Fully distributed H∞ tracking control for multi-agent systems with prescribed-time convergence and external disturbances

In this paper, the decentralized optimal tracking control for multi-agent system with large population and input constraint has been investigated. Conventional multi-agent optimal tracking control algorithms are facing difficulties caused by both communication and computation limits especially when the total number of agents goes to infinity, which is known as the "Curse of Dimensionality". Moreover, the effects from practical scenario such as system input constraints has not thoroughly studied in optimal tracking control problems for massive multi-agent systems. In the paper, the Mean-Field game theory (MFG) theory has been adopted to break the "Curse of Dimensionality" firstly. Then, a novel online reinforcement learning algorithm, named Actor-Critic-Mass (ACM), has been designed to estimate the optimal tracking control for massive multi-agent systems. ACM has three Neural Networks (NNs), i.e. actor, critic, and mass NN, that can online approximate the optimal control policy, cost function, and all agents' state probability density function. The effects from input constraints are integrated into the optimal tracking control problem through introducing a modified cost function. The NNs weights are effectively tuned by the Mean-Field equations. Eventually, the effectiveness and efficiency of the proposed optimal control method has been demonstrated through numerical simulations.

This paper studies robust tracking control for a leader-follower multi-agent system (MAS) subject to disturbances. A challenging problem is considered here, which differs from those in the literature in two aspects. First, we consider the case when all the leader and follower agents are affected by disturbances, while the existing studies assume only the followers to suffer disturbances. Second, we assume the disturbances to be bounded only in rates of change rather than magnitude as in the literature. To address the new challenges, we propose a novel observer-based distributed tracking control design. As a distinguishing feature, the followers can cooperatively estimate the disturbance affecting the leader through to adjust their maneuvers accordingly, which is enabled by the design of first-of-its-kind distributed disturbance observer. We build a specific approach for MASs. Further, we prove that they lead to bounded-error tracking for the considered context and further, asymptotically convergent tracking under a mild relaxation of disturbance setting. We validate the proposed approach using a simulation example.

This article addresses the consensus tracking control of multiagent systems (MASs) via a quadratic programming (QP) optimization framework, where the control Lyapunov function (CLF) condition serves as a constraint. The optimal controllers, derived through the QP solver, not only ensure the tracking control objective but also minimize the cost functions of agents. To enhance energy efficiency, discontinuous control methods, such as intermittent control strategy and event-triggered mechanism, are employed in the control framework. The CLF-based QP controllers are only updated at specific time instants, in order to reduce the frequency of QP problem-solving. In addition to considering optimization, the proposed methods are extended to uncertain MASs to enhance robustness, where the uncertainty is modeled by Gaussian process regression. In the end, simulation results are provided to demonstrate the feasibility of the theoretical analysis.

Delay-dependent distributed event-triggered tracking control for multi-agent systems with input time delay

This paper investigates the event-triggered leader-follower tracking control for the general linear multi-agent systems over directed switching networks. We first propose a distributed event-triggered observer to estimate the state of the leader. We guarantee that the estimation errors of the observer are contained within a certain bound and the observer system does not exhibit Zeno behavior The observer is then used to construct an event-triggered controller in a fully distributed manner to achieve the bounded tracking errors. A numerical example is presented to illustrate the efficacy of the theoretical results.

Event-triggered leader-following tracking control for multivariable multi-agent systems

Leader–follower optimal coordination tracking control for multi-agent systems with unknown internal states

Event-triggered optimal consensus tracking control for multi-agent systems with unknown internal states and disturbances

This paper investigates the finite-time sliding mode control problem for multiagent systems with actuator failures and external disturbances. Given the presence of unknown nonlinear terms in the controlled multiagent systems, a radial basis function neural network is employed to ensure system robustness. To achieve consensus tracking of sliding mode dynamics within a finite-time, a finite-time integral sliding manifold is proposed. An adaptive law is designed to estimate the unknown fault coefficient, and then a distributed event-triggered adaptive sliding mode fault-tolerant control protocol is developed to deal with external disturbances and actuator faults in multiagent systems, which can effectively reduce the communication bandwidth and enhance reliability against actuator faults. To verify the effectiveness of the proposed control method, a numerical example is provided.

This paper presents the hierarchical Q-learning path planning (HQPP) architecture for solving the cooperative tracking control problem of multi-agent systems (MASs) with lumped uncertainties in an unknown environment. The presented architecture consists of three layers, namely, the decision layer, the distributed estimated layer, and the local control layer. Specifically, in the decision layer, we propose the dynamic parameter and trajectory fitting Q-learning (DPTF-Q-learning) algorithm to find a feasible continuous trajectory to the target in an unknown environment. In addition, two dynamic parameters are proposed and introduced into the DPTF-Q-learning algorithm to shorten the required minimum number of steps in the training process. Then, the distributed estimated layer is designed to broadcast the continuous trajectory generated from the decision layer based on the directed communication topology containing a spanning tree. In the local control layer, the cooperative tracking control (CTC) algorithm is proposed to achieve cooperative tracking for MASs in the presence of uncertain dynamics and external disturbances. The sufficient conditions for achieving cooperative tracking control are rigorously derived by employing Lyapunov argument. Finally, numerical simulations are presented to verify the effectiveness of the proposed architecture. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the need of developing an integrated path planning and control method for cooperative tracking of multi-agent systems in a no-signal environment and without the presence of users. Most related works are limited to separate fields: 1) most existing path planning techniques are only applicable to a single agent and discrete environments, and 2) most existing cooperative tracking algorithms focus on guaranteeing control stability and error convergence without decision-making capabilities. To address the above issues, this work proposes a hierarchical control architecture based on reinforcement learning for multi-agent systems to achieve path planning and cooperative tracking tasks. In addition, multi-agent systems exhibit strong robustness and fault tolerance due to their inherent characteristics, so the above mentioned research can be well applied to post-disaster rescue, intelligent logistics, future war, and so on. Numerical simulations based on Matlab and Python verify the effectiveness of the proposed architecture.

In this paper, a distributed output feedback consensus tracking control scheme is proposed for second-order multi-agent systems in the presence of uncertain nonlinear dynamics, external disturbances, input constraints, and partial loss of control effectiveness. The proposed controllers incorporate reduced-order filters to account for the unmeasured states, and the neural networks technique is implemented to approximate the uncertain nonlinear dynamics in the synthesis of control algorithms. In order to compensate the partial loss of actuator effectiveness faults, fault-tolerant parts are included in controllers. Using the Lyapunov approach and graph theory, it is proved that the controllers guarantee a group of agents that simultaneously track a common time-varying state of leader, even when the state of leader is available only to a subset of the members of a group. Simulation results are provided to demonstrate the effectiveness of the proposed consensus tracking method.

ABSTRACTIn this article, we consider the stability and performance of anti‐windup control for the leader‐following output tracking problem of linear multiagent systems (MASs), where each follower is subject to asymmetric input constraint and external disturbance. The MAS with asymmetric constraint is described as a switched MAS with symmetrically saturated input. The anti‐windup approach is used to deal with the input saturation in the switched MAS. First, a distributed control protocol in the form of general dynamic output feedback is designed to guarantee that the unconstrained system has a high performance. Then, an agent‐dependent switching anti‐windup compensation is constructed to mitigate the saturation effect on the system performance. Finally, it is shown via a numerical example that the proposed switching anti‐windup method is less conservative compared with the existing studies.

Based on the fixed directed topology, the finite-time tracking control problem for multi-agent systems with external disturbances is studied. A novel distributed finite-time consensus control protocol is constructed based on the non-singular terminal sliding mode scheme under the comparative state information of the agent. The proposed protocol can drive the follower’s state to track the leader’s state for a limited time. Finally, a consensus on finite-time tracking control is achieved and the convergence time can be estimated. The validity of the results is a case in the numerical simulation.

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.

Asynchronous adaptive event-triggered tracking control for multi-agent systems with stochastic actuator faults