. We show the stability of the overall closed-loop multi-agent system by utilizing the Lyapunov Stability Theory, where we analyze the stability condition by using the Linear Matrix Inequalities method to show the boundedness of the reference model and actuator states. Finally, we illustrate the efficacy of the proposed distributed adaptive controller on an undirected and connected line graph in five cases.