Global consensus tracking of discrete-time saturated networked systems via nonlinear feedback laws

This paper revisits the coordinated tracking of networked systems in the presence of input saturation. For discrete-time networked systems with high-order integrator typed dynamics and input saturation, nonlinear feedback laws are constructed and then sufficient conditions are established to guarantee the global consensus tracking of the systems. Finally, numerical simulations are given to support the theoretical results.     

Distributed fault-tolerant consensus tracking control for multiple Lagrangian systems with preset error bound constraints

Actuator faults often occur in physical systems, which seriously affect the transient performance and control accuracy of the system. For the finite-time consensus tracking problem of multiple Lagrangian systems with actuator faults and preset error constraints, a novel distributed fault-tolerant controller is proposed in this paper. The proposed controller is developed based on the barrier Lyapunov function method and the adding a power integrator technique, which can not only guarantee the steady-state performance of the system but also its transient performance. Due to its strong sensitivity to the variation of system errors, the proposed controller can quickly eliminate the system initial errors and the error perturbations caused by actuator faults. That is, the controller can guarantee that the consensus error converges to zero in a finite time and is always constrained within the preset error bound. Finally, the effectiveness of the developed controller is verified by simulation of a multi-manipulator system.     

Cooperative surrounding control with collision avoidance for networked Lagrangian systems

This paper investigates the cooperative surrounding control problem for networked multi-agent systems with nonlinear Lagrangian dynamics. With the consideration of the target with constant and time-varying velocity, two cooperative surrounding control algorithms with collision avoidance are proposed, in which possible collision among agents is prevented so as to achieve a more reliable and safer performance. For the case when the target has a constant velocity, a velocity observer is designed firstly for each agent. Secondly, to handle the nonlinear dynamics and avoid collisions, the neural networks and potential functions are used for the controller design. Then, the cooperative surrounding control algorithm is proposed such that all the agents surround the target with the desired relative positions. For the case when the target has a time-varying velocity, the velocity observer is designed under the assumption that the target’s partial acceleration is known for each agent. Then, the cooperative surrounding control algorithm is proposed such that the surrounding error between the target and each agent is bounded. The main difference between these two algorithms is that the former can ensure the collision avoidance among target and agents, while the latter can do so only among agents because the target’s velocity is time-varying. The Lyapunov theory is used to prove the stability of the cooperative surrounding control algorithms. The simulation illustrates the effectiveness of the theoretical results.     

Optimal tracking performance for networked control systems with quantization

This paper investigates the optimal tracking performance of the multiple-input multiple-output (MIMO) discrete-time networked control systems (NCSs) considering the quantization of communication channel. The tracking performance is adopted for the H₂ square error criterion. The optimal tracking performance expression is obtained by using the co-prime factorization, the partial factorization, the inner–outer factorization and the spectral decomposition methods. Moreover, the paper also includes the exploration of the optimal tracking performance with input power constraint. The obtained results have demonstrated that the optimal tracking performance is influenced by the non-minimum phase zeros, unstable poles and their directions, the reference signal and the quantization interval. Moreover the theoretical results have also been proven using a number of different examples.     

Event-triggered learning consensus of networked heterogeneous nonlinear agents with switching topologies

In this work, a lifted event-triggered iterative learning control (lifted ETILC) is proposed aiming for addressing all the key issues of heterogeneous dynamics, switching topologies, limited resources, and model-dependence in the consensus of nonlinear multi-agent systems (MASs). First, we establish a linear data model for describing the I/O relationships of the heterogeneous nonlinear agents as a linear parametric form to make the non-affine structural MAS affine with respect to the control input. Both the heterogeneous dynamics and uncertainties of the agents are included in the parameters of the linear data model, which are then estimated through an iterative projection algorithm. On this basis, a lifted event-triggered learning consensus is proposed with an event-triggering condition derived through a Lyapunov function. In this work, no threshold condition but the event-triggering condition is used which plays a key role in guaranteeing both the stability and the iterative convergence of the proposed lifted ETILC. The proposed method can reduce the number of control actions significantly in batches while guaranteeing the iterative convergence of tracking error. Both rigorous analysis and simulations are provided and confirm the validity of the lifted ETILC.     

Bipartite output consensus for heterogeneous linear multi-agent systems with fully distributed protocol

This paper addresses the problem of bipartite output consensus of heterogeneous multi-agent systems over signed graphs. First, under the assumption that the sub-graph describing the communication topology among the agents is connected, a fully distributed protocol is provided to make the heterogeneous agents achieve bipartite output consensus. Then for the case that the topology graph has a directed spanning tree, a novel adaptive consensus protocol is designed, which also avoids using any global information. Each of these two protocols consists of a solution pair of the regulation equation and a homogeneous compensator. Numerical simulations show the effectiveness of the proposed approach.     

Modified tracking performance limitation of networked time-delay systems with two-channel constraints

This paper investigates the modified tracking performance limitation of the networked time-delay systems with two-channel constraints. We consider both the white Gaussian noise and packet dropout constraints in the communication channels. In the plant, the non-minimum phase, unstable poles and time-delay are considered. The modified tracking performance limitation expressions will be achieved using the co-prime factorization and the spectral decomposition technique, and the two-parameter controller is adopted. The results show that the modified tracking performance limitation is related to the intrinsic properties of the given plant, including the non-minimum phase zeroes, the unstable poles and the time-delay. Furthermore, the network communication parameters, e.g. the white Gaussian noise, the packet-dropouts probability and the modified factor affect the modified tracking performance limitation of the networked time-delay systems. Finally, some particular examples are provided to illustrate the efficiency of the proposed method.     

Bipartite consensus of multi-agent systems with matched uncertainty via fully distributed edge-based event-triggered mechanism

The leader-following bipartite consensus of multi-agent systems (MASs) with matched uncertainty is investigated by using the fully distributed asynchronous edge-based event-triggered mechanism. Firstly, event-triggered mechanisms are constructed for each edge and the leader to decrease the consumption of system resources. The state feedback and output feedback control protocols are proposed, which do not depend on the global values of the communication graph. Secondly, sufficient conditions for the bipartite consensus of MASs are obtained by Lyapunov stability theory. Thirdly, the feasibility of the proposed event-triggered mechanisms is further verified by exclusion of Zeno phenomenon. Finally, the effectiveness of control protocol is illustrated by simulation.     

Distributed consensus of a class of networked heterogeneous multi-agent systems

In this paper, we consider the consensus problem of a class of heterogeneous multi-agent systems composed of the linear first-order and second-order integrator agents together with the nonlinear Euler–Lagrange (EL) agents. First, we propose a distributed consensus protocol under the assumption that the parameters of heterogeneous system are exactly known. Sufficient conditions for consensus are presented and the consensus protocol accounting for actuator saturation is developed. Then, by combining adaptive controller and PD controller together, we design a protocol for the heterogeneous system with unknown parameters (in the nonlinear EL dynamics). Based on graph theory, Lyapunov theory and Barbalat's Lemma, the stability of the controllers is proved. Simulation results are also provided to illustrate the effectiveness of the obtained results.     

Optimal tracking performance and design of networked control systems with packet dropouts

The optimal tracking problem for single-input–single-output (SISO) networked control system over a communication channel with packet dropouts is studied in this paper. The tracking performance is measured by the energy of the error signal between the output of the plant and the reference signal. It is shown that the optimal tracking performance is constrained by nonminimum phase zeros, unstable poles, the characteristics of the reference signal and packet dropout probability, and the optimal controller is obtained. It is also shown that when the communication constraint does not exist, the optimal tracking performance reduces to the existing normal tracking performance of the control system without a communication channel. The result shows how the packet dropouts probability of a communication channel may fundamentally constrain a control system's tracking ability. Some typical examples and simulations are given to illustrate the theoretical results.     

Leader-Follower finite-time consensus of multiagent systems with nonlinear dynamics by intermittent protocol

This paper deals with the leader-follower finite-time consensus problem for multiagent systems with nonlinear dynamics via intermittent protocol. The topological structure of the followers is undirected or balanced digraph. Different from most existing works concerning nonlinear dynamics (satisfies Lipschitz continuity), the nonlinear dynamics of each agent satisfies Hölder continuity in this paper. In light of the finite-time control technique, the intermittent control protocol is designed to reach accurate leader-follower finite-time consensus. It is justified that the leader-follower finite-time consensus can be realized if the length of communication is greater than a critical value by using limit theory. Finally, two numerical examples are exhibited to validate the effectiveness of the proposed scheme.     

Distributed state estimator-based consensus tracking of multi-agent systems with exogenous disturbance

The consensus tacking problem for multi-agent systems with a leader of none control input and unknown control input is studied in this paper. By virtue of the relative state information of neighboring agents, state estimator and disturbance estimator are designed for each follower to estimate the system states and exogenous disturbance, respectively. Meanwhile, a novel control protocol based on two estimators is designed to make tracking error eventually converge to zero. Furthermore, the obtained results are further extended to the leader with unknown control input. A novel state estimator with adaptive time-varying gain is proposed such that consensus tracking condition is independent of the Laplacian matrix with regard to the communication topology. Finally, two examples are presented to verify the feasibility of the proposed control protocol.     

Dynamic event-Based resilient consensus of networked lagrangian systems under DoS attacks

In this paper, the dynamic event-based resilient consensus control of the multiple networked Euler-Lagrangian (E-L) systems under the Denial of Service (DoS) attacks is considered. Compared with linear cyber-physical systems, nonlinear networked E-L systems are more complex and closer to actual mechanical systems. For the situation where the topology is a strongly connected directed topology, a controller based on a dynamic event-trigger mechanism is designed to achieve consensus control for the networked E-L system in the absence of DoS attacks. Sufficient conditions are presented, which can guarantee the closed-loop system be stable. Then the resilient consensus problem of event-based controllers under energy-constrained DoS attacks is analyzed. The conditions related to the duration and frequency of DoS attacks are given. Zeno behavior is proved does not exist in the proposed control scheme. Finally, some numerical simulation results are given for verifying the theoretical results.     

Limited-energy output formation for multiagent systems with intermittent interactions

Limited-energy output formation design and analysis problems are addressed for multiagent systems with intermittent interactions. Firstly, a new dynamic output feedback formation control protocol with the limited energy supply is proposed, which contains two independent parts associated with the interactive interval and the non-interactive interval. Then, sufficient conditions for leaderless limited-energy output formation are proposed by a new two-step design approach, which can make two gain matrices of the formation control protocol be designed independently. Meanwhile, the output formation reference function is determined to describe the absolute motion of all agents as a whole. Moreover, by constructing two transformation matrices with specific structures, the main conclusions for leaderless multiagent systems are extended into leader-follower ones. Finally, two numerical simulations are shown to demonstrate theoretical results.     

Adaptive-observer-based formation tracking of networked uncertain underactuated surface vessels with connectivity preservation and collision avoidance

This paper investigates an adaptive output-feedback formation tracking problem for ensuring connectivity preservation and collision avoidance among networked uncertain underactuated surface vessels (USVs) with different communication ranges. An adaptive observer using neural networks is designed to estimate the velocity information of USVs where neural networks estimate unknown nonlinearities of USVs. Especially, contrary to the existing related work of USVs, a new state transformation technique for the adaptive observer design is presented to relax the condition requiring the boundedness of the yaw velocity of USVs. Then, the recursive tracker design strategy is established by using a unified error function for connectivity-preserving and collision-avoiding formation tracking, without employing any potential functions. The proposed formation tracker does not require additional neural networks to estimate unknown nonlinearities derived from the tracker design procedure. The proposed theoretical result is proved in the sense of Lyapunov.     

Distributed finite-time adaptive consensus tracking control for multiple AUVs with state constraints

Aiming at the consensus tracking control problem of multiple autonomous underwater vehicles (AUVs) with state constraints, a new neural network (NN) and barrier Lyapunov function based finite-time command filtered backstepping control scheme is proposed. The finite-time command filter is utilized to filtering the virtual control signal, the error compensation signal is constructed to eliminate filtering error due to the use of filter, and the NN approximation technology is used to deal with the unknown nonlinear dynamics. The control scheme can guarantee that the consensus tracking errors of position states converge into the desired neighborhood of the origin in finite-time while not exceeding the predefined constraints. Finally, simulation studies prove the feasibility of proposed control algorithm.     

Asynchronous consensus of multi-agent systems via variable period intermittent control with heterogeneous pulse-modulated function

Most existing consensus control in multi-agent systems (MASs) require agents to update their state synchronously, which means that some agents need to wait for all individuals to complete the iteration before starting the next iteration. To overcome this bottleneck, this paper studied asynchronous consensus problems of second-order MASs (SOMASs) with aperiodic communication. An asynchronous pulse-modulated intermittent control (APIMC) with heterogeneous pulse-modulated function and time-varying control period, which can unify impulsive control and sampled-data control, is proposed for the consensus of SOMASs. A time-varying discrete system is constructed to describe the evolution of the sample values of position and velocity of the SOMAS. Then, by the analysis tools from the stochastic matrix and the properties of the Laplace matrix of graph, some effective conditions are obtained to show the relationship between the convergence of the controlled SOMASs and the control parameters. Finally, a 300-node SOMAS whose topology is a random geographic network is included to verify the feasibility of the proposed control and the correctness of the theoretical analysis.     

Data-driven control of consensus tracking for discrete-time multi-agent systems

This paper investigates the consensus tracking problem of leader-follower multi-agent systems. Different from most existing works, dynamics of all the agents are assumed completely unknown, whereas some input-output data about the agents are available. It is well known from the Willems et al. Fundamental Lemma that when inputs of a linear time-invariant (LTI) system are persistently exciting, all possible trajectories of the system can be represented in terms of a finite set of measured input-output data. Building on this idea, the present paper proposes a purely data-driven distributed consensus control policy which allows all the follower agents to track the leader agent’s trajectory. It is shown that for a linear discrete-time multi-agent system, the corresponding controller can be designed to ensure the global synchronization with local data. Even if the data are corrupted by noises, the proposed approach is still applicable under certain conditions. Numerical examples corroborate the practical merits of the theoretical results.     

Optimal design for manipulation of random consensus over discrete information in networked systems

In distributed and cooperative systems, the network structure is determinant to the success of the strategy adopted to solve complex tasks. Those systems are primarily governed by consensus protocols whose convergence is intrinsically dependent on the network topology. Most of the consensus algorithms deal with continuous values and perform average-based strategies to reach cohesion over the exchanged information. However, many problems demand distributed consensus over countable values, that cannot be handled by traditional protocols. In such a context, this work presents an approach based on semidefinite programming to design the optimal weights of a network adjacency matrix, in order to control the convergence of a distributed random consensus protocol for variables at the discrete-space domain, based on the voter model. As a second contribution, this work uses Markov theory and the biological inspiration of epidemics to find out a dynamical spreading model that can predict the information diffusion over this discrete consensus protocol. Also, convergence properties and equilibrium points of the proposed model are presented regarding the network topology. Finally, extensive numerical simulations evaluate the effectiveness of the proposed consensus algorithm, its spreading model, and the approach for optimal weight design.     

Observer-based mean square consensus of nonlinear networked systems under Markov switching communication topologies

This paper aims to investigate the mean square consensus (MSC) problem of a class of nonlinear networked systems subject to directed and stochastic switching communication topologies, where the switching law is determined by an ergodic continuous-time Markov process. The cooperative consensus controller is designed by using an observer-based method. Firstly, for the case with Lur’e nonlinear dynamics, by developing a stochastic Lyapunov function, we show that the MSC under consideration can be realized if the union of the underlying network graphs has a directed spanning tree. It is worth noting that none of the network graphs is required to contain a directed spanning tree. Moreover, we study the MSC problem for networked systems with Lipschitz-type nonlinear dynamics. Finally, a numerical simulation is conducted on multiple Chua’s circuit systems to illustrate the effectiveness of the proposed controllers.