Scaled consensus for asynchronous high‐order discrete‐time multiagent systems

This paper investigates the distributed scaled consensus problem of multiple agents with high‐order dynamics under the asynchronous setting, where each agent measures the neighbors' information at certain discrete time instants according to its own clock rather than the whole discrete process and all agents' clocks are independent of each other. Assume that the communication topology can be arbitrarily switched and the information transfer between agents has a time‐varying delay. Under the designed asynchronous distributed control protocol, it is shown that the agents with the same scale will reach a common final state, while the agents with different scales will reach different final states. Moreover, an effective parameters selection strategy is presented for a large number of gain parameters in high‐order multiagent systems based on novel model transformation techniques. Simulation examples are provided to demonstrate the high‐order scaled consensus performances for the agents in the presence of asynchronous setting.     

Decentralized consensus protocol for a class of high‐order multiagent systems

This paper proposes a consensus protocol for a class of high‐order multiagent systems under directed networks. It is supposed that each agent is exposed to an external disturbance additive to its control input. Based on the optimization theory, the consensus protocol gains are designed in order to attenuate the effects of the external disturbances on the performance of the multiagent system. The main problem of existing high‐order consensus protocols in the literature is the dependency of the design on the information of coupling matrices associated with networks topologies. Despite existing high‐order consensus protocols in the literature, the proposed consensus protocol can be designed in a fully decentralized manner based on no global information. The main idea of the design is to propose an control formulation in which the coupling information of the agents is considered as exogenous signals, while the coupling effects of these signals lead to achieving consensus in the multiagent system. Numerical examples verify the effectiveness of the proposed consensus protocol. Copyright © 2016 John Wiley & Sons, Ltd.     

Fully distributed bipartite output consensus of heterogeneous linear multiagent systems based on event‐triggered transmission mechanism

This study investigates the fully distributed bipartite output consensus issue of heterogeneous linear multiagent systems (HLMASs) based on event‐triggered transmission mechanism. Both the cooperative interaction and the antagonistic interaction between neighbor agents are considered. A fully distributed bipartite compensator consisting of time‐varying coupling gain and dynamic event‐triggered mechanism is first proposed to estimate the leader's states. Different from the existing schemes, the proposed compensator is independent of any global information of the network topology, is capable of achieving intermittent communication between neighbors, and is applicable for the signed communication topology. Then the distributed output feedback control protocol is developed such that the fully distributed bipartite event‐triggered output consensus problem can be achieved. Moreover, we extend the results in HLMASs without external disturbances to HLMASs with disturbances, which is more challenging in three cases (a) the disturbances are not available for measurement, (b) the disturbances suffered by each agent are heterogeneous, and (c) the disturbances are not required to be stable or bounded. It is proven that the proposed controllers fulfill the exclusion of Zeno behavior in two consensus problems. Finally, two examples are provided to illustrate the feasibility of the theoretical results.     

Fixed‐time output‐feedback consensus tracking control for second‐order multiagent systems

This paper is concerned with the problem of fixed‐time consensus tracking control for a class of second‐order multiagent systems under an undirected communication graph. A distributed output‐feedback fixed‐time consensus tracking control scheme is proposed to make the states of all individual agents simultaneously track a time‐varying reference state even when the reference state is available only to a subset of the group members and only output measurements are available for feedback. Homogeneous Lyapunov function and homogeneity property are employed to show that the control scheme can guarantee the consensus tracking errors converging the origin in finite time which is bounded by a fixed constant independent of initial conditions. Numerical simulations are carried out to demonstrate the effectiveness of the proposed control law.     

Distributed event-triggered optimal bipartite consensus control for multiagent systems with input delay via reinforcement learning method

In this article, an optimal bipartite consensus control (OBCC) scheme is proposed for heterogeneous multiagent systems (MASs) with input delay by reinforcement learning (RL) algorithm. A directed signed graph is established to construct MASs with competitive and cooperative relationships, and model reduction method is developed to tackle input delay problem. Then, based on the Hamilton–Jacobi–Bellman (HJB) equation, policy iteration method is utilized to design the bipartite consensus controller, which consists of value function and optimal controller. Further, a distributed event-triggered function is proposed to increase control efficiency, which only requires information from its own agent and neighboring agents. Based on the input-to-state stability (ISS) function and Lyapunov function, sufficient conditions for the stability of MASs can be derived. Apart from that, RL algorithm is employed to solve the event-triggered OBCC problem in MASs, where critic neural networks (NNs) and actor NNs estimate value function and control policy, respectively. Finally, simulation results are given to validate the feasibility and efficiency of the proposed algorithm.     

Directed spanning tree–based adaptive protocols for second‐order consensus of multiagent systems

This paper discusses the consensus problem of second‐order multiagent systems with nonlinear dynamics. A directed spanning tree–based adaptive control protocol is developed, which overcomes the drawback that the spectrum of the Laplacian matrix must be known a priori. A scheme for reordering the nodes is proposed. Applying the developed method and the Lyapunov stability theory, some distributed adaptive laws are designed in the directed network. It is found that the consensus can be achieved by randomly choosing a directed spanning tree and using the developed distributed adaptive law. Finally, an example is presented to illustrate the theoretical analysis.     

Consensus control of output‐constrained multiagent systems with unknown control directions under a directed graph

This paper presents a novel distributed adaptive control algorithm for uncertain higher‐order nonlinear multiagent systems subject to output constraints and unknown control directions. Regarding the latter, a generic class of cases is considered, allowing completely unknown and even nonidentical control directions. Furthermore, the communication topology is only required to contain a fixed directed spanning tree. To guarantee the output constraints and address the asymmetric directed communication topology, a new reference output using the transformation strategy is introduced for each agent, benefiting from which the consensus problem of the multiagent system is recast as local tracking control problems of single agents. Then, the distributed control algorithm is recursively established based on the backstepping technique and the Nussbaum‐type function. By leveraging the unique properties of the Laplacian matrix on directed graphs and matrix theory, it is shown that, under the proposed distributed algorithm, uniform boundedness of all closed‐loop signals can be ensured, and asymptotic consensus is achieved without violation of the output constraints. Finally, simulation studies on the angle control of single‐link robots are given to verify the effectiveness of the proposed algorithm.     

Distributed H∞ consensus of second‐order multiagent systems with nonconvex constraints

In this paper, the distributed H_∞ consensus problem for second‐order multiagent systems is studied. We first propose a distributed control algorithm based on local information. In the presence of external disturbances, some sufficient conditions are then derived to guarantee that all agents reach the distributed H_∞ consensus, and meanwhile, the inputs of agents always stay in the nonconvex sets, making the results in this paper more practical. Finally, a numerical simulation is provided to show the effectiveness of the results.     

Distributed fixed‐time consensus for multiagent systems with unknown control directions

This paper investigates the consensus problem for high‐order multiagent systems with unknown control directions and directed communication constraints. To handle the problem of unknown control directions, a logic switching rule is established in the framework of fixed‐time stability. Then, the consensus is achieved in two steps. A group of distributed fixed‐time observers is designed to estimate the reference signals first. Based on these estimates and the designed logic switching rule, a novel control protocol is proposed for each follower system. Different from the existing results, the consensus is achieved with a fixed‐time convergence rate, and the unknown control directions are allowed to be nonidentical for each agent. Finally, simulation results are given to exhibit the validity of the proposed method.     

Active fault‐tolerant consensus control of Lipschitz nonlinear multiagent systems

This article investigates the active fault‐tolerant consensus problem for Lipschitz nonlinear multiagent systems under detailed balanced directed graph and actuator faults. First, a fault detection filter for each agent is designed, and all agents can be divided into two categories: healthy agents and possibly faulty agents. Second, fully distributed adaptive fault‐tolerant consensus protocols for healthy and possibly faulty agents are proposed to achieve state consensus. Third, based on the fault detection method and fault‐tolerant consensus protocols, active fault‐tolerant consensus algorithms are given. Simulation examples are presented to verify the effectiveness of the proposed active fault‐tolerant algorithms.     

Leaderless and leader‐following fixed‐time consensus for multiagent systems via impulsive control

This article addresses the leaderless fixed‐time consensus (LLFTC) and leader‐following fixed‐time consensus (LFFTC) problems for multiagent systems (MASs) via impulsive control. First, a novel fixed‐time stability for impulsive dynamical system is developed. Then the novel fixed‐time impulsive control protocols are designed to achieve leaderless and leader‐following consensus for MASs. Based on the impulsive control theory, fixed‐time stability theory and algebraic graph theory, some sufficient conditions are derived for each agent to achieve LLFTC and LFFTC under the proposed control protocols. Finally, numerical simulations are put forward to validate the theoretical results.     

Distributed optimal resource allocation of second‐order multiagent systems

In this paper, the resource allocation problems of multiagent systems are investigated. Different from the well‐studied resource allocation problems, the dynamics of agents are taken into account in our problem, which results that the problem could not be solved by most of existing resource allocation algorithms. Here, the agents are in the form of second‐order dynamics, which causes the difficulties in designing and analyzing distributed resource allocation algorithms. Based on gradient descent and state feedback, two distributed resource allocation algorithms are proposed to achieve the optimal allocation, and their convergence are analyzed by constructing suitable Lyapunov functions. One of the two algorithms can ensure that the decisions of all agents asymptotically converge to the exact optimal solution, and the other algorithm achieves the exponential convergence. Finally, numerical examples about the economic dispatch problems of power grids are given to verify the effectiveness of the obtained results.     

Distributed learning consensus control based on neural networks for heterogeneous nonlinear multiagent systems

This paper considers a novel distributed iterative learning consensus control algorithm based on neural networks for the control of heterogeneous nonlinear multiagent systems. The system's unknown nonlinear function is approximated by suitable neural networks; the approximation error is countered by a robust term in the control. Two types of control algorithms, both of which utilize distributed learning laws, are provided to achieve consensus. In the provided control algorithms, the desired reference is considered to be an unknown factor and then estimated using the associated learning laws. The consensus convergence is proven by the composite energy function method. A numerical simulation is ultimately presented to demonstrate the efficacy of the proposed control schemes.     

Distributed control of uncertain multiagent systems for tracking a leader with unknown fractional‐order dynamics

This paper proposes distributed adaptive cooperative control algorithms for second‐order agents to track a leader with unknown dynamics. The models of the followers and the leader are composed of uncertain nonlinear components. The order of the leader's dynamics is unknown and can be fractional. Only the single output information is shared among neighbored agents. To simplify the control design, linearly parameterized neural networks are used to approximate the unknown functions. We first present an adaptive control for leaderless consensus and then extend the method to the tracking problem. Thorough theoretical proofs as well as numerical simulation are included to verify the results. Compared with relevant literature, the new approach applies to a larger variety of systems because (i) knowledge about the structure of leader's model is unnecessary; (ii) the unknown functions in different agents' dynamics can be diverse and arbitrary, in other words, the algorithms apply to heterogeneous agents; (iii) the results can be simply used without parameter calculations.     

Leader‐following consensus of multiple uncertain Euler‐Lagrange systems

A new adaptive distributed controller is developed for the leader‐following consensus problem of multiple uncertain Euler‐Lagrange systems. A distinct feature of our proposed approach as opposed to the existing ones is that it does not need the exchange of controller's state among the communication network. As a consequence, it not only makes the implementation of the controller much easier but also reduces the communication cost. The effectiveness of the main result is demonstrated by some exemplary applications to cooperative control of multiple two‐link robot arms.     

Distributed consensus of second‐order multiagent systems with nonconvex input constraints

This paper addresses the input constrained consensus of second‐order multiagent systems with nonconvex constraints. A new update law is proposed to make the position states of all agents converge to a common point and the velocities converge to zero, while the input of each agent stays in a certain constraint set. The closed‐loop system is first converted to an equivalent system by taking a novel coordinate transformation. Then, it is proved that the input constrained consensus can be achieved if the graphs jointly have directed spanning trees by using the Metzler matrix theory. Finally, simulations are provided to demonstrate the effectiveness of the proposed algorithm.     

Adaptive‐impulsive consensus of multiagent systems

This paper studies the leader‐following consensus problem of multiagent nonlinear systems with uncertain parameters and control gain error. On the basis of the theory of impulsive differential equations, the adaptive control technique, and the Lyapunov stability theory, some novel adaptive‐impulsive consensus conditions are given to realize the consensus of a class of multiagent nonlinear systems. Compared with the existing investigations on the impulsive consensus of multiagent systems, the proposed impulsive control protocol with uncertain parameters and control gain error is more rigorous and effective in practical systems. Four numerical simulations are verified to confirm the effectiveness of the proposed methods.     

Adaptive leaderless consensus control of uncertain multiagent systems with unknown control directions

This article solves the leaderless consensus problem of a class of uncertain nonlinear multiagent systems with unknown control directions and unknown system parameters. Without using the Nussbaum function approach, a novel control scheme is proposed by means of the switching mechanism. The control algorithm guarantees that consensus errors converge to the origin asymptotically, and the amplitude of the control signals is much smaller compared with those using Nussbaum functions. The simulation results illustrate the effectiveness of the proposed algorithm.     

Adaptive mean-square consensus of heterogeneous multiagent systems with stochastic switching topologies

The leader–following consensus of linear heterogeneous multiagent systems is investigated in this paper. To comply with the most practical scenario, the communicating topologies among agents are assumed to switch stochastically and driven by a continuous-time discrete-state Markov process, whose state space corresponds to all the possible topologies. A novel distributed adaptive compensator is designed for the followers to reconstruct the exogenous signals without knowing the Laplacian matrix who is regarded as a global information, and sufficient conditions are given to ensure the compensator converges to the dynamic of the leader asymptotically in the mean square sense. Then, based on the compensator, we solved the consensus problem both by distributed state and measurement output feedback control schemes under output regulation framework, which allow followers to have nonidentical state dimensions. Finally, the theoretical results are demonstrated by a numerical example.     

考虑量化的多智能体系统数据驱动双向一致性控制

针对未知动力学模型非线性离散时间多智能体系统,在信息传递过程中的数据量化问题,以及智能体之间的合作与竞争关系,提出了一种数据驱动控制算法,实现了多智能体系统的双向一致性跟踪控制.首先,利用紧凑形动态线性化(CFDL)方法,将未知动力学模型的非线性智能体转化为含有时变参数的数据模型,并通过设计性能指标函数获得时变参数的估...