Heterogeneous Multiagent Research Articles

Model-Free Containment Control of Fully Heterogeneous Linear Multiagent Systems

Model-Free Containment Control of Fully Heterogeneous Linear Multiagent Systems

Optimal Formation Control of Second-Order Heterogeneous Multiagent Systems Using Adaptive Predefined-Time Strategy

Optimal Formation Control of Second-Order Heterogeneous Multiagent Systems Using Adaptive Predefined-Time Strategy

Resilient Time-Varying Formation Tracking for Heterogeneous High-Order Multiagent Systems With Multiple Dynamic Leaders

This paper investigates time-varying output formation tracking problems for heterogeneous multiagent systems in an adversarial environment, where both leaders and followers are vulnerable to cyber-attacks. Despite the presence of adversarial agents, the normal followers not suffering from cyber-attacks seek to achieve a desired for-mation configuration and track a safe trajectory generated by normal leaders' outputs simultaneously. To this end, a resilient time-varying output formation (RTVOF) tracking scheme is presented in this work. Firstly, to counteract the effect of adversarial agents, a safety strategy is proposed for general linear heterogeneous systems, which is a syn-thesis of the mean-subsequence-reduced family algorithms and an attack detection procedure. Secondly, using the neighboring interaction, a resilient distributed observer and a local formation controller are designed for each normal follower to estimate the state and track the output of the safe formation reference. Then, it is proved that the hetero-geneous multiagent system with multiple dynamic leaders and time-varying topologies can accomplish the RTVOF tracking if specific graph-theoretic properties and forma-tion feasibility conditions are satisfied. Finally, simulation examples are given to demonstrate the effectiveness of obtained results.

Neuroadaptive Output Formation Tracking for Heterogeneous Nonlinear Multiagent Systems With Multiple Nonidentical Leaders.

This article investigates the practical time-varying output formation tracking (TVOFT) problem for heterogeneous nonlinear multiagent systems (MASs) having multiple leaders, where agents herein could have heterogeneous dynamics and interact with each other under event-triggered communications. It is required that the outputs of followers not only track the predefined convex combination of multiple leaders but also achieve the desired time-varying formation simultaneously. The existing works on formation tracking problems for MASs with multiple leaders depend on the assumption that each follower is a well-informed or uninformed follower, where the well-informed follower is required to have all the leaders as its neighbor. To remove the limitation, a fully distributed observer-based formation tracking control protocol is developed and employed. First, an adaptive state observer with an edge-based event-triggered mechanism for estimating the states of multiple leaders is proposed based on the neighboring interactions, which eliminates the unexpected Zeno behavior. Second, a novel observer is constructed for each follower by exploiting the output information of the follower, in which the adaptive neural network (NN)-based approximation is exploited to compensate for the unknown nonlinearity. A practical TVOFT control protocol is then generated by the proposed observers, where the parameters are determined by an algorithm including five steps. With the help of Lyapunov stability theory and output regulation method, a practical TVOFT criterion for the considered closed-loop system is derived. Finally, the effectiveness of the proposed control scheme is illustrated by a numerical example.

Resilient Consensus Control for Heterogeneous Multiagent Systems via Multiround Attack Detection and Isolation Algorithm

Resilient Consensus Control for Heterogeneous Multiagent Systems via Multiround Attack Detection and Isolation Algorithm

Funnel-Based Predefined-Time Containment Control of Heterogeneous Multiagent Systems With Sensor and Actuator Faults

Funnel-Based Predefined-Time Containment Control of Heterogeneous Multiagent Systems With Sensor and Actuator Faults

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

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

Prior Knowledge-Augmented Broad Reinforcement Learning Framework for Fault Diagnosis of Heterogeneous Multiagent Systems

A heterogeneous multiagent system (MAS) can easily experience unpredictable faults due to its complex structure and involvement of different individuals. However, existing approaches have several issues, including complicated network architecture, insufficient feature extraction, and poor generalization ability. This study proposes a novel framework called prior-knowledge-augmented broad reinforcement learning (PK-BRL) to effectively diagnose faults in a heterogeneous MAS. First, we construct a highly realistic visualized heterogeneous MAS and perfom fault injection. Second, we present a novel fault diagnosis framework based on broad reinforcement learning with prior knowledge that effectively integrates offline reinforcement learning and broad learning into the fault diagnosis process. The interaction between heterogeneous multiagents and the constructed environment enables us to learn a superior fault diagnosis strategy. Finally, experiments conducted on software-in-the-loop and hardware-in-the-loop platforms demonstrate that the proposed PK-BRL framework has a state-of-the-art diagnostic accuracy for the heterogeneous MAS, which offers valuable theoretical and practical significance for real-world application.

Optimal Output Consensus of Heterogeneous Linear Multiagent Systems Over Weight-Unbalanced Directed Networks.

This article investigates the distributed optimal output consensus problem of heterogeneous linear multiagent systems over weight-unbalanced directed networks. A novel distributed continuous-time state feedback controller is proposed to steer the outputs of all the agents to converge to the optimal solution of the global cost function. Under the standard condition that the unbalanced digraph is strongly connected and the local cost functions are strongly convex with global Lipschitz gradients, the exponential convergence of the closed-loop multiagent system is established. Then, the proposed state feedback control law is extended to an observer-based output feedback setting. Two examples are finally provided to illustrate the effectiveness of the proposed control schemes.

Fixed-Time Anti-Disturbance Average-Tracking for Heterogeneous Linear Multiagent Systems

Fixed-Time Anti-Disturbance Average-Tracking for Heterogeneous Linear Multiagent Systems

Prescribed-Time Cooperative Output Regulation of Heterogeneous Multiagent Systems

Prescribed-Time Cooperative Output Regulation of Heterogeneous Multiagent Systems

Data-Based Optimal Synchronization of Heterogeneous Multiagent Systems in Graphical Games via Reinforcement Learning.

This article studies the optimal synchronization of linear heterogeneous multiagent systems (MASs) with partial unknown knowledge of the system dynamics. The object is to realize system synchronization as well as minimize the performance index of each agent. A framework of heterogeneous multiagent graphical games is formulated first. In the graphical games, it is proved that the optimal control policy relying on the solution of the Hamilton-Jacobian-Bellmen (HJB) equation is not only in Nash equilibrium, but also the best response to fixed control policies of its neighbors. To solve the optimal control policy and the minimum value of the performance index, a model-based policy iteration (PI) algorithm is proposed. Then, according to the model-based algorithm, a data-based off-policy integral reinforcement learning (IRL) algorithm is put forward to handle the partially unknown system dynamics. Furthermore, a single-critic neural network (NN) structure is used to implement the data-based algorithm. Based on the data collected by the behavior policy of the data-based off-policy algorithm, the gradient descent method is used to train NNs to approach the ideal weights. In addition, it is proved that all the proposed algorithms are convergent, and the weight-tuning law of the single-critic NNs can promote optimal synchronization. Finally, a numerical example is proposed to show the effectiveness of the theoretical analysis.

Heterogeneous Unknown Multiagent Systems of Different Relative Degrees: A Distributed Optimal Coordination Design

Heterogeneous Unknown Multiagent Systems of Different Relative Degrees: A Distributed Optimal Coordination Design

PDE-Based Deployment of Heterogeneous Nonlinear Multiagents: A Single-Point Control Scheme

PDE-Based Deployment of Heterogeneous Nonlinear Multiagents: A Single-Point Control Scheme

Fully Distributed Tracking Control for Heterogeneous Multiagent Systems With a Noncooperative Leader Over Event-Triggered Communication

Fully Distributed Tracking Control for Heterogeneous Multiagent Systems With a Noncooperative Leader Over Event-Triggered Communication

Enhancing Collaboration in Heterogeneous Multiagent Systems Through Communication Complementary Graph.

Heterogeneous multiagent systems are characterized by diverse task distributions, which are prevalent in practical scenarios, such as distributed decision making and robotic collaboration. A significant challenge in these systems is the constraint of limited observations, where each agent has access only to partial information. Many studies facilitate information exchange by employing shared parameters among agents. However, this approach is generally more effective for homogeneous systems where agents have similar observation or action spaces. In heterogeneous systems, indiscriminate parameter sharing can significantly increase the exploration cost required for effective adaptation. To address this challenge, we propose a novel communication complementary graph model (CCGM) for enhancing collaboration in heterogeneous multiagent systems. Our approach builds upon the training framework of heterogeneous agent reinforcement learning (HARL) with trust region learning and nonparameter sharing. This model utilizes advantage function decomposition and sequential updates to promote policy convergence. Within this framework, we introduce a novel communication method inspired by signaling games, where agents acting as receivers, process messages from other agents alongside their own observations. CCGM aligns the messages with observations in a graph-based communication module, which establishes communication relationships and supplements observational information. Subsequently, agents generate self-interested information, which they then share with others as senders. We evaluate our algorithm across various environments, including multiagent particle environments (MPE) and multiagent MuJoCo (MAMuJoCo) robot experiments. The results demonstrate the effectiveness of CCGM in enhancing HARL-based algorithms.

Adaptive Dynamic Event-Triggered Bipartite Time-Varying Output Formation Tracking Problem of Heterogeneous Multiagent Systems

Adaptive Dynamic Event-Triggered Bipartite Time-Varying Output Formation Tracking Problem of Heterogeneous Multiagent Systems

Further Results on Fixed-Time Leader-Following Consensus of Heterogeneous Multiagent Systems With External Disturbances

Further Results on Fixed-Time Leader-Following Consensus of Heterogeneous Multiagent Systems With External Disturbances

Dynamic Leader–Follower Output Containment Control of Heterogeneous Multiagent Systems Using Reinforcement Learning

Dynamic Leader–Follower Output Containment Control of Heterogeneous Multiagent Systems Using Reinforcement Learning

A Modular Event-Triggered Containment Control Scheme for Nonlinear Heterogeneous Multiagent Systems With Unknown Leaders.

This article addresses the containment control problem in multiagent systems with nonlinear heterogeneous followers and multiple unknown leaders whose dynamics are exclusively known to their neighbors. The primary goal is to ensure the convergence of each follower to the dynamic convex hull spanned by the leaders under the constraints of limited communication resources. To achieve this, this article introduces a modular event-triggered containment control scheme with three modules. The first module, Module I-signal generator, is designed for each follower to generate a reference signal asymptotically entering the dynamic convex hull without relying on follower dynamics. The second module, Module II-event-triggered mechanism, is tailored to save communication resources effectively by determining when to broadcast information based on perturbed system stability and input-to-state stability theories. The third module, Module III-tracking controller, treats each follower as an independent agent and is crafted to track the reference signal generated by Module I using an output regulation approach. It is established that the system achieves containment control without Zeno behavior under the influence of these modules, and the theoretical results are validated through simulation examples, demonstrating the practical validity of the proposed approach.