Consensus Control For Multi-agent Systems Research Articles

Data-Based Optimal Consensus Control for Multiagent Systems With Policy Gradient Reinforcement Learning.

This article investigates the optimally distributed consensus control problem for discrete-time multiagent systems with completely unknown dynamics and computational ability differences. The problem can be viewed as solving nonzero-sum games with distributed reinforcement learning (RL), and each agent is a player in these games. First, to guarantee the real-time performance of learning algorithms, a data-based distributed control algorithm is proposed for multiagent systems using offline system interaction data sets. By utilizing the interactive data produced during the run of a real-time system, the proposed algorithm improves system performance based on distributed policy gradient RL. The convergence and stability are guaranteed based on functional analysis and the Lyapunov method. Second, to address asynchronous learning caused by computational ability differences in multiagent systems, the proposed algorithm is extended to an asynchronous version in which executing policy improvement or not of each agent is independent of its neighbors. Furthermore, an actor-critic structure, which contains two neural networks, is developed to implement the proposed algorithm in synchronous and asynchronous cases. Based on the method of weighted residuals, the convergence and optimality of the neural networks are guaranteed by proving the approximation errors converge to zero. Finally, simulations are conducted to show the effectiveness of the proposed algorithm.

Event-triggered based security consensus control for multi-agent systems with DoS attacks

This work devotes to the security consensus problems for time-varying multi-agent systems (MASs) based on event-triggered control method with denial-of-service (DoS) attacks and parameter uncertainties. During information transmission, DoS attacks exist in both control and measurement channels, regarded as a dwell-time switching scheme, and hence the augmented system can be transformed into a class of discrete-time switched system subject to dwell-time property which contains an unstable and a stable subsystem. To increase the utilization of communication resources, the control input signal can be updated by adopting a novel event-driven mechanism with state-dependent threshold over a given finite horizon. By utilizing multiple Lyapunov functions analysis strategy, two sufficient conditions are obtained to achieve the desired H∞ consensus performance. The cone complementary linearization algorithm is employed to cope with the nonlinear terms, and the desired controller gain matrices are calculated via resolving the corresponding linear matrix inequalities. Finally, the effectiveness of this proposed method is verified by providing a simulation example and the multi-automobile system.

Consensus control of multi-agent systems with deception attacks using event-triggered adaptive cognitive control

This paper presents a novel event-triggered adaptive cognitive control to address the consensus problem of multi-agent systems (MASs) with a Leader under deception attacks. By using the reinforcement learning, adaptive radial basis function (RBF) neural networks and sliding mode control, an adaptive cognitive control is developed. This control has two parts: Actor and Critic. The Actor is designed by using adaptive RBF neural networks and sliding mode control, named adaptive sliding mode control. It is used to control the agent. The Critic is constructed utilizing the adaptive RBF neural networks, to evaluate the control performance of the Actor. In addition, to reduce the communication cost, an event triggered mechanism is designed. The Lyapunov stability analysis shows that the proposed event-triggered adaptive cognitive control can ensure the stabilization of the MASs in case of deception attacks. Simulations are performed to validate the feasibility of the proposed event-triggered adaptive cognitive control, indicating that it can decrease the effects of deception attacks and ensure that all Followers can synchronize to the Leader.

Lyapunov Redesign-Based Optimal Consensus Control for Multi-Agent Systems With Uncertain Dynamics

This brief investigates an optimal consensus problem for a set of integrator systems with dynamic uncertainties. A hierarchical distributed control framework based on partial global information is given, which can reach the optimal solution of the team objective function. Each agent has an optimal control law based on the neighbors’ information and the gradients of the local cost functions, and a compensator derived with Lyapunov redesign technique to deal with the dynamic uncertainties. Then, to remove the constraint on initial conditions and the need for global knowledge of the state, the state differences between neighbors are involved to strengthen the optimal controller while local information feedback is included in the compensator. The resulted fully distributed algorithms can guarantee asymptotic convergence of the systems in the sense of input-to-state stability (ISS). The effectiveness of the method is illustrated by numerical examples.

Optimal consensus control of multi-agent systems based on the state-dependent riccati equation

This paper is concerned with optimal consensus control of a multi-agent system, leaderless or leader-follower control. The hierarchical state-dependent Riccati equation (SDRE) controller was the main framework. The Lyapunov-based method is used for solving time-varying systems. Not only positive definite solution of the related algebraic Riccati equation is realized by the SDRE method based on Lyapunov, but also the negative definite solution is realized. Furthermore, the leader-follower tracking structure is developed based on two nonlinear differential equations; the feedforward differential equation and the state-dependent Riccati differential equation, which consider the error in the cost function through the co-sate equation. The application of the proposed controller structure in controlling the lifting system of multiple 3D cranes is studied. The theoretical results have been verified by simulating the leaderless and leader-follower consensus control problem.

Adaptive fuzzy sliding-mode consensus control of nonlinear under-actuated agents in a near-optimal reinforcement learning framework

This study presents a new framework for merging the Adaptive Fuzzy Sliding-Mode Control (AFSMC) with an off-policy Reinforcement Learning (RL) algorithm to control nonlinear under-actuated agents. In particular, a near-optimal leader-follower consensus is considered, and a new method is proposed using the framework of graphical games. In the proposed technique, the sliding variables’ coefficients are considered adaptively tuned policies to achieve an optimal compromise between the satisfactory tracking performance and the allowable control efforts. Contrary to the conventional off-policy RL algorithms for consensus control of multi-agent systems, the proposed method does not require partial knowledge of the system dynamics to initialize the RL process. Furthermore, an actor-critic fuzzy methodology is employed to approximate optimal policies using the measured input/output data. Therefore, using the tuned sliding vector, the control input for each agent is generated which includes a fuzzy term, a robust term, and a saturation compensating term. In particular, the fuzzy system approximates a nonlinear function, and the robust part of the input compensates for any possible mismatches. Furthermore, the saturation compensating gain prevents instability due to any possible actuator saturation. Based on the local sliding variables, the fuzzy singletons, the bounds of the approximation errors, and the compensating gains are adaptively tuned. Closed-loop asymptotic stability is proved using the second Lyapunov theorem and Barbalat's lemma. The method's efficacy is verified by consensus control of multiple REMUS AUVs in the vertical plane.

Resilient Memory Event-triggered Consensus Control for Multi-agent Systems with Aperiodic DoS Attacks

Resilient Memory Event-triggered Consensus Control for Multi-agent Systems with Aperiodic DoS Attacks

A survey on fault-tolerant consensus control of multi-agent systems: trends, methodologies and prospects

The consensus control problem of multi-agent systems (MASs) has received an increasing research interest with ever-developing consensus theory. Before it can be widely applied to actual engineering, the consensus theory needs to be improved by addressing some practical problems. One of the key problems is how to ensure the desirable performance of each agent as well as the entire MAS when confronted with faults, which is referred to as the fault-tolerant consensus control (FTCC) issue. In this survey, we review the classic and recent results in the context of FTCC from two aspects, namely, physical security and logical security. In addition, we provide the latest results on FTCC where the physical and logical security are considered simultaneously in a framework. Finally, we outline several potential future research topics on the FTCC issue.

Median-Based Resilient Consensus Over Time-Varying Random Networks

This brief investigates the resilient consensus control for multiagent systems over a time-varying directed random network. We propose a median-based consensus strategy, which is purely distributed and, as opposed to the Weighted-Mean-Subsequence-Reduced approaches in the existing literature, shared estimate regarding the number of malicious agents in the neighborhood of each cooperative agent is not required. This offers more applicability and flexibility as seeking a shared estimate of surrounding threats is often difficult in practice. In addition to malicious agents, random availability of communication edges is accommodated in the random network framework. Sufficient conditions are derived for reaching almost sure consensus by using a martingale convergence theorem. Finally, the theoretical findings are illustrated by numerical simulations.

Event-based consensus control for multi-agent systems against joint sensor and actuator attacks

The paper focuses on the observer-based consensus control issue for a class of discrete-time multi-agent systems suffering from the joint actuator and sensor attacks. An event-triggered communication rule with the adaptive threshold update is introduced to reduce the communication burden. A PI-type controller with a finite-time window integral loop is constructed to achieve bounded consensus in the mean-square sense (BCMS). With the help of common properties of Laplace matrices, the closed-loop multi-agent system is converted into an easy-to-analyze pattern. In light of such a pattern, a sufficient condition is derived to realize the desired consensus by the stochastic analysis. Furthermore, the expected gains of the controller and the observer are determined by resorting to matrix inequalities in combination with the cone complementarity linearization algorithm. Finally, a numerical example and a simulation of the platooning vehicle are proposed to illustrate the effectiveness of the proposed control scheme.

Fault tolerant consensus control of multi-agent systems under dynamic event-triggered mechanisms

In this paper, the fault-tolerant consensus control (FTCC) problem is studied for multi-agent systems (MASs) with the dynamic event-triggered mechanism (DETM). To ease the communication burden, DETM governed by an additional internal dynamical variable is introduced. A novel fault-tolerant controller is presented to mitigate system performance degradation caused by failures, where a simple fault compensator is constructed through a protocol-based observer. Then, some sufficient conditions are established to examine the bounded consensus while optimizing the predetermined quadratic cost criterion. In addition, the explicit expression of the desired controller is also parameterized through orthogonal decomposition. Finally, two simulation results are made for the sake of verifying the effectiveness of the developed FTCC scheme, the fault compensation test as well as the quadratic cost one.

Finite-Time Average Consensus Control of Multi-Agent Systems Based on the Aperiodically Intermittent Control

This study investigates the finite-time consensus problem of multi-agent systems under an aperiodically intermittent control. By designing an appropriate control protocol, the whole control process can be divided into two states, namely, the continuous communication time period and the interrupted communication time period. These two control states are aperiodic. Then, according to the designed control protocol, the appropriate Lyapunov function is constructed using the segmented state information. Subsequently, the corresponding state model of the multi-agent system is introduced, and the relevant conditions for the consensus of the multi-agent system under an aperiodically intermittent control are obtained using norm inequality and the corresponding properties of the Kronecker product. After determining the consensus of the general model, the nonlinear term and uncertainty term are added, and the consensus is realized using the control protocol. The control method presented in this research can avoid continuous communication, and the aperiodic control process is more in line with the relevant needs of the actual project than the periodic control process. As a result, the proposed method can effectively reduce energy consumption and fit actual projects. Finally, the effectiveness of the theory is proven via numerical simulation.

Dynamic Event-Triggered Consensus Control for Multi-Agent Systems Using Adaptive Dynamic Programming

The consensus optimal control problem for a class of linear multi-agent systems with directed communication networks is studied in this paper using adaptive dynamic programming. To overcome the restrictions of the agent’s low processing capability and to extend the actuator’s lifetime, consider the event-triggered. In the beginning, a dynamic event-triggered is provided, with several existing static event-triggered serving as special examples. When using the dynamic event-triggered, a longer interval can be shown between any two consecutive event-triggered. Designing a dynamic event-triggered control law becomes more challenging when implemented in directed networks. In addition, on the basis of dynamic event-triggered, a novel adaptive dynamic programming is used to construct a suitable dynamic event-triggered control law, which employs just the interaction information between agents and does not need model dynamics, overcoming the difficulty of solving the algebraic Riccati equation. Finally, the effectiveness of the proposed method is verified by simulation results, and no agent exhibits Zeno behavior.

Bipartite Consensus Control of Multi-agent Systems Under Multiple Denial-of-Service Cyber Attacks

Bipartite Consensus Control of Multi-agent Systems Under Multiple Denial-of-Service Cyber Attacks

Event-triggered sliding mode scaled consensus control for multi-agent systems

In this paper, the scaled consensus problem for discrete-time multi-agent systems is investigated via event-triggered sliding mode control approach subject to unknown external bounded disturbance and time-varying state delay. A new event-triggered integral sliding surface function is introduced to handle the external disturbances. Then, a distributed event-triggered control method is integrated with sliding mode control to address the energy-constrained issue in the scaled consensus problem. Subsequently, a sufficient condition is established to ensure a predefined H∞ performance can be achieved for the underlying system. Furthermore, an event-triggered sliding mode control protocol is developed to guarantee the state trajectories of all agents approach the dictated scales in the discrete-time multi-agent systems. A numerical example is given to demonstrate the effectiveness of the proposed design techniques.

Memory-based event-triggered leader-following consensus for T-S fuzzy multi-agent systems subject to deception attacks

In this paper, the problem of fuzzy model-based leader-following consensus control for multi-agent systems (MASs) under deception attacks is investigated. For the sake of alleviating the communication burden, a novel memory-based event-triggered scheme (METS) is first proposed for the considered MASs to reduce redundant data transmission, and the leader-following consensus can be achieved faster with a smaller adjustment error by applying the historical released packets. Considering the designed METS and upper-bounded attacks synthetically, the closed-loop fuzzy system model is well established. Furthermore, with the help of Lyapunov-Krasovskii technique, some sufficient conditions are derived to ensure the consensus of MASs subject to deception attacks. Finally, a simulation example is introduced to manifest the effectiveness of the proposed method.

Global Adaptive Consensus Control for Multiagent Systems with Predefined Accuracy

This paper addresses a consensus problem for uncertain nonlinear multiagent systems with predefined precision under disturbance. By employing the neural networks method and backstepping technique, adaptive controllers for each agent are created. In contrast to the exiting global control methods for multiagent systems, global precision consensus control scheme is first put forward. Moreover, by using three n th-order continuous differentiable functions, adaptive tuning laws and virtual controllers and the real controller are designed. It is proved that the presented method can ensure that all signals are globally bounded and systems can be consistent with a given accuracy under disturbance. Finally, a practical simulation verifies the correctness for the devised control protocol.

Encryption–decryption-based consensus control for multi-agent systems: Handling actuator faults

This paper deals with the encryption–decryption-based passive fault-tolerant consensus control problem for a class of linear multi-agent systems subject to loss-of-effectiveness and additive actuator faults. Both the secure communication and fault tolerance issues are investigated, and their impacts on the consensus are examined. To ensure the communication security, two encryption–decryption-based fault-tolerant consensus control schemes are proposed, with which the original state of the local agent is first encrypted into a series of codewords and then decrypted by other agents to realize data privacy preserving. The necessary and sufficient condition is established for the addressed consensus problem under the adopted controller structure. A series of numerical examples are given to verify the effectiveness of the developed consensus control strategy.

Sampled-data-based fault-tolerant consensus control for multi-agent systems: A data privacy preserving scheme

This paper is concerned with the fault-tolerant consensus control problem for a general class of linear continuous-time multi-agent systems (MASs) with the data privacy preserving (DPP) constraints. Rather than employing the traditional cryptographic mechanism, we develop a novel dynamic-quantization-based DPP scheme from a control-theoretic perspective. Under our developed DPP scheme, the sampled, quantized, released, received and recovered data is used to design a fault-tolerant controller. With the adopted controller structure, the consensus of the MASs is guaranteed where the size of the data transmitted among agents can be made finite. A series of numerical examples are given and the comparative analysis is carried out to illustrate the effectiveness of the developed consensus control scheme.

Consensus control of multi-agent systems with P-one-sided Lipschitz

This paper investigates the consensus problem for a class of multi-agent systems with P-one-sided Lipschitz nonlinearity. This nonlinearity offers a significant extension to application range of existing consensus control protocol design methods, which removes the assumption of the quadratic inner-boundedness. To solve this problem, a strongly connected graph topology is applied within the consensus protocol design framework. Moreover, the leader-following scenario is addressed under the graph topology. Under the above objectives, sufficient conditions are employed to yield consensus. Two examples are performed to illustrate the properties.