Tracking Problem Of Multi-agent Systems Research Articles

K-filter-based distributed adaptive bipartite tracking for nonlinear multi-agent systems with unknown control direction

This paper investigates K-filter-based distributed adaptive output-feedback bipartite tracking problem for multi-agent systems with unknown control direction. The agents are partitioned into two groups, where agents belonging to the same group are cooperative while agents belonging to the distinct group are competitive. Firstly, the unmeasurable state variables of the system under unknown control direction are estimated through K-filters. Then, a neighbourhood error is introduced to transform a structurally balanced network into a cooperative network. Based on K-filters, neighbourhood error, Nussbaum-type functions and dynamic surface control, a distributed adaptive output-feedback control protocol is designed to achieve the bipartite tracking. It is shown that all signals in the resulting closed-loop systems are semi-global bounded and the output of each agent can track the reference signal provided that the network is structurally balanced and contains a directed spanning tree. Finally, a numerical example is given to verify the effectiveness of the proposed method.

Distributed Adaptive Fault-Tolerant Control for Multiagent Systems via Virtual-Actuator-Based Reconfiguration.

This article aims to develop a virtual-actuator-based control scheme for the consensus tracking problem of multiagent systems (MASs) against actuator faults and mismatched disturbances. The proposed scheme has a double-layer structure. In the cyber layer, the nominal controller is designed with neighboring information for the fault-free case. While in the physical layer, the fault compensator, working as the virtual actuator, is applied to reconfigure faulty plants adaptively. This design enjoys the advantages that the nominal controller needs no adjustment and all its properties can be preserved after failure. Moreover, the proposed control scheme is distinguished by the following features: 1) the commonly imposed rank condition on outage faults is removed; 2) the norm bound of the leader's input is allowed to be unknown even though the topologies are switching and directed; and 3) there is no need to use the estimates of faults in the virtual actuator design, which means the negative impacts caused by the inaccurate fault estimation can be avoided. Finally, a numerical example is given to validate the effectiveness of the theoretical results.

Distributed Resilient Tracking of Multiagent Systems Under Actuator and Sensor Faults.

The distributed resilient tracking problem for multiagent systems (MASs) is investigated in the presence of actuator/sensor faults over directed topology. Both actuator fault and sensor fault are taken into account. Meanwhile, using the local information, the fault compensators are introduced. Then, based on the fuzzy-logic systems (FLSs) and modification technique of adaptive law, a novel distributed adaptive resilient control protocol is developed, which can compensate the effect of faults on the actuator and sensor. It turns out that all signals of MASs are bounded, while the tracking errors enter an adjustable bounded region around the origin. Toward the end, two simulations are provided to validate the effectiveness of the theoretical results.

Resilient Event-Triggered Adaptive Cooperative Fault-Tolerant Tracking Control for Multiagent Systems Under Hybrid Actuator Faults and Communication Constraints

This paper investigates the cooperative fault-tolerant tracking problem for multiagent systems with directed topology that experience communication link faults and actuator faults under malicious attacks. Based on a resilient event-triggered mechanism, a novel distributed high-order sliding-mode observer and a robust adaptive cooperative fault-tolerant control method are proposed, which can achieve the desired formation shape tracking under communication limitations and hybrid actuator faults caused by malicious attacks. First, the designed distributed event-triggered observer can estimate the unknown leader. Second, the proposed event-triggered mechanism can be used to reduce the impact of malicious attacks on the information and physical layers of the multiagent system. Additionally, the utilization rate of network communication is further improved so that it can save communication resources by reducing the number of event triggers. Then, based on the information of the leader, the designed robust adaptive cooperative fault-tolerant controller can compensate for the influence of the hybrid actuator faults on the system under malicious attacks. Finally, the effectiveness of the method is verified by two sets of example simulations.

Distributed Adaptive Consensus Output Tracking Problem of Nonlinear Multi-Agent Systems with Unknown High-Frequency Gain Signs under Directed Graphs

This paper deals with the consensus output tracking problem for multi-agent systems with unknown high-frequency gain signs, in which the subsystems are connected over directed graphs. The subsystems may have different dynamics, as long as the relative degrees are the same. A new type of Nussbaum gain is first presented to tackle adaptive consensus control of network-connected systems without the knowledge of the high-frequency gains. Adaptive laws and internal models are then proposed to handle the uncertainties and unknown parameters. An integral Lyapunov function based on sufficient conditions is finally introduced to tackle the asymmetry of the Laplacian matrix of directed graphs, into which we incorporate the new Nussbaum gain and the adaptive internal model to design the controller. It is apparent that the control scheme and the adaptive laws are fully distributed, which means that only the relative information of the neighbourhood subsystems’ outputs is used, and the simulation results validate the effectiveness of the control design, whereby they guarantee the asymptotic convergence of errors to zero as well as the boundedness of the state variables.

Distributed adaptive bipartite consensus tracking of high-order nonstrict-feedback nonlinear multi-agent systems

This paper is concerned with the problem of adaptive bipartite tracking problem for multi-agent systems (MASs) over signed directed graphs with unknown nonlinear functions. Each following agent is modelled by a higher-order nonlinear system in nonstrict-feedback form. The virtual and the actual control items of the considered system are the power functions with positive odd integers rather than linear items. A distributed neural-based adaptive backstepping scheme is proposed, where the unknown nonlinear dynamics are approximated by Neural networks. Under the proposed protocol, the bipartite output tracking errors converge to a small neighbourhood of the origin and all the signals of the closed-loop system remain semiglobally uniformly ultimately bounded. Since the considered high-order non-strict feedback nonlinear MASs in our paper include some existing nonlinear MASs as the special case, our result can be extended to control more general nonlinear MASs. Finally, the simulation results illustrate the validity of the proposed schemes by a numerical example and a practical example about a group of forced damped pendulums.

Finite-Time Consensus Tracking of Multi-Agent Systems Using Time-Fuel Optimal Pursuit Evasion

Computation of a decentralized feedback strategy for consensus tracking problem of multi-agent systems that communicate over a directed graph containing a directed spanning tree, is considered. The agents are subjected to bounded control inputs and are governed by double integrator dynamics. Agents transmit their state measurement data and upper bound on control input to other agents over directed edges in the graph. Using the transmitted data, the agents track the reference trajectory generated by an agent that is assigned as the root node. Each pair of agents connected by a directed edge is identified as a pursuer-evader pair where the child node (pursuer) tries to match its state with that of the parent node (evader), in finite time by consuming min-max amount of time-fuel. Nash equilibrium feedback strategies using time-fuel optimal switching curves are derived for the two-player time-fuel pursuit evasion game. By making use of the derived Nash equilibrium strategy each child node tracks its respective parent node's state by locally consuming min-max time-fuel cost. As a consequence, all agents are shown to track the reference trajectory generated by the root agent in finite time with reduced fuel consumption.

Coordination Tracking of Multiagent Systems With Active Replacement Strategy Under Node Failures

Coordination tracking problem of multiagent systems is studied for the application of threat defense in a monitored area. When targets intrude this area, agents which are nearby to them are set to track the targets. Modified nonsingular terminal sliding mode controller is proposed for the agents. We design a novel continuous function in the controller to eliminate the singularity, which makes it able to estimate the finite tracking time. Based on the estimated time, generalized Voronoi diagram considering the velocity of second-order agents and targets is presented. During the tracking process, the event of node failures will trigger a designed coordination strategy. The event that the target enters a new generalized Voronoi cell triggers active replacement coordination strategy. With the proposed active replacement strategy, the tracking time is reduced even under node failures. Comparison lemma is utilized to deal with the agent switching issue caused by node failures or active replacements under new event-triggered coordination strategies.

Bounded Observer-Based Consensus Algorithm for Robust Finite-Time Tracking Control of Multiple Nonholonomic Chained-Form Systems

This article is concerned with the leader–follower consensus tracking problem for multiagent systems with nonholonomic high-order chained-form dynamics subject to external disturbances. A novel distributed and bounded observer is first developed for each follower to estimate the leader information in a finite time. Then, a bounded fast terminal sliding-mode control protocol is constructed for each follower to track the estimated leader's states leading to the fast convergence performance as well as strong robustness. Contrary to some existing finite-time consensus tracking schemes, the control input constraint is taken into account by utilizing hyperbolic tangent saturation function to reduce the risk of the actuator saturation. Moreover, an approximation-based technique is introduced to reduce the conservatism of the upper bound of convergence time. Finally, some simulations for a set of wheeled mobile robots are carried out to demonstrate the efficiency of the proposed control algorithm.

Adaptive MLP neural network controller for consensus tracking of Multi-Agent systems with application to synchronous generators

In this study a novel cooperative controller design is developed to tackle the consensus tracking problem of Multi-Agent systems (MAS) based on multilayer perceptron neural network (MLPNN), applied on a distributed synchronous generator (SG) Multi-Agent system in presence of model uncertainties and external disturbances. Application of MLPNN in controller design can lead to smoother system response and can neutralize the impacts of model uncertainties. Furthermore, the proposed method benefits from a novel algorithm formerly known as error backpropagation (BP) algorithm to update and to regulate the weights of MLPNN adaptively based on the principles of consensus error. The proposed strategy can be very effective in control of the distributed SG Multi-Agent system due to its ability for system identification, parameter estimation, and disturbance approximation. Moreover, the utilization of neural networks can meet the criterion to make the consensus error uniformly ultimately bounded. Ultimately, simulation results illustrate the applicability and effectiveness of the novel MLPNN controller to model the system uncertainties and to deal with external disturbances of the distributed SG Multi-Agent system.

Distributed consensus for multi‐agent systems via adaptive sliding mode control

AbstractThis article considers the consensus of multi‐agent systems (MASs) with external disturbances by using sliding mode control. First, by establishing a novel sliding mode surface and designing a sliding mode control protocol with constant gains, the consensus of MASs with unknown bounded disturbances is studied. Second, based on the barrier function, a new type of adaptive sliding mode switching protocol is proposed, in which the control gains can be chosen randomly and the upper bound of the disturbance does not need to be known in a prior. By using this protocol, the consensus error can converge to any given region of zero in finite time. Moreover, the leaderless consensus and leader‐following tracking problem of MASs with general dynamics and external disturbances are also investigated via extending the proposed adaptive sliding mode protocol. Finally, some examples are presented to illustrate the effectiveness of the control strategies.

Iterative learning control for multi-agent systems with impulsive consensus tracking

In this paper, we adopt D-type and PD-type learning laws with the initial state of iteration to achieve uniform tracking problem of multi-agent systems subjected to impulsive input. For the multi-agent system with impulse, we show that all agents are driven to achieve a given asymptotical consensus as the iteration number increases via the proposed learning laws if the virtual leader has a path to any follower agent. Finally, an example is illustrated to verify the effectiveness by tracking a continuous or piecewise continuous desired trajectory.

Fully Distributed Consensus Tracking of Multiagent Systems With a High-Dimensional Leader and Directed Communication Topology

We study the asymptotic consensus tracking problem for multiagent systems (MASs) with uncertain dynamics under directed communication topology, where the leader has higher dimensional dynamics than followers. In our approach, the uncertainties are approximated by a neural network with arbitrary accuracy in compact regions. To remove the effects of leader’s high dimensional dynamics on consensus, a compensation controller with adaptive coupling strengths is designed. Moreover, to eliminate the effects of uncertain dynamics and external disturbances on consensus, a discontinuous feedback controller with neuro-adaptive law is given. Furthermore, we show fully distributed asymptotic consensus tracking of the considered MAS under the designed compensator plus the feedback controller can be achieved if the communication topology contains a directed spanning tree with the leader being the root. In addition, a theorem is given under the undirected topology. Finally, we perform a simulation to validate the theoretical result.

Distributed Tracking of Nonholonomic Targets Over Multiagent Systems

This paper considers the distributed tracking problem for multiagent systems (MASs) under connected and undirected graphs. A distributed estimator is proposed and analyzed for every agent to estimate the states of a general class of targets with continuous-time nonlinear dynamics. The agents employ a distributed estimator that is designed for each agent based on its information and communication with their local neighbors in the network for estimating the states of the targets in the presence of noise. It is proved that using the proposed distributed estimator, the agents can exponentially estimate the states of the noisy nonlinear targets with ultimate bounded errors. Simulation results demonstrate the effectiveness of the proposed estimator.

Formation Tracking via Iterative Learning Control for Multiagent Systems with Diverse Communication Time-Delays

In this paper, we consider the formation tracking problem for multiagent systems with diverse communication time-delays by using iterative learning control (ILC) method based on the frequency domain analysis. A first-order ILC law for multiagent systems with diverse communication time-delays is first proposed and its convergence conditions are given by the general Nyquist stability criterion and Gershgorin’s disk theorem. Then, in order for the system to track accurately, a second-order ILC law is presented. The conditions for system tracking with zero error are established. Numerical simulations show that the proposed ILC laws for multiagent systems with diverse communication time-delays are able to achieve effectively formation tracking. And the convergence speed remains the same as the learning control algorithm without communication delay.

Robust finite-time consensus control for multi-agent systems with disturbances and unknown velocities

In this paper, we investigated the finite-time consensus tracking problem for multi-agent systems with external bounded disturbances and input bounded disturbances and unknown velocities. Based on the Lyapunov finite-time theorem, a novel finite-time consensus control is constructed by using the backstepping method. For unknown velocities, the high-gain observer is used to estimate the velocity information. It is proved that the consensus can be achieved in finite time. The consensus shows fast response and strong robustness to various disturbances. Finally, the effectiveness of the results is illustrated by numerical simulations.

Distributed formation tracking using local coordinate systems

This paper studies the formation tracking problem for multi-agent systems, for which a distributed estimator–controller scheme is designed relying only on the agents’ local coordinate systems such that the centroid of the controlled formation tracks a given trajectory. By introducing a gradient descent term into the estimator, the explicit knowledge of the bound of the agents’ speed is not necessary in contrast to existing works, and each agent is able to compute the centroid of the whole formation in finite time. Then, based on the centroid estimation, a distributed control algorithm is proposed to render the formation tracking and stabilization errors to converge to zero, respectively. Finally, numerical simulations are carried to validate our proposed framework for solving the formation tracking problem.

Fixed-Time Connectivity-Preserving Distributed Average Tracking for Multiagent Systems

This brief studies the fixed-time average tracking problem for multiagent systems under communication constraints, in which each agent has a limited sensing range. A distributed nonlinear control protocol is proposed to make a team of agents track the average of multiple time-varying reference signals with bounded derivatives. Furthermore, the initial interaction patterns can be preserved under the designed protocol and the average tracking can be achieved within fixed time, where the bound of settling time is independent of the initial conditions. Thus, the fixed convergence time can be flexibly adjusted. Finally, some numerical examples are provided to illustrate the performance and effectiveness of the theoretical results.

Distributed Tracking of Nonlinear Multiagent Systems Under Directed Switching Topology: An Observer-Based Protocol

This paper deals with a consensus tracking problem for multiagent systems (MASs) with Lipschitz-type nonlinear dynamics and directed switching topology. Unlike most existing works where the relative full state measurements of neighboring agents are utilized, it is assumed that only the relative output measurements of neighboring agents are available for coordination. To achieve consensus tracking in the considered MASs, a new class of observer-based protocols is proposed. By appropriately constructing some topology-dependent multiple Lyapunov functions, it is theoretically shown that distributed consensus tracking in the closed-loop MASs equipped with the designed protocols can be ensured if each possible topology contains a directed spanning tree rooted at the leader and the dwell time for the switchings among different topology is less than a derived positive quantity. Interestingly, it is found that the communication topology for observers’ states may be independent with that of the feedback signals. The derived results are further extended to the case of directed switching topology with only average dwell time constraints. Finally, the effectiveness of the analytical results is demonstrated via numerical simulations.

Data-Driven Multiagent Systems Consensus Tracking Using Model Free Adaptive Control.

This paper investigates the data-driven consensus tracking problem for multiagent systems with both fixed communication topology and switching topology by utilizing a distributed model free adaptive control (MFAC) method. Here, agent's dynamics are described by unknown nonlinear systems and only a subset of followers can access the desired trajectory. The dynamical linearization technique is applied to each agent based on the pseudo partial derivative, and then, a distributed MFAC algorithm is proposed to ensure that all agents can track the desired trajectory. It is shown that the consensus error can be reduced for both time invariable and time varying desired trajectories. The main feature of this design is that consensus tracking can be achieved using only input-output data of each agent. The effectiveness of the proposed design is verified by simulation examples.