Multi-agent Research Articles

Optimized distributed formation control using identifier–critic–actor reinforcement learning for a class of stochastic nonlinear multi-agent systems

This article is to propose an adaptive reinforcement learning (RL)-based optimized distributed formation control for the unknown stochastic nonlinear single-integrator dynamic multi-agent system (MAS). For solving the issue of unknown dynamic, an adaptive identifier neural network (NN) is developed to determine the stochastic MAS under expectation sense. And then, for deriving the optimized formation control, the RL is putted into effect via constructing a pair of critic and actor NNs. With regard of the traditional RL optimal controls, their algorithm exists the inherent complexity, because their adaptive RL algorithm are derived from negative gradient of the square of Hamilton–Jacobi–Bellman (HJB) equation. As a result, these methods are difficultly extended to stochastic dynamical systems. However, since this adaptive RL laws are derived from a simple positive function rather than the square of HJB equation, it can make optimal control with simple algorithm. Therefore, this optimized formation scheme can be smoothly performed to the stochastic MAS. Finally, according to theorem proof and computer simulation, the optimized method can realize the required control objective.

Optimal consensus control of dynamically interconnected multi-agent systems: A SDRE approach for efficient and stable operation

This study focuses on achieving optimal consensus control based on the state-dependent Riccati equation (SDRE) in multi-agent systems (MASs) characterized by the interconnectivity of multiple sub-systems with physical interconnections. The objective is to establish a suitable communication topology among the MAS controllers that considers both local and global objectives while ensuring stability through distributed control. To address this, an innovative extension of leader-follower and leaderless consensus control methodologies is proposed, accompanied by stability analysis employing the Lyapunov method. Additionally, the paper explores consensus control for dynamically interconnected MASs handling suspended load transportation, such as multi-lift systems. By utilizing the principles of virtual work by D'Alembert and Gauss's principle of least constraint, a constrained multibody system model is developed, incorporating the Udwadia-Kalaba motion equation approach. Simulation results and theoretical analysis validate the efficiency and performance of the proposed consensus controller structure.

Adaptive Neurodynamic Approach to Multiple Constrained Distributed Resource Allocation.

In this article, an adaptive neurodynamic approach over multiagent systems is designed to solve nonsmooth distributed resource allocation problems (DRAPs) with affine-coupled equality constraints, coupled inequality constraints, and private set constraints. It is to say, agents focus on tracking the optimal allocation to minimize the team cost under more general constraints. Among the considered constraints, multiple coupled constraints are dealt with by introducing auxiliary variables to make Lagrange multipliers reach consensus. Furthermore, aiming to address private set constraints, an adaptive controller is proposed with the aid of the penalty method, thus avoiding the disclosure of global information. Through using the Lyapunov stability theory, the convergence of this neurodynamic approach is analyzed. In addition, to reduce the communication burden of systems, the proposed neurodynamic approach is improved by introducing an event-triggered mechanism. In this case, the convergence property is also explored, and the Zeno phenomenon is excluded. Finally, a numerical example and a simplified problem on a virtual 5G system are implemented to demonstrate the effectiveness of the proposed neurodynamic approaches.

Robust Cooperative Control for Heterogeneous Uncertain Nonlinear High-Order Fully Actuated Multiagent Systems.

This research is intended to address a robust cooperative control problem of heterogeneous uncertain nonlinear high-order fully actuated multiagent systems (HUN-HOFAMASs). A nonlinear HOFA system model is used to describe the multiagent systems (MASs) with heterogeneous uncertain nonlinear dynamics, which is called the HUN-HOFAMASs. A predictive terminal sliding-mode control-based robust cooperative control scheme is presented to address this problem. In this scheme, heterogeneous nonlinear dynamics of original system are offset to establish a linear constant HOFA system with the help of full actuation feature. Then, a terminal sliding-mode variable for enhancing the system robustness is introduced to handle the uncertainties. Furthermore, a linear incremental prediction model is developed in a HOFA form by means of a Diophantine equation. According to this model, the multistep terminal sliding-mode predictions are yielded to optimize the robust cooperative control performance and compensate for the network-induced communication constraints in the feedback and forward channels. Based on a linear matrix inequality (LMI) method, a necessary and sufficient criterion is derived to discuss the simultaneous consensus and stability of closed-loop HUN-HOFAMASs. The simulation and comparison results of cooperative flying around of multiple spacecraft system are shown to illustrate the capability and advantage of the presented predictive terminal sliding-mode control for robust cooperative control.

Quantization-Based Adaptive Fuzzy Consensus for Multiagent Systems Under Sensor Deception Attacks: A Novel Compensation Mechanism.

This study mainly investigates the adaptive leader-following consensus tracking control problem for a class of nonlinear multiagent systems (MASs) subjected to unknown control directions, external disturbances, and sensor deception attacks. To start with, an equivalent MAS with known control directions is obtained by introducing a linear state transformation. For the purpose of estimating the unavailable system states caused by malicious attacks, a quantization-based fuzzy state observer is designed, and the fuzzy-logic system (FLS) is utilized to approximate nonlinear functions. Moreover, a dynamic uniform quantizer with scaling function is established to reduce information transmission. With the help of coordinate transformation and available compromised states, a novel compensation mechanism is designed to offset the influence of filter errors while avoiding the problem of "explosion of complexity" in the backstepping design process. In addition, the Nussbaum-type function is considered to eliminate the design obstacle of unknown control gains resulting from the attacks. Under the constructed consensus protocol, it is proved theoretically that the consensus tracking error converges to an adjustable small neighborhood of the origin, and all signals in the closed-loop system are bounded. Finally, the feasibility of the provided secure control scheme is verified through two simulation examples.

Dynamic event-triggered consensus for stochastic delay multi-agent systems under directed topology

This paper addresses the mean-square exponential consensus (MSEC) challenge in stochastic delay multi-agent systems (MASs) under directed topology. The main contributions include avoiding infinitely fast sampling and conducting a random stability analysis. First, a dynamic event-triggered mechanism (DETM) with a fixed positive lower bound for the inter-execution time is designed to prohibit Zeno behavior. The proposed control strategy does not involve the expectations of system signals. Then, sampling errors are estimated using Itô isometry, which ensures that the fixed control at each work interval is maintained with adequate feedback capacity to mitigate the effects of these errors. Furthermore, with the help of a Halanay inequality in integral form and Lyapunov theorem framework, some conditions are proposed to guarantee the consensus of stochastic delay MASs under directed topology. Finally, an example is presented to prove the effectiveness of the DETM method.

Optimal Consensus Control for Continuous-Time Linear Multiagent Systems: A Dynamic Event-Triggered Approach.

This article investigates the optimal consensus problem for general linear multiagent systems (MASs) via a dynamic event-triggered approach. First, a modified interaction-related cost function is proposed. Second, a dynamic event-triggered approach is developed by constructing a new distributed dynamic triggering function and a new distributed event-triggered consensus protocol. Consequently, the modified interaction-related cost function can be minimized by applying the distributed control laws, which overcomes the difficulty in the optimal consensus problem that seeking the interaction-related cost function needs all agents' information. Then, some sufficient conditions are obtained to guarantee optimality. It is shown that the developed optimal consensus gain matrices are only related to the designed triggering parameters and the desirable modified interaction-related cost function, relaxing the constraint that the controller design requires the knowledge of system dynamics, initial states, and network scale. Meanwhile, the tradeoff between optimal consensus performance and event-triggered behavior is also considered. Finally, a simulation example is provided to verify the validity of the designed distributed event-triggered optimal controller.

A Novel Parallel Control Method for Optimal Consensus of Nonlinear Multiagent Systems.

This work concentrates on the initial introduction of parallel control to investigate an optimal consensus control strategy for continuous-time nonlinear multiagent systems (MASs) via adaptive dynamic programming (ADP). First, the control input is integrated into the feedback system for parallel control, facilitating an augmented system's optimal consensus control with an appropriate augmented performance index function to be established, which is identical to the original system's suboptimal control with a conventional performance index. Second, the feasibility of the proposed control scheme is evaluated based on the policy iteration algorithm, and the convergence of the algorithm is demonstrated. Then, an online learning algorithm becomes available to implement the ADP-based optimal parallel consensus control protocol without prior knowledge of the system. The Lyapunov approach is employed to indicate that the signals are convergent. Ultimately, the experimental data support the theoretical results.

Optimized Backstepping-Based Containment Control for Multiagent Systems With Deferred Constraints Using a Universal Nonlinear Transformation.

This article investigates an optimized containment control problem for multiagent systems (MASs), where all followers are subject to deferred full-state constraints. A universal nonlinear transformation is proposed for simultaneously handling the cases with and without constraints. Particularly, for the constrained case, initial values of states are flexibly managed to the midpoint between upper and lower boundaries by utilizing a state-shifting function, thus eliminating the initial restriction conditions. By deferred constraints, the state is forced to fall back into the restrictive boundaries within a preassigned time. A neural network (NN)-based reinforcement learning (RL) algorithm is executed under the identifier-critic-actor architecture, where the Hamilton-Jacobi-Bellman (HJB) equation is built in every subsystem to optimize control performance. For actor and critic NNs, updating laws are simplified, since the gradient descent method is performed based on a simple positive function rather than square of Bellman residual error. In view of the Lyapunov stability theorem and graph theory, it is proved that all signals are bounded and the outputs of followers can eventually enter into the convex hull constituted by leaders. Finally, simulations confirm the validity of the proposed approach.

Mixed-Zero-Sum-Game-Based Memory Event-Triggered Cooperative Control of Heterogeneous MASs Against DoS Attacks.

This article studies the problem of memory event-triggered cooperative adaptive control of heterogeneous nonlinear multiagent systems (MASs) under denial-of-service (DoS) attacks based on the multiplayer mixed zero-sum (ZS) game strategy. First, a neural-network-based reinforcement learning scheme is structured to obtain the Nash equilibrium solution of the proposed multiplayer mixed ZS game scheme. Then, a memory-based event-triggered mechanism considering the historical data is proposed. This effectively avoids incorrect triggering information caused by unknown external factors. Moreover, thanks to the idea of switching topology, the mixed ZS game problem under the influence of node-based DoS attacks is solved efficiently. In accordance with the Lyapunov stability theory, it is proved that all signals of heterogeneous MASs are bounded, all heterogeneous followers can track the trajectory of the leader during the no-attack period, the attacked follower can achieve stabilization control during the attack period, and the remaining nonattacked followers can achieve cooperative control during the attack period. Finally, the effectiveness of the designed memory-event-triggered-based mixed ZS game cooperative control strategy is tested by the given simulation results.

Predefined Accuracy Adaptive Tracking Control for Nonlinear Multiagent Systems With Unmodeled Dynamics.

This article focuses on an adaptive dynamic surface tracking control issue of nonlinear multiagent systems (MASs) with unmodeled dynamics and input quantization under predefined accuracy. Radial basis function neural networks (RBFNNs) are employed to estimate unknown nonlinear items. A dynamic signal is established to handle the trouble introduced by the unmodeled dynamics. Moreover, the predefined precision control is realized with the aid of two key functions. Unlike the existing works on nonlinear MASs with unmodeled dynamics, to avoid the issue of "explosion of complexity," the dynamic surface control (DSC) method is applied with the nonlinear filter. By using the designed controller, the consensus errors can gather to a precision assigned a priori. Finally, the simulation results are given to demonstrate the effectiveness of the proposed strategy.

Comparative Analysis of Single and Multiagent Large Language Model Architectures for Domain-Specific Tasks in Well Construction

Summary This paper explores the application of large language models (LLMs) in the oil and gas (O&G) sector, specifically within well construction and maintenance tasks. The study evaluates the performances of a single agent and a multiagent LLM-based architecture in processing different tasks, offering a comparative perspective on their accuracy and the cost implications of their implementation. The results indicate that multiagent systems offer improved performance in question and answer (Q&A) tasks, with a truthfulness measure 28% higher than single-agent systems but at a higher financial cost. Specifically, the multiagent architecture incurs costs that are, on average, 3.7 times higher than those of the single-agent setup due to the increased number of tokens processed. Conversely, single-agent systems excel in Text-to-SQL (structured query language) tasks, particularly when using the Generative Pre-Trained Transformer 4 (GPT-4), achieving a 15% higher score compared to multiagent configurations, suggesting that simpler architectures can sometimes outpace complexity. The novelty of this work lies in its original examination of the specific challenges presented by the complex, technical, unstructured data inherent in well construction operations, contributing to strategic planning for adopting generative artificial intelligence (AI) (Gen-AI) applications and providing a basis for optimizing solutions against economic and technological parameters.

Adaptive Finite-Time Consensus Tracking Control for Nonlinear Multi-Agent Systems: An Improved Tan-Type Nonlinear Mapping Function Method

Adaptive Finite-Time Consensus Tracking Control for Nonlinear Multi-Agent Systems: An Improved Tan-Type Nonlinear Mapping Function Method

Adaptive Asymptotic Tracking Funnel Constraint Control for High-Power Nonlinear Multi-Agent Systems With Non-Affine Nonlinear Faults and Fixed-Time Convergence

Adaptive Asymptotic Tracking Funnel Constraint Control for High-Power Nonlinear Multi-Agent Systems With Non-Affine Nonlinear Faults and Fixed-Time Convergence

Rigid Formation Control on a Sphere: A Heterogeneous System Approach.

We consider a heterogeneous multiagent system for tracking multiple targets with a rigid formation on a unit sphere, where the targets and chasing agents are governed by single- and double-integrator models, respectively. To make asymptotic rendezvous between agents and their corresponding targets, we use an autonomous system consisting of attraction forces and velocity alignments. If the target's position, velocity, and acceleration information are available, we derive a multiagent system for complete rendezvous and obtain its exponential convergence result. If we have only the location and velocity information of the targets, we provide an autonomous system for practical rendezvous and the corresponding mathematical analysis. To prove the main results, we employ frame-rotation-structure decomposition for the double-integrator model and the geometric properties of a rigid formation on a sphere. We also provide numerical simulations to confirm our mathematical results and apply them to multiagent dynamics with a rigid formation that patrols the boundary line for a certain area on the sphere.

A PI+R Control Scheme Based on Multi-Agent Systems for Economic Dispatch in Isolated BESSs

A PI+R Control Scheme Based on Multi-Agent Systems for Economic Dispatch in Isolated BESSs

Resolving the Resource Decision-Making Dilemma of Leaderless Group-Based Multiagent Systems and Repeated Games

Resolving the Resource Decision-Making Dilemma of Leaderless Group-Based Multiagent Systems and Repeated Games

Robust Predefined Output Containment for Heterogeneous Nonlinear Multiagent Systems Under Unknown Nonidentical Leaders' Dynamics.

This article discusses the robust predefined output containment (RPOC) control problem for heterogeneous nonlinear multiagent systems having multiple uncertain nonidentical leaders. In order to solve this problem, a new kind of distributed observer-based RPOC control framework is presented. First, for obtaining the information of nonidentical leaders' dynamics, including uncertain parameters in leaders' system matrices, output matrices, states, and outputs, four kinds of adaptive observers are constructed in a fully distributed form without any knowledge of the dynamics of nonidentical leaders, exactly. Second, on the basis of adaptive learning technique, a new RPOC controller is then developed by using the presented observers, where the adaptive observers can make up for the uncertain parameter in followers' dynamics, and the solutions of output regulation equations can be obtained adaptively by the developed adaptive strategy. Furthermore, with the help of the output regulation method and Lyapunov stability theory, the RPOC criteria for the considered system under unknown nonidentical leaders' dynamics are derived from the constructed controller. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed RPOC controller.

Funnel-Based Cooperative Output Regulation Control for Uncertain Nonlinear Multiagent Systems Under Input Saturation

Funnel-Based Cooperative Output Regulation Control for Uncertain Nonlinear Multiagent Systems Under Input Saturation

Effect of repulsive interaction and initial velocity on collective motion process.

Self-propelled collective motion is a highly complex phenomenon, necessitating advanced practical and theoretical tools for comprehension. The significance of studying collective motion becomes apparent in its diverse applications. For instance, addressing evacuation challenges in scenarios with multiple agents can be achieved through an examination of collective motion. Research indicates that the transition of individuals (such as birds, fish, etc.) from a state of rest to equilibrium constitutes a phase transition. Our interest of the issue is to delve into the nature of this transitional phase and elucidate the parameters that shape it. Hence, the primary aim of this paper is to grasp the kinetic phase transition by examining how initial velocity and repulsive interactions impact the dynamics of the system. To gain insight into the complex behavior of multi-agent systems, we apply an extended version of the classical Vicsek model. This extension includes an additional interaction zone, the repulsive zone, where particles repel each other at close range to avoid collisions. Our study uses numerical simulations to explore the system's behavior under various conditions. The focus of this study is the impact of initial velocity on the collective movement of particles. The importance of this research lies in comprehending how velocity affects the overall movement. The conclusion we can draw from these results is that the initial velocity affects both the noise and the density. The novelty of the work is the transition phase, yet it lacks universal characteristics because the critical noise depends on the initial velocity system and the repulsion radius zone. Notably, the repulsion radius and particle density play pivotal roles in achieving a phase transition from one equilibrium state to another aligned equilibrium state.