Control Problem For Multi-agent Systems Research Articles

Distributed adaptive event-triggered asymptotic tracking control of linear uncertain multiagent systems by using output only

For the robust tracking control problem of multiagent systems with linear uncertainties, most of the existing studies require full state feedback. Moreover, the designed control strategy also uses global information, such as the number of agents or the eigenvalues of the Laplacian matrix, which does not satisfy the distributed requirement for multiagent design. To address these problems, first, a Luenburger observer is designed to observe the full state of the system by using the output information. Second, the constant gain in the traditional distributed observer for estimating exosystem state information is replaced with a time-varying gain that comes from the output of a newly designed filter, which avoids the use of the global information mentioned above. Finally, based on the above observer and adaptive dynamic feedback, an event-triggered output feedback control strategy is given. This control strategy is not only piecewise constant, which is easy to implement, but also makes the robust tracking control error converge asymptotically to zero. Furthermore, it is theoretically proven that each agent does not have Zeno behaviour, and the effectiveness of the designed control strategy is verified with a simulation example.

Distributed Fault-Tolerant Containment Control Protocols for the Discrete-Time Multiagent Systems via Reinforcement Learning Method.

This article investigates the model-free fault-tolerant containment control problem for multiagent systems (MASs) with time-varying actuator faults. Depending on the relative state information of neighbors, a distributed containment control method based on reinforcement learning (RL) is adopted to achieve containment control objective without prior knowledge on the system dynamics. First, based on the information of agent itself and its neighbors, a containment error system is established. Then, the optimal containment control problem is transformed into an optimal regulation problem for the containment error system. Furthermore, the RL-based policy iteration method is employed to deal with the corresponding optimal regulation problem, and the nominal controller is proposed for the original fault-free system. Based on the nominal controller, a fault-tolerant controller is further developed to compensate for the influence of actuator faults on MAS. Meanwhile, the uniform boundedness of the containment errors can be guaranteed by using the presented control scheme. Finally, numerical simulations are given to show the effectiveness and advantages of the proposed method.

An RNN-Based Performance Identification Model for Multi-Agent Containment Control Systems

In the containment control problem of multi-agent systems (MASs), the convergence of followers is always a potential threat to the security of system operations. From the perspective of system topology, the inherently non-linear properties of the algebraic connectivity of the follower2follower (F2F) network, combined with the influence of the leader2follower (L2F) topology on the system, make it difficult to design the convergence positions of the followers through mere mathematical analysis. Therefore, in the background of temporary networking tasks for large-scale systems, to achieve the goal of forecasting the performance of the whole system when networking is only completed with local information, this paper investigates the application and effectiveness of recurrent neural networks (RNNs) in the containment control system performance identification, thus improving the efficiency of system networking while ensuring system security. Two types of identification models based on two types of neural networks (NNs), MLP and standard RNN are developed, according to the range of information required for performance identification. Evaluation of the models is carried out by means of the coefficient of determination (R2) as well as the root-mean-square error (RMSE). The results show that each model may produce a better forecasting accuracy than the other models in specific cases, with models based on the standard RNN possessing smaller errors. With the proposed method, model identification can be achieved, but in-depth development of the model in further studies is still necessary to the extent the accuracy of the model.

NNs-observer-based fully distributed consensus control for MASs under deception attacks

This paper proposes a novel resilient intrusion-tolerance control scheme to solve the consensus control problem of multi-agent systems (MASs) under deception attacks. Consider the state unavailability of MASs with unknown nonlinear terms, the unmeasurable state is estimated by designing the neural-networks(NNs)-based observer. Then, a fully distributed intrusion-tolerant adaptive protocol is developed to implement the consensus control task. The designed control strategy only involves the agent states that interact with the neighboring agent and does not relate to the whole communication topology information. The designed controller can ensure that the consensus error of the nonlinear MASs is eventually ultimately bounded under deception attacks. Finally, a numerical simulation is provided to verify the feasibility of the designed scheme.

Bipartite Flocking Control for Multi-Agent Systems With Cooperation-Competition Interactions and Random Packet Dropouts

This paper addresses the bipartite flocking control problem of multi-agent systems with random packet dropouts, in which cooperative and competitive information interactions among agents are described by a signed network. The random packet dropouts are denoted by Bernoulli random variable and the information transmission failure between adjacent agents is possible at every time step. In this case, the unreliable information transmission within multi-agent systems may lead to behavioral dispersion and instability. To solve this problem, a novel fully distributed control scheme is designed. In light of the convergence method for products of infinite sub-stochastic matrices, the bipartite flocking control can be global almost sure to achieve and some essentially algebraic conditions are established. Finally, the effectiveness of the control scheme under random packet dropouts is illustrated by a numerical simulation example.

Prescribed-time containment control of high-order nonlinear multi-agent systems based on distributed observer

This paper investigates the prescribed-time containment control problem for multi-agent systems with high-order nonlinear dynamics under a directed communication topology. Firstly, in view of the fact that only some follower agents can directly access the state information of multiple leader agents, a prescribed-time distributed observer is put forward to estimate the convex hull spanned by these leaders. Then, with the help of the distributed observer, a novel containment control method is developed for each follower based on a time-varying scaling function, so that all followers can converge to the convex hull spanned by the states of multiple leaders within a prescribed time. The comparison with the finite-time and fixed-time control methods differs in that the convergence time of the method proposed in this paper is independent of the initial conditions and control parameters and can be arbitrarily preassigned according to actual needs. Finally, an example is given to demonstrate the usefulness of the prescribed-time containment control method.

Data-Driven Tracking Control for Multi-Agent Systems With Unknown Dynamics via Multithreading Iterative Q-Learning

This article addresses the tracking control problem of multiagent systems (MASs) with unknown dynamics. First, by designing a compensator, an augmented neighborhood error system is proposed. Then, a multithreading iterative <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning algorithm is developed, which can transform the tracking control problem into the optimal regulation of the augmented error system. In this algorithm, the agent controller is multithread, which consists of thread unit, global unit, and replay buffer. The thread unit is used to create the thread to collect the transitions with different control policies, which can reduce data relativity and improve data collection speed and exploration capability. The replay buffer is used to store the generated transitions. The global unit is used to generate the control policy. Moreover, the convergence analysis of the proposed algorithm is given, which shows that the neighborhood error converges to zero and the tracking control problem can be solved. Finally, the numerical example not only verifies the effectiveness of the obtained results but also shows the proposed algorithm performs better than the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -iterative algorithm.

Bipartite consensus tracking control for periodically-varying-delayed multi-agent systems with uncertain switching topologies

This paper studies the bipartite tracking control problem for multi-agent systems (MASs). The purpose of this paper is to propose novel consensus criteria and further design the bipartite tracking controller subject to uncertain switching topologies and time delays. By constructing an improved monotone-delay-interval-based Lyapunov functional (MDIBLF), we establish lower conservatism consensus conditions than those of previous results. Moreover, the periodically-varying delay is considered in consensus tracking protocol for MASs. It is highlighted that a new zero equation is introduced to deal with the double integral term in the Lyapunov functional. And the quadratic function negative-determination lemma is not required in our method. Applying the proposed approach, we obtain a bipartite controller for the followers to track the leader. Finally, to demonstrate the superiority of the presented method, two simulation examples based on quadrotors and spacecraft formation flying model are introduced.

Frequency domain cascade consensus and disturbance rejection control of multi-agent systems

This work describes a cascade consensus control problem of multi-agent systems (MASs) under disturbances in frequency domain (FD). An internal model control–based scheme, a kind of FD scheme is utilized to address the network of agents. Each agent of the network consists of two loops control configuration. Inner-loop control is responsible for single-agent control and eliminates the effects of internal disturbances. Whereas the outer-loop controller works as multi-agent control and eliminates the effects of external disturbances. For both loops, robust quasi-[Formula: see text] controller is derived for disturbance rejection and resilient consensus of all agents. This scheme is novel in the sense that it can eliminate the impacts of disturbances on the MAS without the restriction of the number of nodes. An analytical scheme is utilized for the tuning of the designed controller to track the consensus performance of the network. Numerical examples are given to describe the validity of the proposed algorithm.

Dynamic Target Enclosing Control Scheme for Multi-Agent Systems via a Signed Graph-Based Approach

Dear Editor, This letter investigates the target enclosing control problem of multi-agent systems. A signed graph-based control strategy is presented, where the agents are steered to enclose the dynamic target from both sides as they move. This is inspired by the phenomenon that signed networks exhibit bipartite clustering if the underlying graph is structurally balanced, so that the agents may naturally enclose the zero point from opposite sides (+ and -) if proper controllers are applied. By adopting a distributed observer to estimate the information of dynamic target, a consensus-based enclosing control scheme is designed. Furthermore, a complete Laplacian matrix (CLM)-based Lyapunov analysis method is introduced to prove the control stability, which provides simpler theoretical validation for the stability analysis of average consensus than the conventional method based on state transition matrix. Finally, some numerical simulations are shown to verify the effectiveness of the proposed control scheme.

Multi-agent quantum-inspired deep reinforcement learning for real-time distributed generation control of 100% renewable energy systems

With promoting peaking carbon emissions and achieving carbon neutrality, the real-time distributed control of the prosumers of 100% renewable energy systems (RESs) is challenging. This paper proposes multi-agent quantum-inspired deep reinforcement learning (QDRL) approaches for real-time distributed generation control of 100% RESs. Quantum-inspired operation is introduced into deep reinforcement learning (DRL) as quantum-inspired Q-learning, quantum-inspired state–action–reward-state–action, quantum-inspired deep Q-network, quantum-inspired policy gradient, quantum-inspired deep deterministic policy gradient, quantum-inspired twin-delayed deep deterministic policy gradient, quantum-inspired actor–critic,​ quantum-inspired proximal policy optimization, and quantum-inspired soft actor–critic.​ These proposed nine QDRL approaches are compared with DRL approaches under two 100% RESs. The numeric results show that the QDRL obtains more minor carbon emissions and frequency deviations under complex 100% RESs. Moreover, the quantum states of QDRL match the uncertain states of the prosumers of 100% RESs. Besides, the exploration and exploitation of the QDRL for the real-time control problems of multi-agent systems are verified and analyzed.

Bipartite time-varying formation group containment control for multi-agent systems based on multi-layer network and semi-signed directed graph

The bipartite time-varying formation group containment tracking control problem of multi-agent systems with unknown input leader on semi-signed digraph is studied. In this paper, the multi-agent system is divided into three layers: the leader layer with unknown input, the formation layer with cooperative-competitive relationship, and the containment layer without competitive relationship. First, the formation members in formation layer track the state of the leader in the leader layer, to achieve bipartite time-varying formation and form two convex hull. Then, by assuming two subgroups of the containment layer exist a well-informed individual (which can receive corresponding convex hull of all the formation members of communication), respectively, the followers of the two subgroups can not only converge to respectively two convex hulls formed by formation layer, also can make the followers of the same subgroup converge to a common value, this provides a prerequisite for the formation control of the followers in the containment layer. Next, different control protocols are designed for formation layer and containment layer respectively based on neighbor information, and Lyapunov function is constructed to provide stability proof for the realization of the problem. Finally, several simulation results are given to verify the validity of the theory.

Predefined-Time Consensus of Nonlinear Multi-Agent Input Delay/Dynamic Event-Triggered Under Switching Topology

The consensus control problem of multi-agent systems (MASs) is studied. Firstly, a predefined-time consensus control algorithm is proposed for nonlinear uncertain systems with the input delay and switching topology. Then, the problem of resource consumption is considered, a dynamic event-triggered predefined-time consensus control algorithm is presented by introducing internal dynamic variables, which can make the MASs achieve consensus in the preset time. The correctness of the algorithm is proved by algebraic graph theory and Lyapunov theory, and there is no Zeno behavior. Simulation comparison experiments verify the effectiveness and superiority of the proposed algorithm. Compared with the finite-time control algorithm, the convergence time of this algorithm is independent of the initial state. The upper bound of the system convergence time can be set by selecting a time parameter. Compared with the fixed-time control algorithm, the convergence time of this algorithm is independent of the controller parameters, only related to a single parameter, the setting is simple, and the estimated convergence time is less conservative. Compared with the static triggering mechanism, the dynamic triggering mechanism can avoid a large number of triggering.

Adaptive Bearing-Only Control of Multiple Euler-Lagrange Systems for Static Geometric Formation⋆

This paper studies the formation control problem of multi-agent systems each of which the dynamics are modeled by the Euler-Lagrange equation with unknown parameters. The objective of each agent is to develop a control law using the bearing information with respect to its neighbors, such that the multi-agent system forms a static geometric formation defined by the bearing constraints among agents. An adaptive dynamic control law with a leader-following type is proposed such that the objective can be achieved in the presence of unknown systems’ parameters. Finally, simulation examples are presented to illustrate the effectiveness and feasibility of the main results.

Event-triggered fault-tolerant secure containment control of multi-agent systems through impulsive scheme

This paper studies a containment control problem of multi-agent systems. A two-layer distributed control framework is proposed to handle the external network attack and internal actuator failure. In the network layer, an event-triggered impulsive control protocol is designed to handle the deception attack, which can deal with stochastic attacks injected into the controller under limited energy bound. Compared with the traditional event-triggered-based continuous control method, the impulsive control mechanism can further reduce energy consumption with better robustness. In the physical layer, a fault-tolerant controller, which considers multiplicative failure, is investigated to maintain the system’s stability. Rigorous proofs for controller stability are given through Lyapunov theory, and Zeno behavior is also avoided within the designed control mechanism. Finally, a numerical simulation is demonstrated to test and verify the proposed control scheme.

Prescribed Performance Control for Multiagent Systems via Fuzzy Adaptive Event-Triggered Strategy

This article discusses the prescribed performance control problem for multiagent systems involving the state triggering and the controller output triggering simultaneously. To successfully apply the backstepping technique in the event-triggered control design, the virtual control signal is constructed by the original system state. Compared with the existing results on the prescribed performance, a new barrier Lyapunov function is developed with considering the characteristics of multiagent systems, and a fuzzy adaptive event-triggered control protocol is proposed by the backstepping procedure. It guarantees that the consensus tracking error converges to a predefined region of the origin in a preset finite time. At the same time, all other closed-loop signals remain bounded without the Zeno behavior. Finally, a simulation example confirms the availability of the developed control scheme with a comparison.

Practical Fixed-Time Affine Formation for Multi-Agent Systems With Time-Based Generators

The practical fixed-time affine formation (PFAF) control problem for multi-agent systems is studied in this brief. The tasks of formation assembly, transformation and time-varying maneuver are able to be completed by affine formation control technique based on stress matrix flexibly and conveniently. Based on time-based generators (TBG) techniques, two fixed-time control laws are designed for leaderless and leader-follower affine formation control problems, respectively. Compared with the existing finite-time affine formation control laws, the convergence times of the proposed control laws in this brief are independent of the system’s initial states. Finally, numerical simulation results are presented to demonstrate the effectiveness of the proposed control laws.

Optimal Leader-Following Consensus Control of Multi-Agent Systems: A Neural Network Based Graphical Game Approach

In this article, the optimal leader-following consensus control problem of multi-agent systems is solved using a novel neural network-based (NN-based) integrated heuristic dynamic programming (IHDP) algorithm under a distributed learning framework. The optimal controller is designed to find a Nash equilibrium of the multi-agent differential graphical game. According to the principle of optimality, this problem can be transformed into solving a set of coupled Hamilton-Jacobi-Bellman (HJB) equations, which are difficult to solve directly because of the nonlinearity and strong coupling of the equations. The proposed algorithm combines policy iteration (PI) and value iteration (VI), and the Nash equilibrium of performance index among agents is achieved distributedly, i.e., all agents update their control inputs only use local information. The convergence of the algorithm is rigorously proved. Neural network (NN) approximation implementation is given for the unknown structures of local value functions of agents. The effectiveness of the algorithm is verified by numerical simulation.

Sampled-data-based consensus of multi-agent systems with multiplicative noise and time-delays

The sampled-data-based consensus control problem of multi-agent systems (MASs) with multiplicative noise and time-delays is studied in this paper. The MASs are given in a continuous-time setting while information exchange occurs only at the sampling instants. Therefore, the traditional methods for continuous-time or discrete-time models are not applicable. To resolve the problem, a discretization-reconnection method is developed to investigate the consensus control of first-order and second-order MASs, respectively. Some sufficient conditions, explicitly related to the control gains and the sampling step size, are established for both mean-square (m.s.) and almost sure (a.s.) consensus. Under these conditions, consensus can be achieved by designing appropriate control gains and the sampling step size for MASs with given noise intensity and time-delays. Numerical simulations verify the effectiveness of the theoretical results.

A new perspective on cooperative control of multi-agent systems through different types of graph Laplacians

In the field of systems and control, many cooperative control problems of multi-agent systems have been actively studied in the past two decades. This article aims to organize extensive existing work on different cooperative control problems into three categories, based on three different types of graph Laplacian matrices involved. A Laplacian matrix is an important representation of graph topology, and depending on the field of its entries, there are three types: ordinary Laplacian (non-negative diagonal entries and non-positive off-diagonal entries), signed Laplacian (arbitrary real entries), and complex Laplacian (arbitrary complex entries). Each type of graph Laplacian is useful in modeling and solving a different set of cooperative control problems. In particular, their algebraic properties are fundamental in characterizing stability and performance of the respective solution algorithms. To our best knowledge, organizing the literature on multi-agent cooperative control through the lens of different graph Laplacians is new.