Heterogeneous Nonlinear Multi-agent Systems Research Articles

Model-free adaptive consensus design for a class of unknown heterogeneous nonlinear multi-agent systems with packet dropouts

This paper studies the consensus problem for a class of unknown heterogeneous nonlinear multi-agent systems via a network with random packet dropouts. Based on the dynamic linearization technique, novel model-free adaptive consensus protocols with the data compensation mechanism are designed for both leaderless and leader-following cases. The advantage of this approach is that only neighborhood input and output data of the agents are required in the protocol design. For the stability analysis, a new Squeeze Theorem based method is developed to derive the theoretic results instead of the traditional contraction mapping principle used in model-free adaptive control. It is shown that the consensus can be achieved for both leaderless and leader-following cases if the communication topology is strongly connected. Finally, numerical simulations verifying the correctness of the theoretical results are given.

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.

Guest Editorial Special Issue on Robust Cooperative Control for Heterogeneous Nonlinear Multiagent Systems

Guest Editorial Special Issue on Robust Cooperative Control for Heterogeneous Nonlinear Multiagent Systems

Distributed Prescribed-Time Consensus Tracking for Heterogeneous Nonlinear Multi-Agent Systems Under Deception Attacks and Actuator Faults

Distributed Prescribed-Time Consensus Tracking for Heterogeneous Nonlinear Multi-Agent Systems Under Deception Attacks and Actuator Faults

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.

Prescribed Performance Fault-Tolerant Control for Synchronization of Heterogeneous Nonlinear MASs Using Reinforcement Learning.

In this article, a novel approach of prescribed performance synchronization control is developed for heterogeneous nonlinear multiagent systems (MASs) subject to unknown actuator faults. Considering that not all followers are able to access the information of the leader, a distributed auxiliary perception system is proposed to estimate the state information of the leader to guarantee that the estimation errors converge to zero within fixed time. Then, based on the estimated states, a prescribed performance fault-tolerant control (FTC) approach is proposed, which achieves the user-defined performance specifications even in the presence of system faults. Moreover, as accurate system dynamic models are perhaps hard to acquire in practical engineering, a data-based method is proposed by using the reinforcement learning (RL) algorithm to design the fault-tolerant controller, which only needs the off-policy online data and is independent of the model dynamics of followers. The stability and synchronization with the prescribed behavior are guaranteed through the Lyapunov stability theorem. Finally, simulation results are presented to illustrate the effectiveness of the developed controller.

Distributed adaptive tracking consensus control for a class of heterogeneous nonlinear multi-agent systems

The proposed approach differs from existing works in that it models the constraints of each follower as a nonlinear strict feedback system, rather than relying on a desired reference trajectory for accessible subsystems. To address the limitations caused by uncertain terms in systems, radial basis functions neural networks are utilized to compensate for these unknown nonlinear terms. This leads to a novel distributed adaptive consensus tracking control protocol for high-order nonlinear heterogeneous multi-agent systems, based on the backstepping technique. By introducing a non-zero parameter in the traditional radial basis functions neural network, a new universal approximation is constructed, which overcomes the limitation of the approximation’s finite domain. Additionally, the approximation precision can be adjusted online using provided laws, and the dimension explosion of virtual and real control gains can be avoided through the use of the designed control approach. Simulation results are provided to demonstrate the effectiveness of the proposed control scheme.

Neural Network-Based Fixed-Time Tracking and Containment Control of Second-Order Heterogeneous Nonlinear Multiagent Systems.

This study concentrates on the fixed-time tracking consensus and containment control of second-order heterogeneous nonlinear multiagent systems (MASs) with and without measurable velocity under directed topology. By defining a time-varying scaling function and approximating the unknown nonlinear dynamics with radial basis function neural networks (RBFNNs), a novel distributed protocol for solving the fixed-time tracking consensus and containment control problems of second-order heterogeneous nonlinear MASs with full states available is proposed based on a nonsingular sliding-mode control method constructed by designing a prescribed-time convergent sliding surface. For the scenario of immeasurable velocity, a fixed-time convergent states' observer is designed to reveal the velocity information when the unknown linearity is bounded. Subsequently, a distributed fixed-time consensus protocol based on observed velocity information is proposed for the extended results. Ultimately, the acquired results are verified by three simulation examples.

Simplified ADP-Based Distributed Event-Triggered Fault-Tolerant Control of Heterogeneous Nonlinear Multiagent Systems With Full-State Constraints

Simplified ADP-Based Distributed Event-Triggered Fault-Tolerant Control of Heterogeneous Nonlinear Multiagent Systems With Full-State Constraints

Model-free adaptive bipartite consensus control for unknown heterogeneous nonlinear MASs with different input delays

In this paper, a novel model-free adaptive bipartite consensus control scheme is proposed for unknown heterogeneous nonlinear multi-agent systems (MASs) with different input delays. Firstly, an equivalent data model is established for each agent by using the dynamic linearisation method. Secondly, based on an established equivalent data model and constructed predictive compensation algorithm, the distributed model-free adaptive bipartite consensus control scheme (DMFABCC) is designed by only utilising the measured input/output data rather than the state-space model of MASs. Next, by selecting an appropriate Lyapunov function, the convergence of bipartite tracking error is rigorously proved. Finally, numerical simulations are conducted to demonstrate the effectiveness of the proposed DMFABCC scheme for achieving the bipartite consensus target.

Leader-following consensus of heterogeneous nonlinear multi-agent systems within arbitrary pre-specified time

Abstract The leader-following consensus problem of second-order heterogeneous nonlinear multi-agent systems (MASs) is studied in this paper. Based on the free-will arbitrary time (FWAT) theory, a piecewise distributed controller is developed to solve this problem within an arbitrary pre-specified convergence time. It should be noted that the pre-specified convergence time is independent of any system parameters and initial conditions. Furthermore, compared with the existing FWAT methods, consensus can still be maintained after the convergence time under the protocol designed in this work. Finally, numerical examples are given to illustrate the validity of the proposed method.

Data-driven adaptive consensus control for heterogeneous nonlinear Multi-Agent Systems using online reinforcement learning

In this paper, a distributed control algorithm based on a data-driven approach is developed to solve the consensus control of heterogeneous nonlinear Multi-Agent Systems (MAS). The consensus obtained from the solution of the Hamilton–Jacobi–Bellman (HJB) equation is challenging for unknown nonlinear systems. To address this issue, improved online reinforcement learning (RL) is employed to generate an approximate solution for each agent to achieve consensus. Unlike model-based RL and traditional algorithms, this method leverages I/O data to guide the learning of policies without any prior knowledge of agent dynamics. Furthermore, the adaptability of algorithm to heterogeneous nonlinear agents is enhanced by implementing online updates to the control strategy and dynamic linearization (DL). The convergence analysis of the algorithm is provided, along with the impact of the learning rate parameters on the consensus of MAS. By comparing with other data-driven methods, simulations are conducted to verify the stability and adaptability of the proposed algorithm.

Quantised adaptive consensus control of heterogeneous nonlinear multi-agent systems under external disturbance

This paper is primarily devoted to the distributed consensus tracking control for heterogeneous nonlinear multi-agent systems (MASs) with a directed communication topology. Taking the considered heterogeneous nonlinear MASs own unknown nonlinear term and quantised input signals, the adaptive radial basis function neural networks (RBFNNs)-based approximator is utilised to tackle these interesting challenges. Next, considering the boundness of disturbances, the upper bound of time-varying disturbance is utilised in the controller design. Then, a distributed dynamic compensator is developed to complete coordination task, which only requires to exchange output information via the communication network. In addition, the compensator transforms the adaptive output consensus problem into asymptotic tracking problem, without needing extra stability conditions. Finally, by means of the Lyapunov stability theory, the satisfying result of the consensus error converging to zero asymptotically can be obtained. Through simulations, the effectiveness of the developed protocol is illustrated.

Finite-Time Time-Varying Formation Tracking for Heterogeneous Nonlinear Multiagent Systems Using Adaptive Output Regulation.

The finite-time output time-varying formation tracking (TVFT) problem for heterogeneous nonlinear multiagent system (MAS) is investigated in this article, where the dynamics of the agents can be nonidentical, and leader's input is unknown. The target of this article is that the outputs of followers need to track leader's output and realize the desired formation in finite time. First, for removing the assumption that all agents are required to know the information of leader's system matrices and the upper boundary of its unknown control input in previous studies, a kind of finite-time observer is constructed by exploiting the neighboring information, which can estimate not only the leader's state and system matrices but also can compensate for the effects of unknown input. On the basis of the developed finite-time observers and adaptive output regulation method, a novel finite-time distributed output TVFT controller is proposed with the help of the technique of coordinate transformation by introducing an extra variable, which removes the assumption that the generalized inverse matrix of follows' input matrix needs to be found in the existing results. By means of the Lyapunov and finite-time stability theory, it is proven that the expected finite-time output TVFT can be realized by the considered heterogeneous nonlinear MASs within a finite time. Finally, simulation results demonstrate the efficacy of the proposed approach.

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.

Couple-group consensus of heterogeneous nonlinear multi-agent systems with cooperative–competitive interactions and input saturation

Couple-group consensus of multi-agent systems with cooperative–competitive interactions and input saturation is studied under fixed and switching topologies based on pinning control and event-triggered control. First, for heterogeneous multi-agent systems consisting of first- and second-order agents, we propose two new distributed event-triggered control protocols, and the control protocols both enable the groups of agents to achieve the desired consensus state, which can effectively reduce the number of controller updates and save communication resources. Second, based on the Lyapunov stability theory, the sufficient conditions are established for the couple-group consensus of multi-agent systems. Meanwhile, Zeno behavior is excluded. Third, the effectiveness and feasibility of the control methods proposed in this paper are verified via some simulation examples.

Robust Consensus Tracking Strategy of Heterogeneous Nonlinear Multi-Agent Systems With Time-Varying Input Delays

Robust Consensus Tracking Strategy of Heterogeneous Nonlinear Multi-Agent Systems With Time-Varying Input Delays

Data-Driven Distributed Predictive Tracking Control for Heterogeneous Nonlinear Multi-Agent Systems With Communication Delays

Data-Driven Distributed Predictive Tracking Control for Heterogeneous Nonlinear Multi-Agent Systems With Communication Delays

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.

Deep Luenberger observer-based consistency tracking for nonlinear heterogeneous multi-agent systems with uncertain drift dynamics

In recent years, multi-agent systems (MASs) technology, represented by swarms of unmanned aerial vehicles (UAVs), has been continuously developed and become a hot topic in academic research. Combining the theory of deep learning (DL) and Luenberger observer, this paper puts forward the tracking control problem for the nonlinear heterogeneous MASs with uncertain drift dynamics. Concretely, a novel class of deep Luenberger observer (DLO) is constructed for state estimation and a novel deep robust controller (DRC) is designed based on the proposed DLOs. Finally, a numerical simulation demonstrates that the proposed methodology is valid, more general and performs better compared to traditional robust control methods.