Tracking Control For Multi-agent Systems Research Articles

Event-Triggered Cooperative Tracking Control of Multiagent Systems With a Dynamic Leader via Approximate Dynamic Programming

Event-Triggered Cooperative Tracking Control of Multiagent Systems With a Dynamic Leader via Approximate Dynamic Programming

Distributed model free adaptive fault-tolerant consensus tracking control for multiagent systems with actuator faults

The model free fault-tolerant consensus tracking control problem is studied for the multiagent systems with actuator faults. To reduce the impact of fault, an adaptive estimation law is developed to estimate the fault information online, which doesn't need an additional training and can reduce the computational cost. With the help of the adaptive fault compensation, a distributed model free adaptive fault-tolerant consensus tracking mechanism is designed to guarantee that agents are able to track the expected signal. Furthermore, only the input/output data of agents are employed throughout the design process, the system dynamics are not required. The effectiveness of developed approach is illustrated through the simulation examples.

Hierarchical Q-Learning Path Planning for Cooperative Tracking Control of Multi-Agent Systems With Lumped Uncertainties

Hierarchical Q-Learning Path Planning for Cooperative Tracking Control of Multi-Agent Systems With Lumped Uncertainties

Bearing-Only Formation Tracking Control for Multi-Agent Systems With Time-Varying Velocity Leaders

Bearing-Only Formation Tracking Control for Multi-Agent Systems With Time-Varying Velocity Leaders

Distributed Prescribed Performance Tracking Control for Multiagent Systems With Collision Avoidance and Initial Restriction Overcoming

Distributed Prescribed Performance Tracking Control for Multiagent Systems With Collision Avoidance and Initial Restriction Overcoming

Predefined-Time Consensus Tracking Control for Multiagent System With Channel Fading.

This article proposes a predefined-time leader-following consensus control scheme for a second-order multiagent system (MAS) whose communication network is subject to channel fading. New distributed observers are designed to achieve prescribed-time leader's states estimation under undirected graph and digraph over faded communication channel. Then, a new adaptive dynamic surface predefined-time control is developed for the elimination of the mismatched disturbances stemmed from estimation error and achieving practical predefined-time leader-following consensus. It is proved that the developed control approach achieves predefined-time consensus tracking. This article's contribution is to propose novel distributed observers to remove the influence of channel fading and estimate leader's states under undirected graph and digraph within prescribed time and develop a novel predefined-time control to achieve predefined-time consensus tracking over fading channel. A simulation example verifies that the designed control scheme is effective.

A Novel Bipartite Consensus Tracking Control for Multiagent Systems Under Sensor Deception Attacks.

This article presents a novel adaptive bipartite consensus tracking strategy for multiagent systems (MASs) under sensor deception attacks. The fundamental design philosophy is to develop a hierarchical algorithm based on shortest route technology that recasts the bipartite consensus tracking problem for MASs into the tracking problem for a single agent and eliminates the need for any global information of the Laplacian matrix. As the sensors suffer from malicious deception attacks, the states cannot be measured accurately, we thus construct a novel dynamic estimator to estimate the actual states, which, together with a new coordinate transformation involving the attacked and estimated state variables, allows a distributed security control scheme to be developed, in which the singularity of the adaptive iterative process involved in existing works is completely avoided. Furthermore, the Nussbaum functions are included in the controller to account for the influence of the unknown control gains caused by sensor deception attacks. It is shown that the distributed consensus tracking errors converge to a small neighborhood of the origin, and all the signals in the closed-loop system remain bounded. Simulation on a forced damped pendulums (FDPs) is conducted to demonstrate and verify the effectiveness of the proposed strategy.

Adaptive Time-Varying Formation Tracking Control for Multiagent Systems With Nonzero Leader Input by Intermittent Communications.

The time-varying formation (TVF) tracking problem is studied for linear multiagent systems (MASs), where followers reach a preset TVF when tracking the leader's state. Followers are divided into the informed ones, which directly receive the leader's information, and uninformed ones. To alleviate communication requirements, trigger mechanisms are designed for the leader and all edges. Note that the designed trigger mechanisms enable the leader to send information intermittently and each follower to transmit information asynchronously when the corresponding trigger mechanism is satisfied. To address the TVF tracking problem, the node-event (for the leader) and (dynamic) edge-event triggered adaptive control strategy is proposed, which is fully distributed and has no relation to the system network's scale. Moreover, the MASs do not exhibit the Zeno behavior. Finally, a practice example is introduced to effectively illustrate the theoretical results.

Multi-Group Tracking Control for MASs of UAV with a Novel Event-Triggered Scheme

The flight control of UAVs can be implemented and theoretically analyzed using multi-agent systems (MASs), and tracking control is one of the important control technologies. This paper studies multi-group tracking control for multi-agent systems of UAV, in which the control scheme combines event-triggered technology and impulsive theory. The advantage of multi-group tracking control lies in its ability to realize multiple groups of tracking targets and make the UAV complete multiple groups of tasks. The tracking control makes use of a novel dynamic event-triggered control (DETC) proposed in this paper, in which it can better regulate and optimize the triggering frequency by adjusting the parameters. Furthermore, several forms of network interference that may affect the safety of UAV tracking control have also been resolved. Lastly, simulations are presented with numerical examples to showcase the efficacy of the proposed tracking control.

Distributed Tracking Control of Multi-Agent Systems With Transfer Restricted Switching Topologies: A Cycle Stabilization Approach

This paper addresses the consensus tracking control of multi-agent systems (MASs) with transfer restricted switching topologies. The transfer switching between topologies is described by a novel switching digraph. Under the switching digraph, multiple time-varying Lyapunov functions (MTVLFs) allowed to increase are constructed to characterize the energy change of error system. A cycle stabilization approach is proposed to achieve the consensus tracking of MASs even if all communication topologies do not contain a directed spanning tree rooted at the leader node. Finally, three examples are provided to validate the effectiveness of the results.

Adaptive tracking control for multiagent systems with event‐triggered communication and asymmetric input saturation

AbstractIn this paper, the adaptive tracking control problem is investigated for the multiagent systems with event‐triggered (ET) communication and asymmetric input saturation. By adopting an auxiliary system, the problem of asymmetric input saturation is successfully handled. Two ET mechanisms are employed in the controller‐to‐actuator channel and communication channel respectively to economize the limited communication resources. The update frequency of the controller can be reduced by devising a novel switching ET mechanism, which can unify the three existing ET schemes. Based on a backstepping technique, a distributed ET controller is devised, which only requires the sampled value of neighboring states. Due to the discontinuity of the ET state signals, the repetitive differentiation of virtual control laws will not be computed. To solve this problem, the predesigned differentiable partial derivatives of virtual control laws are used to construct the ET virtual control laws. By applying the Lyapunov stability method, it is proved that the desired tracking performance and the stability of the closed‐loop system can be guaranteed. Finally, a simulation example demonstrates that the proposed control strategy is effective.

Dynamic Event-Triggered Adaptive Tracking Control for Multiagent Systems with Power Exponential Function Using Fuzzy Disturbance Observer

Dynamic Event-Triggered Adaptive Tracking Control for Multiagent Systems with Power Exponential Function Using Fuzzy Disturbance Observer

Distributed Fault-Tolerant Formation Tracking Control for Multiagent Systems With Multiple Leaders and Constrained Actuators.

The distributed formation tracking control problem with multiple leaders under actuator faults and constraints is investigated in this article. All followers in the multiagent system should achieve a desired time-varying formation and track the convex combination of multiple leaders. To accomplish the control task, an active reconfigurable control scheme is proposed using the local information between agents, as well as the fault values of individuals provided by fault estimation observers. Combining with the Lyapunov stability theorem and the property of the Laplacian matrix, the control gains are calculated using the adaptive technique with a formation tracking feasibility condition. The original reconfigurable protocol is modified by utilizing anti-windup compensators to against saturation phenomenons (both magnitude and rate) in actuators. The simulation results validate that the presented scheme can address the faults as well as the actuator saturation.

Human-in-the-loop distributed cooperative tracking control with applications to autonomous ground vehicles: A data-driven mixed iteration approach

This paper proposes a data-driven learning algorithm for the human-in-the-loop cooperative tracking control of multi-agent systems with completely unknown dynamics. The core role of the human is to supervise a team of agents via sending a control command to only the leader vehicle, where the leader is a non-autonomous agent that has a nonzero but bounded control input receiving the control command from the human operator. To effectively reduce communication frequency among vehicles, a distributed observer only using the relative input–output information is projected to approximate the relative state information of agents. Then, taking the transient state into account instead of the steady-state, a value iteration (VI) algorithm is proposed to solve the optimal output trajectory tracking control without an initial stabilizing control policy but sacrificing the convergence rate. Under the VI algorithm, a mixed iteration (MI) algorithm by making the full advantages of both VI and the traditional policy iteration algorithms is developed, which not only guarantees the convergence rate but also removes the requirement of an initial stabilizing control policy. Finally, a practical multi-robot network is provided to verify and compare the advantages of the proposed theoretical algorithm.

Sliding mode-based finite-time consensus tracking control for multi-agent systems under actuator attacks

Since it is inevitable for multi-agent systems (MASs) to encounter external malicious attack in the complex network environment, this paper studies the finite-time consensus tracking (FTCT) problem of MASs with unknown follower's states via sliding mode control (SMC) in the presence of actuator attacks. First, the states of each follower can be approximated by the designed appropriate observers. Then, the FTCT problem of MASs in two stages (i.e., reaching phase and sliding motion phase) of sliding mode dynamic is discussed by introducing partitioning strategy. Besides, the lower bound for the reaching phase is analyzed to ensure the feasibility of the designed SMC. The optimization problem is then formulated and is efficiently solved by the genetic algorithm (GA). Note that the attacks considered in this paper are false data injection attacks (FDIAs). Finally, simulation results are presented to illustrate the effectiveness and applicability of the proposed control method.

Tracking Control of Multi-Agent Systems Using a Networked Predictive PID Tracking Scheme

With the rapid development of network technology and control technology, a networked multi-agent control system is a key direction of modern industrial control systems, such as industrial Internet systems. This paper studies the tracking control problem of networked multi-agent systems with communication constraints, where each agent has no information on the dynamics of other agents except their outputs. A networked predictive proportional integral derivative (PPID) tracking scheme is proposed to achieve the desired tracking performance, compensate actively for communication delays, and simplify implementation in a distributed manner. This scheme combines the past, present and predictive information of neighbour agents to form a tracking error signal for each agent, and applies the proportional, integral, and derivative of the agent tracking error signal to control each individual agent. The criteria of the stability and output tracking consensus of multi-agent systems with the networked PPID tracking scheme are derived through detailed analysis on the closed-loop systems. The effectiveness of the networked PPID tracking scheme is illustrated via an example.

Fault-Tolerant Time-Varying Formation Tracking Control for Multi-Agent Systems With Actuator Faults and Switching Topologies

Fault-Tolerant Time-Varying Formation Tracking Control for Multi-Agent Systems With Actuator Faults and Switching Topologies

Formation Tracking Control for Multi-Agent Systems with Collision Avoidance and Connectivity Maintenance

This paper investigates the formation tracking control of multiple agents with a double-integrator model and presents a novel distributed control framework composed of three items: a potential-based gradient term, a formation term, and a navigation term. Considering the practical situation, each agent is regarded as a rigid-body with a safe radius and a sensing region. To enable collision avoidance and connectivity maintenance among multiple agents, a new potential function with fewer parameters is established. The predetermined formation is also achieved by taking the difference between the actual displacement and the desired displacement as a consensus variable. Lastly, the virtual navigator provides trajectory signals and guides the multiple agent movement. Two instances of an equilateral triangle formation and a hexagonal formation are used in the simulation to verify the proposed method.

A Multilayer Graph for Multiagent Formation and Trajectory Tracking Control Based on MPC Algorithm.

This article studies the formation and trajectory tracking control of multiagent systems. We present a novel multilayer graph for the multiagent system to enable extensibility of the interaction network. Based on the multilayer graph, a formation control law by using the potential function approach is developed for autonomous formation, formation maintenance, collision, and obstacle avoidance. When the desired formation is achieved, the barycentric of the formation shape is viewed as a virtual leader, and a model predictive control (MPC) scheme is applied to the virtual leader for tracking a reference trajectory; meanwhile, the agents will maintain the desired angles and distances via the formation control law. By applying the proposed schemes, the tasks of formation maintenance and trajectory tracking in a constrained space are fulfilled. Comprehensive simulation studies under different environmental constraints and trajectories confirm the effectiveness of the proposed approaches in addressing the formation and trajectory tracking problems.

Adaptive cooperative fault-tolerance tracking control for multi-agent system with hybrid actuator faults and multiple unknown control directions

This paper investigates the cooperative fault-tolerant control for the nonlinear multi-agent system with hybrid actuator faults and multiple unknown control directions based on robust adaptive fuzzy sliding mode technology (RAFSMT). Firstly, a piecewise Nussbaum function is proposed to solve the multiple unknown control directions, and the robust adaptive fuzzy technique is designed to handle the nonlinear uncertainty in the system. Meanwhile, a first-order sliding mode differentiator is introduced, and it is combined with the adaptive backstepping technique to obtain the first-order derivative of the virtual control law. Moreover, a robust bounded method is used to improve the convergence speed and tracking accuracy of the designed adaptive cooperative controller. Then, the designed controller can effectively solve the fault-tolerant tracking control problem of multi-agent systems with hybrid actuator faults and unknown control directions. Compared with the methods in other references, the proposed method can make the system still maintain a good tracking effect with small steady-state error, short response time, and good fault tolerance. Finally, the effectiveness of the proposed method is verified by the comparison of simulation examples.