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The fully distributed output leader‐follower event‐triggered resilient control algorithm is proposed for heterogeneous multi‐agent systems (MASs) under communication link faults and constraints. An estimation of the information of the leader is achieved by designing a dynamic event‐triggered observer with resilience to link faults without using the eigenvalues of the global matrix. Zeno behavior is also excluded under the dynamic triggering mechanism. Considering that the state information of the follower is not available, a distributed output leader‐follower control law is further designed to achieve asymptotically convergence of the leader‐follower consensus error, and only the output information of followers is required. Finally, the availability of the theoretical results is shown in two numerical examples. [ABSTRACT FROM AUTHOR]

This article studies the model‐free learning‐based distributed cooperative tracking control of human‐in‐the‐loop multi‐agent systems in the presence of an active leader. The core role of human‐in‐the‐loop is to use the ground station to send control commands to the non‐zero control input of the leader, and then directly or indirectly control a group of agents to complete complex tasks. Meanwhile, three essential demands including the completely unknown system model, the control objective obtained optimally, as well as no initial admissible control strategy requirement, are satisfied simultaneously. It is worth emphasizing that the relevant results only satisfy one or two demands at most, which are essentially not applicable to this problem. In this article, a model‐based human‐in‐the‐loop learning algorithm is first presented to achieve the optimal tracking control, as well as the convergence of the proposed learning algorithm is proved. Then, a bias‐based data‐driven learning algorithm is proposed, which provides the potential opportunities to overcome the difficulties caused by the above‐mentioned three demands. Finally, the validity of theoretical results is testified by a numerical example. [ABSTRACT FROM AUTHOR]

By applying iterative learning control approach, the consensus is studied for multi‐agent systems (MASs) with one‐sided Lipschitz (OSL) nonlinearity. Firstly, the P‐type and D‐type learning schemes with initial state learning are introduced for such MASs. Then, utilizing the OSL and the quadratically inner‐bounded constraints, the convergence conditions of the consensus algorithms are presented and analyzed under a directed communication graph. We show that both algorithms, on a fixed finite‐time interval, can achieve perfect consensus tracking. Finally, the correctness of the obtained results is illustrated with simulation examples. [ABSTRACT FROM AUTHOR]

A distributed control fashion of cooperative formation tracking may be vulnerable to malicious attacks on sensors and communications that deteriorate the formation tracking performance or even destabilize the entire multiagent system. This article addresses a resilient time‐varying output formation tracking problem of linear heterogeneous multiagent systems, where an attacker can adversely inject false data injection (FDI) attacks on sensors and Denial‐of‐Service (DoS) attacks on communications. Both characteristics will not only enhance the practical relevance of the studied issue, but also bring nontrivial design challenges of distributed control algorithms and convergence analysis. A novel resilient distributed control architecture is developed to guarantee a global exponential time‐varying output formation tracking. In particular, the design includes features: (1) a resilient distributed leader estimator is developed to estimate the leader's state for each follower under DoS attacks over digraphs; (2) a distributed output feedback controller integrated with an observer‐based resilient mechanism is developed to achieve the time‐varying output formation tracking under attacks; (3) unlike existing related works to handle attacks as bounded disturbances or faults, the proposed design is resilient to enable exponential convergence, whereas many related works obtain uniformly ultimately boundedness (UUB) convergence. Simulation results are presented to validate the design's effectiveness. [ABSTRACT FROM AUTHOR]

In the research on consensus control of multi‐agent systems (MASs), it is the key to ensure that the state quantities (speed, displacement, etc.) of all agents tend to be consensus. Under this premise, how to accelerate the convergence rate of the consensus error is also an important issue. Aiming at the accelerated consensus problem of MASs composed of partial difference equations, based on the network topology and the output form of each follower, a distributed closed‐loop accelerated iterative learning control (ILC) protocol with variable gain was proposed, which was designed to improve the speed of consensus error. With the help of basic mathematical tools such as discrete Gronwall inequality and contraction mapping method, the sufficient conditions for the convergence of the consensus error are derived and analyzed. Finally, numerical simulations show that the proposed acceleration control protocol is more effective than the traditional open‐loop and closed‐loop ILC protocols. [ABSTRACT FROM AUTHOR]

This article focuses on global fuzzy consensus control of unknown second‐order nonlinear multi‐agent systems based on adaptive iterative learning scheme. In order to achieve global consensus, a replacement idea is introduced, where fuzzy systems are used as feedforward compensators to model unknown nonlinear dynamics relying on tracking signals. Considering that the network communication is distributed, a kind of hybrid control protocol is designed to avoid the complete dependence on the tracking signals. In addition, considering the complexity of the external environment, this article extends the above distributed protocol to the case of unknown control directions to study global consensus. Finally, the feasibility of the proposed protocols is verified by Matlab numerical simulations. [ABSTRACT FROM AUTHOR]

In this article, an iterative learning approach is proposed for the formation control of discrete‐time multi‐agent systems, where the trial length of each learning iteration is randomly varying. In particular, a modified state error related to the prescribed formation is defined by taking into account the nonuniform actual trial length that could be different from the desired one. Then, a P‐type iterative learning protocol is established for switching networks of agents subject to nonuniform trial lengths, and the convergence analyses are given for the fixed and the iteration‐varying initial conditions respectively. It shows that through iterative learning, the given formation will be maintained among multiple agents in the entire time interval of one trial. In the end, simulations are done to demonstrate the correctness of the obtained theoretical results. [ABSTRACT FROM AUTHOR]

Summary: In recent years, multi‐agent system (MAS) have attracted extensive attention, but the study for MAS via zonotope theory is very few. This article considers the distributed cooperative fault detection (FD) and fault isolation (FI) problems for a MAS, in which every agent is assumed to be with disturbances in both the sate equation and output equation, by combining Luenberger‐like observers and zonotope theory. To begin with, a robust H∞ observer is constructed for each agent, and zonotope theory is applied on the error dynamic system of the H∞ observer such that the boundary estimation of the system state can be computed iteratively when the MAS has no fault. After this, for distributed cooperative FD purpose, a residual is also constructed based on the H∞ observer. Because the constructed residual contains disturbance, it cannot be used for FD directly. To overcome this drawback, the interval estimation of the residual is calculated by using zonotope method, and based on the residual interval estimate, a residual‐based distributed cooperative FD strategy is presented. Moreover, the distributed cooperative FI scheme is discussed and an FI strategy is also developed by modifying the distributed cooperative FD scheme. Finally, a simulation example is presented to show the effectiveness and advantages of the proposed method. [ABSTRACT FROM AUTHOR]

This paper focuses on the consensus issue of nonlinear multiagent systems (MASs) with intrinsic time‐varying delays via the nonfragile and aperiodic memory sampled‐data control scheme. An improved continuous Lyapunov–Krasovskii functional (LKF) with some two‐sided looped‐functional terms is introduced, in which several free‐weighting‐matrices are not necessarily positive definite. Furthermore, based on the property of Laplacian matrix, the consensus issue of original MASs can be investigated directly without transforming into error systems. Then, several sufficient criteria for the consensus of MASs are presented by means of the recently developed generalized integral inequality and the constructed LKF. Next, the required nonfragile controller gain is obtained by resorting to linear matrix inequalities and the maximal length of sampling period is derived as well. Finally, two illustrative examples are employed to exhibit the effectiveness and feasibility of the proposed consensus protocol. [ABSTRACT FROM AUTHOR]

This paper considers the consensus control problem of multi‐agent systems (MAS) with distributed parameter models. Based on the framework of network topologies, a second‐order PI‐type iterative learning control (ILC) protocol with initial state learning is proposed by using the nearest neighbor knowledge. A discrete system for proposed ILC is established, and the consensus control problem is then converted to a stability problem for such a discrete system. Furthermore, by using generalized Gronwall inequality, a sufficient condition for the convergence of the consensus errors between any two agents is obtained. Finally, the validity of the proposed method is verified by two numerical examples. [ABSTRACT FROM AUTHOR]

In this paper, we consider the consistent tracking problem of noninstantaneous impulsive multi‐agent systems. Take advantage of the repeatability of tracking tasks and the learning ability of each agent, we show that all agents of linear systems are driven to achieve a given asymptotical consensus as the number of iteration increases by using the standard D‐type learning law with the initial state learning rule. In addition, for nonlinear systems, we use PD‐type learning law with the initial state learning rule to prove that all agents are driven to achieve a given asymptotical consensus as the number of iteration increases. Finally, two numerical examples are given to verify the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

In this paper, the finite‐time asymmetric bipartite consensus problem of multi‐agent systems with signed digraph is considered. Firstly, an asymmetric index is introduced to describe a desired output relationship of the agents in terms of quantity. Based on the information of the agent's communication and the index, a novel iterative learning control protocol is proposed. By establishing the input and output errors relationship of the agents along the iteration domain, a sufficient condition is derived and a defined leaderless tracking error is proved to be asymptotically convergence as iteration increases. The result shows that the proposed design can ensure the agents achieve the asymmetric bipartite consensus goal in the finite‐time. Moreover, the proposed design is also extended to deal with the problem of the multi‐agent systems with heterogeneous dynamics. Finally, numerical simulation examples verify the effectiveness of the proposed protocol. [ABSTRACT FROM AUTHOR]

This paper considers the consensus tacking problem for nonlinear fractional‐order multiagent systems by presenting a PDα‐type iterative learning control update law with initial learning mechanisms. The asymptotical convergence of the proposed distributed learning algorithm is strictly proved by using the properties of fractional calculus. A sufficient condition is derived to guarantee the whole multiagent system achieving an asymptotic output consensus. An illustrative example is given to verify the theoretical results. [ABSTRACT FROM AUTHOR]

In this study, the fully distributed adaptive iterative learning coordination control of the uncertain non-linear leader-follower multi-agent systems with input saturation is studied. Under the alignment initial condition and Lyapunov theory, a novel adaptive distributed control protocol with a fully saturated parameter learning law is designed. Despite the existence of input saturation, the global perfect consensus tracking can be realised over a finite time interval. Besides, the consensus tracking problem is extended to the formation control problem as well. Ultimately, the validity of theoretical analysis of this study is shown by two examples. [ABSTRACT FROM AUTHOR]

Summary: 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. [ABSTRACT FROM AUTHOR]

This study considers the consensus tracking problem of singular multi-agent systems by using an iterative learning control approach. Here, the communication among the followers is described by a directed graph, and only a portion of the followers can receive the leader's information. For such singular multi-agent systems, a unified iterative learning algorithm is proposed in both continuous-time domain and discrete-time domain. Furthermore, the convergence condition of the algorithm is presented and analysed. In this study, the main contribution is to extend the iterative learning control theory from multi-agent systems to singular multi-agent systems. It is shown that the algorithm can guarantee the outputs of the followers converge to the leader's trajectory on a finite time interval along the iteration axis. Finally, the provided examples illustrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

Summary: This survey paper studies deterministic control systems that integrate three of the most active research areas during the last years: (1) online learning control systems, (2) distributed control of networked multiagent systems, and (3) hybrid dynamical systems (HDSs). The interest for these types of systems has been motivated mainly by two reasons: First, the development of cheap massive computational power and advanced communication technologies, which allows to carry out large computations in complex networked systems, and second, the recent development of a comprehensive theory for HDSs that allows to integrate continuous‐time dynamical systems and discrete‐time dynamical systems in a unified manner, thus providing a unifying modeling language for complex learning‐based control systems. In this paper, we aim to give a comprehensive survey of the current state of the art in the area of online learning control in multiagent systems, presenting an overview of the different types of problems that can be addressed, as well as the most representative control architectures found in the literature. These control architectures are modeled as HDSs, which include as special subsets continuous‐time dynamical systems and discrete‐time dynamical systems. We highlight the different advantages and limitations of the existing results as well as some interesting potential future directions and open problems. [ABSTRACT FROM AUTHOR]

In this paper, we use 2 iterative learning control schemes (P‐type and PI‐type) with an initial learning rule to achieve the formation control of linear fractional‐order multiagent systems. To realize the finite‐time consensus, we assume repeatable operation environments as well as a fixed but directed communication topology for the fractional‐order multiagent systems. Both P‐type and PI‐type update laws are applied to generate the control commands for each agent. It is strictly proved that all agents are driven to achieve an asymptotical consensus as the iteration number increases. Two examples are simulated to verify the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper addresses the consensus problem of nonlinear multiagent system with state constraints. A novel γ-type barrier Lyapunov function is adopted to handle with the bounded constraints. The iterative learning control strategy is introduced to estimate the unknown parameter and basic control signal. Five control schemes are designed, in turn, to address the consensus problem comprehensively from both theoretical and practical viewpoints. These schemes include the original adaptive scheme, projection-based scheme, smooth function-based scheme and its alternative, and dead-zone-like scheme. The consensus convergence and constraints guarantee are strictly proved for each control scheme by using the barrier composite energy function approach. Illustrative simulations verify the theoretical analysis. [ABSTRACT FROM AUTHOR]

This paper deals with the high-precision consensus seeking problem of multi-agent systems when they are subject to switching topologies and varying communication time-delays. By combining the iterative learning control (ILC) approach, a distributed consensus seeking algorithm is presented based on only the relative information between every agent and its local (or nearest) neighbors. All agents can be enabled to achieve consensus exactly on a common output trajectory over a finite time interval. Furthermore, conditions are proposed to guarantee both exponential convergence and monotonic convergence for the resulting ILC processes of multi-agent consensus systems. In particular, the linear matrix inequality technique is employed to formulate the established convergence conditions, which can directly give formulas for the gain matrix design. An illustrative example is included to validate the effectiveness of the proposed ILC-motivated consensus seeking algorithm. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This study considers an iterative learning control approach to achieve accurate coordination performances of the output data sequences for multiple plants that are involved in a networked environment. To realise such a desirable control objective, an update process of the input data sequence is needed to refine its output performance iteratively for each plant, which uses the local or nearest neighbour knowledge. The nominal multi-agent systems are employed as the plants' description, for which input-output data-driven consensus problems are addressed in a hybrid networked environment given by signed directed graphs with both cooperative and antagonistic interactions. It is proved that the output data can be guaranteed to achieve bipartite consensus or remain stable for the multi-agent networks under structurally balanced or structurally unbalanced signed graphs. Moreover, the convergence conditions are derived, which need less knowledge of the agents' plant, and the proposed consensus results can be developed to take into account the plant uncertainties and noises. Simulation tests are performed to verify the effectiveness of the learning approach in refining high input-output data-driven consensus performances of networked agents. [ABSTRACT FROM AUTHOR]

Arbitrary high precision is considered one of the most desirable control objectives in the relative formation for many networked industrial applications, such as flying spacecrafts and mobile robots. The main purpose of this paper is to present design guidelines of applying the iterative schemes to develop distributed formation algorithms in order to achieve this control objective. If certain conditions are met, then the control input signals can be learned by the developed algorithms to accomplish the desired formations with arbitrary high precision. The systems under consideration are a class of multi-agent systems under directed networks with switching topologies. The agents have discrete-time affine nonlinear dynamics, but their state functions do not need to be identical. It is shown that the learning processes resulting from the relative output formation of multi-agent systems can converge exponentially fast with the increase of the iteration number. In particular, this work induces a distributed algorithm that can simultaneously achieve the desired relative output formation between agents and regulate the movement of multi-agent formations as desired along the time axis. The illustrative numerical simulations are finally performed to demonstrate the effectiveness and performance of the proposed distributed formation algorithms. [ABSTRACT FROM AUTHOR]

SUMMARY This paper deals with formation control problems for multi-agent systems by using iterative learning control (ILC) design approaches. Distributed formation ILC algorithms are presented to enable all agents in directed graphs to achieve the desired relative formations perfectly over a finite-time interval. It is shown that not only asymptotic stability but also monotonic convergence of multi-agent formation ILC can be accomplished, and the convergence conditions in terms of linear matrix inequalities can be simultaneously established. The derived results are also applicable to multi-agent systems that are subject to stochastic disturbances and model uncertainties. Furthermore, the feasibility of convergence conditions and the effect of communication delays are discussed for the proposed multi-agent formation ILC algorithms. Simulation results are given for uncertain multi-agent systems to verify the theoretical study. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]