Multi-agent Research Articles

Distributed adaptive finite-time fault-tolerant cooperative control of heterogeneous multi-agent systems with saturation and disturbances

In this paper, the issue of distributed adaptive finite-time fault-tolerant cooperative control (FT-FTCC) problem is investigated for multiple unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) with unknown parameter uncertainties, actuator faults, input saturation and external disturbances. Starting from the dynamic models of the UAVs and UGVs, an unified control model is presented. Then, a sliding-mode estimator is presented to estimate the position of the leader for the followers which only uses the information from neighbours. Next, a distributed adaptive FT-FTCC scheme, which can also deal with the uncertainties, actuator faults, input saturation and disturbances, is proposed by utilising disturbance observers and neural networks. Based on Lyapunov function approach, the tracking errors of all followers subject to the pre-defined desired positions are uniformly ultimately bounded. Finally, simulations are given to validate the efficiency of the developed FT-FTCC scheme.

Alternating Projection Method for Intersection of Convex Sets, Multi-Agent Consensus Algorithms and Averaging Inequalities

The history of the alternating projection method for finding a common point of several convex sets in Euclidean space goes back to the well-known Kaczmarz algorithm for solving systems of linear equations, which was devised in the 1930s and later found wide applications in image processing and computed tomography. An important role in the study of this method was played by I.I. Eremin’s, L.M. Bregman’s, and B.T. Polyak’s works, which appeared nearly simultaneously and contained general results concerning the convergence of alternating projections to a point in the intersection of sets, assuming that this intersection is nonempty. In this paper, we consider a modification of the convex set intersection problem that is related to the theory of multi-agent systems and is called the constrained consensus problem. Each convex set in this problem is associated with a certain agent and, generally speaking, is inaccessible to the other agents. A group of agents is interested in finding a common point of these sets, that is, a point satisfying all the constraints. Distributed analogues of the alternating projection method proposed for solving this problem lead to a rather complicated nonlinear system of equations, the convergence of which is usually proved using special Lyapunov functions. A brief survey of these methods is given, and their relation to the theorem ensuring consensus in a system of averaging inequalities recently proved by the first author is shown (this theorem develops convergence results for the usual method of iterative averaging as applied to the consensus problem).

Observer-Based Fixed-Time Consensus Tracking for a Class of Nonlinear Multi-Agent Systems

In this paper, the problem of fixed-time consensus tracking for a class of nonlinear multi-agent systems (MASs) with uncertainties and external disturbances is addressed. To solve this problem, a new fixed-time consensus tracking protocol with disturbance rejection abilities is proposed that consists of a fixed-time distributed observer (FTDO), a fixed-time disturbance observer (FDO), and a nonsmooth backstepping controller with lumped disturbance compensation (NBCDC). The fixed-time stabilities are analyzed correspondingly. Firstly, an FTDO is designed to estimate a leader’s output by using the topology of communication networks for each follower. Second, an FDO is developed to observe the lumped disturbances composed of model uncertainties and external disturbances. Finally, an NBCDC is proposed for improving the closed-loop performance. The simulation results show that the proposed method is effective.

Observer-based adaptive time-varying formation tracking of one-sided Lipschitz nonlinear multi-agent systems

In this article, the problem of time-varying formation (TVF) tracking for nonlinear multi-agent systems (MASs) is investigated. The agent dynamics are described by one-sided Lipschitz (OSL) and quadratic inner-boundedness (QIB) function. Adaptive continuous TVF tracking protocols for nonlinear MASs are proposed. Compared to prior nonlinear system, this approach is more general and less conservative than nonlinear system with large Lipschitz constant. In addition, the continuous protocols are proposed to avoid undesirable chattering from non-zero inputs of the leader. Finally, the expected theoretical results are achieved through effective numerical simulations.

Multilevel modeling and control of dynamic systems.

The underlying changes of complex dynamic systems are affected by numerous highly interdependent components with nonlinear interactions and cannot be suitably predicted by merely analyzing their individual parts. Recent technological developments (reducing the size of actuators and sensors, increasing speed and productivity) make it possible to monitor and handle systems on time, even during transient processes. In many practical applications, to effectively study rapidly evolving systems, their structure has to be examined at three levels: Micro-Scale (Short-Term), Meso-Scale (Intermediate-Term), and Macro-Scale (Long-Term). This multiscale framework provides a powerful tool for understanding and managing the intricate dynamics of complex systems. Macro-scale and micro-scale approaches are conventionally utilized in general system modeling and control theory, especially within the fields of networked control and multi-agent systems. By leveraging the meso-scale perspective, it is possible to balance detailed component analysis with overarching system behaviors, thereby enhancing the efficiency and effectiveness of operational strategies in complex systems. This paper presents and discusses a formal meso-level depiction of dynamics and control, assuming a finite number of attractors form and remain stable over time progress. Integral characteristics and dynamics of clusters are defined, simplifying control synthesis while maintaining problem formulation. A significant challenge is the model error due to changing cluster structures linked to randomized estimation and optimization algorithms valid under unknown disturbances. To demonstrate the practicality of this framework, the approach is applied to control tasks of a group of robots, leading to scalable and effective control strategies.

Finite-Time Partial Component Consensus for Nonlinear Leader-Following Multi-Agent Systems

The problem of finite-time partial component consensus (FTPCC) for first-order nonlinear multi-agent systems (MASs) is investigated in this paper for the first time. By incorporating the permutation matrix approach, we derive a novel error system for identical components, which facilitates stability analysis. Leveraging partial variable stability theory and related foundational knowledge, we devise two adaptable protocols. These protocols are tailored to achieve FTPCC in nonlinear MASs, one for systems without disturbances and another for those with bounded disturbances. To validate our findings, numerical examples are provided, demonstrating the effectiveness of the proposed results.

Neurodynamics-based formation tracking control of leader-follower nonholonomic multiagent systems

This paper uses a bioinspired neurodynamic (BIN) approach to investigate the formation control problem of leader-follower nonholonomic multiagent systems. In scenarios where not all followers can receive the leader's state, a distributed adaptive estimator is presented to estimate the leader's state. The distributed formation controller, designed using the backstepping technique, utilizes the estimated leader states and neighboring formation tracking error. To address the issue of impractical velocity jumps, a BIN-based approach is integrated into the backstepping controller. Furthermore, considering the practical applications of nonholonomic multiagent systems, a backstepping controller with a saturation velocity constraint is proposed. Rigorous proofs are provided. Finally, the effectiveness of the presented formation control law is illustrated through numerical simulations.

Abstract 4138722: Large Language Model based multi-agent Transcatheter Aortic Valve Implantation team to augment multidisciplinary meetings - proof of concept.

Introduction: Multidisciplinary team (MDT) discussions are integral to Transcatheter Aortic Valve Implantation (TAVI) decision making. Large language model (LLM) ubiquity and low-code no-code platforms have enabled clinician lead solution development. Specialised chatbots or ‘agents’ have evolved into multi-agent systems that can personify human collaboration. We assess the performance of an artificial intelligence (AI) multi-agent TAVI MDT. Methods: Four de-identified TAVI cases from two metropolitan Australian hospitals were assessed by a mock human TAVI MDT (h-MDT) and an AI multi-agent TAVI MDT (ai-MDT). The ai-MDT was created with Agentflow within Flowise AITM and had a hierarchical multi-agent architecture suited to complex reasoning required for TAVI MDT simulation (figure). LLM limitations necessitated the ai-MDT rely on imaging reports rather than clinical images. The h-MDT and ai-MDT consisted of similar team members. Outputs from the h-MDT and ai-MDT was adjudicated by a panel of four blinded TAVI doctors that determined if output was human vs AI and assigned a SMIC score (4-12, 4=good, 12=poor) that assessed structure, missing information, incorrect information and clinical utility. Time durations for h-MDT and ai-MDT were recorded. Results: Adjudicators differentiated human vs. AI output 100% of the time and ai-MDT output had better SMIC scores than h-MDT (U-stat 213, p=0.0011). ai-MDT outperformed h-MDT in the domains of structure, missing information and clinical utility but was not statistically different in the incorrect information domain (U-stat 132, p=0.88). The average time for each case in h-MDT was 15 minutes and 45 seconds compared to 97 seconds for ai-MDT. Conclusion: This demonstrates the potential of using LLM based multi-agent systems as a clinical adjunct in highly specialized multidisciplinary clinical meetings. AI responses were superior for structure, clinical utility and missing information and non-inferior for incorrect information compared to humans, which highlights that hallucinations remain an issue with generative AI. Time was saved but image interpretation still requires human input, for now. Cognitive AI continues to require human supervision for implementation.

Fixed-Time Consensus Multi-Agent-Systems-Based Speed Cooperative Control for Multiple Permanent Magnet Synchronous Motors with Complementary Sliding Mode Control

To improve the tracking performance and robustness of traditional multi-motor speed cooperative control, this paper proposes a speed cooperative control method for multiple permanent magnet synchronous motors (multi-PMSMs) based on the fixed-time consensus protocol for multi-agent systems (MASs) combined with CSMC. Firstly, the speed regulation system of multi-PMSMs is regarded as a MAS. By designing a distributed consensus protocol based on an undirected communication topology, the system achieves fixed-time consensus convergence. Then, a terminal integral sliding mode observer (TISMO) is designed to estimate disturbances, and feedforward compensation is introduced into the consensus protocol to obtain the desired q-axis current. Furthermore, within the framework of the vector control speed cooperative system of PMSMs, a CSMC is designed to track the q-axis reference current. Meanwhile, the stability of the above controllers and observers is theoretically proven using the Lyapunov functions. Finally, comparative experiments are conducted on a multi-PMSM speed regulation experimental platform to verify the proposed control method against the traditional deviation coupling control (DCC) method. The results indicate that under the new control method proposed in this paper, the chattering phenomenon is reduced by about 2 r/min compared to the traditional DCC method. During sudden load and sudden relief load conditions, the speed fluctuation is reduced by approximately 4%, demonstrating good tracking performance and strong robustness.

Computationally Efficient Inference via Time-Aware Modular Control Systems

Control in multi-agent decision-making systems is an important issue with a wide variety of existing approaches. In this work, we offer a new comprehensive framework for distributed control. The main contributions of this paper are summarized as follows. First, we propose PHIMEC (physics-informed meta control)—an architecture for learning optimal control by employing a physics-informed neural network when the state space is too large for reward-based learning. Second, we offer a way to leverage impulse response as a tool for system modeling and control. We propose IMPULSTM, a novel approach for incorporating time awareness into recurrent neural networks designed to accommodate irregular sampling rates in the signal. Third, we propose DIMAS, a modular approach to increasing computational efficiency in distributed control systems via domain-knowledge integration. We analyze the performance of the first two contributions on a set of corresponding benchmarks and then showcase their combined performance as a domain-informed distributed control system. The proposed approaches show satisfactory performance both individually in their respective applications and as a connected system.

Timed Interpreted Systems as a New Agent-Based Formalism for Verification of Timed Security Protocols

This article introduces a new method for modelling and verifying the execution of timed security protocols (TSPs) and their time-dependent security properties. The method, which is novel and reliable, uses an extension of interpreted systems, accessible semantics in multi-agent systems, and timed interpreted systems (TISs) with dense time semantics to model TSP executions. We enhance the models of TSPs by incorporating delays and varying lifetimes to capture real-life aspects of protocol executions. To illustrate the method, we model a timed version of the Needham–Schroeder Public Key Authentication Protocol. We have also developed a new bounded model checking reachability algorithm for the proposed structures, based on Satisfiability Modulo Theories (SMTs), and implemented it within the tool. The method comprises a new procedure for modelling TSP executions, translating TSPs into TISs, and translating TISs’ reachability problem into the SMT problem. The paper also includes thorough experimental results for nine protocols modelled by TISs and discusses the findings in detail.

Resource Allocation in UAV-D2D Networks: A Scalable Heterogeneous Multi-Agent Deep Reinforcement Learning Approach

In unmanned aerial vehicle (UAV)-assisted device-to-device (D2D) caching networks, the uncertainty from unpredictable content demands and variable user positions poses a significant challenge for traditional optimization methods, often making them impractical. Multi-agent deep reinforcement learning (MADRL) offers significant advantages in optimizing multi-agent system decisions and serves as an effective and practical alternative. However, its application in large-scale dynamic environments is severely limited by the curse of dimensionality and communication overhead. To resolve this problem, we develop a scalable heterogeneous multi-agent mean-field actor-critic (SH-MAMFAC) framework. The framework treats ground users (GUs) and UAVs as distinct agents and designs cooperative rewards to convert the resource allocation problem into a fully cooperative game, enhancing global network performance. We also implement a mixed-action mapping strategy to handle discrete and continuous action spaces. A mean-field MADRL framework is introduced to minimize individual agent training loads while enhancing total cache hit probability (CHP). The simulation results show that our algorithm improves CHP and reduces transmission delay. A comparative analysis with existing mainstream deep reinforcement learning (DRL) algorithms shows that SH-MAMFAC significantly reduces training time and maintains high CHP as GU count grows. Additionally, by comparing with SH-MAMFAC variants that do not include trajectory optimization or power control, the proposed joint design scheme significantly reduces transmission delay.

Mitigating Large Vision-Language Model Hallucination at Post-hoc via Multi-agent System

This paper addresses the critical issue of hallucination in Large Vision-Language Models (LVLMs) by proposing a novel multi-agent framework. We integrate three post-hoc correction techniques: self-correction, external feedback, and agent debate, to enhance LVLM trustworthiness. Our approach tackles key challenges in LVLM hallucination, including weak visual encoders, parametric knowledge bias, and loss of visual attention during inference. The framework employs a Plug-in LVLM as the base model to reduce its hallucination, a Large Language Model (LLM) for guided refinement, external toolbox models for factual grounding, and an agent debate system for consensus-building. While promising, we also discuss potential limitations and technical challenges in implementing such a complex system. This work contributes to the ongoing effort to create more reliable and trustworthy multimodal multi-agent systems.

Interval observer-based consensus tracking for multi-agent systems under Markovian switching topologies with stealthy attack

This article investigates the bounded consensus tracking problem for uncertain linear multi-agent systems (MASs) under Markovian switching topologies in the presence of unknown but bounded disturbances and stealthy attacks. First, a class of interval observers (IOs) is designed to estimate confidence bounds of agents states and stealthy attack signals. The stealthy attack is considered as that the output information is modified by the malicious attacker. Second, distributed control protocols under Markovian switching topologies are proposed for the MASs based on the designed IOs to achieve bounded consensus. The mean-square error bounds depend on the error bounds of disturbances. After rigorous mathematical proofs, some sufficient conditions are acquired for achieving control goals. Simulations are provided to validate the effectiveness of the proposed IOs and control protocols.

Autonomous Cooperative Hunting with Rule-Based and Self-Learning Control for Multiagent Systems

This paper considers the problem of autonomous cooperative hunting in an unknown dynamic environment, where a group of mobile agents collaborate to capture a moving target. Due to the decentralized decision-making nature of multi-agent systems and the presence of real-world constraints, it is a challenging task. To solve this problem, an artificial rule based hunting algorithm (AR-HA) is firstly developed based on the principles of attraction and repulsion with heading adjustment, and each agent is controlled by the designed rules. Then, to further enhance the stability of cooperative hunting, a self-learning algorithm based on Twin Delayed Deep Deterministic policy gradient (SL-TD3) is proposed. Each agent is governed by its own SL-TD3 controller and learns independently from its interaction with the environment, taking advantage of the reward function designed based on the control rules of AR-HA. Besides, in order to improve training efficiency, imitation learning is employed to initialize the actor network. Experiments on both virtual and real robots demonstrate the effectiveness of the proposed algorithms for autonomous cooperative hunting.

Optimal Model-Free Mean-Square Consensus for Multi-Agents with Markov Switching Topology

Due to the real applications, optimal consensus reinforcement learning with switching topology is still challenging due to the complexity of topological changes. This paper investigates the optimal consensus control problem for discrete multi-agent systems under Markov switching topologies. The goal is to design an appropriate algorithm to find the optimal control policies that minimize the performance index while achieving consensus among the agents. The concept of mean-square consensus is introduced, and the relationship between consensus error and tracking error to achieve mean-square consensus is studied. A performance function for each agent under switching topologies is established and a policy iteration algorithm using system data is proposed based on the Bellman optimality principle. The theoretical analysis shows that the consensus error realizes mean-square consensus and the performance function is optimized. The efficacy of the suggested approach is confirmed by numerical simulation using an actor–critic neural network. As a result, the value function is the optimum and the mean-square consensus can be reached using this technique.

Observer-Based Event-Triggered Fuzzy Control for Switched Multi-Agent Systems with State Constraints and Sensor Faults

In this paper, we consider the output feedback event-triggered tracking control problem for a class of switched nonlinear multi-agent systems (MASs) with sensor faults and state constraints. For unknown nonlinear functions in the system, fuzzy logic systems are used to approximate them. To address sensor faults, sensor error compensation coefficients are designed to compensate for the errors caused by sensor faults. Meanwhile, state observer is designed to estimate the unmeasurable states in the system, providing necessary information for control design. Based on directed switching networks containing a directed spanning tree, an event-triggered output feedback controller is designed to enable the MAS to track the leader agent. By constructing a suitable barrier Lyapunov function, the stability of the system is analyzed, and it is proved that the consensus tracking can be achieved without violating the state constraints, the uniform boundedness of all the closed-loop system signals is ensured and the Zeno behavior can be eliminated. Finally, the effectiveness of the proposed algorithm is verified through a simulation example.

Data-Driven Formation Control for Multi-Vehicle Systems Induced by Leader Motion

In this paper, a leader motion mechanism is studied for the finite time achievement of any desired formation of a multi-agent system. The approach adopted in this paper exploits a recent technique based on leader motion to the formation control problem of second-order systems, with a special effort to networks of mobile devices and teams of vehicles. After a thorough description of the problem framework, the leader motion mechanism is designed to accomplish the prescribed formation attainment in finite time. Both asymptotic and transient behavior are thoroughly analyzed, to derive the appropriate analytical conditions for the controller design. The overall algorithm is then finalized by two procedures that allow the exploitation of local data only, and the leader motion mechanism is performed based on data collected by the leader during a preliminary experimental stage. A final section of simulation results closes the paper, confirming the effectiveness of the proposed strategy for formation control of a multi-agent system.

Distributed model predictive control for asynchronous multiagent systems with self‐triggered coordinator

AbstractThis paper investigates a distributed model predictive control strategy for asynchronous nonlinear multiagent systems (MASs) with external disturbances via self‐triggered generators and prediction horizon regulators. First, a shrinking constraint related to the error between the actual state and the predicted state is introduced into the optimal control problem to enhance the robustness of the system. Then, the triggering interval and the corresponding prediction horizon are determined by altering the expression of the Lyapunov function, thus achieving a trade‐off between control performance and energy loss. By implementing the proposed algorithm, the coordination objective of the MASs is achieved under asynchronous communication. Finally, the recursive feasibility and closed‐loop stability are proven successively. An illustrative example is conducted to demonstrate the merits of the presented approach.

Distributed MPC with composite event-triggering mechanisms for nonlinear multi-agent systems

Distributed MPC with composite event-triggering mechanisms for nonlinear multi-agent systems