This paper investigates the output containment tracking problem in general heterogeneous multi-agent systems facing prescribed performance and intermittent communication. In this case, a novel non-periodic intermittent control framework is introduced to facilitate the intricate nature of the complex network to achieve the containment objective. First, an intermittent communication network is built by introducing a novel intermittent interval condition combining average dwell-time and extreme value theories into the directed graph. Second, a distributed non-periodic intermittent containment control strategy is designed, utilizing an internal system and a modified containment control approach. Subsequently, a distributed prescribed performance hybrid controller is developed to achieve output containment tracking. Additionally, sufficient conditions for the exponential stability are obtained based on the non-periodic intermittent and prescribed performance control methods. This criterion adopts the characterization of the average time interval. The effectiveness of the designed hybrid control strategy is verified by the simulation example, showcasing its advantage to solve the challenges in intermittent communication and prescribed performance.

In this article, a novel general clustered network framework with hybrid communication is constructed for multiple uncertain Euler-Lagrange (EL) systems. The objective is to ensure that the systems under consideration achieve containment tracking in the predefined path at the prescribed time. First, in the light of the hierarchical control design, the output tracking problem is decomposed into the desired signals tracking and the stability of the nonlinear uncertain coupled systems. Second, the wide-area network is described by a combination of a directed graph and an intermittent control scheme, then all agents are divided into different subnetworks in demand or scenarios. Based on this, a distributed prescribed-time hybrid observer under a time-varying scaling function and a novel containment error method is designed to achieve the containment tracking. In addition, an adaptive distributed prescribed-time hybrid control strategy is proposed for the uncertainty estimation. Then, the prescribed-time stability of uncertain EL systems is analyzed and guaranteed using the general Lyapunov theory and intermittent control method. Finally, the proposed hybrid control strategy is verified by the simulation results of multiple flexible manipulator systems.

This paper addresses the problem of prescribed-time output feedback stabilization for a class of nonlinear uncertain systems with both external disturbances and mismatched uncertainties. By utilizing a non-separation principle design approach, a novel active disturbance rejection control (ADRC) scheme is proposed. Firstly, a prescribed-time extended state observer (PTESO) is designed to simultaneously estimate unmeasured system states and external disturbances. Secondly, a feedforward-feedback composite controller is developed by integrating the PTESO with a constructive backstepping procedure. Furthermore, the prescribed-time stability of the entire closed-loop system is rigorously analyzed by using a Lyapunov function method. Finally, numerical simulations validate the effectiveness of the proposed control method.

This study investigates the resilient output containment problem in heterogeneous multiagent systems that facing intermittent communication and sensor attacks, specifically focusing deception attacks within wide-area networks (WANs). A novel WAN framework is introduced to effectively analyze the intricate nature of the network. The proposed framework facilitates the examination of connected agents across multiple scenarios to achieve the desired containment objective. The framework involves three key steps. First, a distributed hybrid control strategy is developed, utilizing an internal model to handle intermittent communication between clusters and continuous communication within clusters. Second, considering the unknown states of compromised heterogeneous agents, a Luenberger observer is devised for each agent, employing adaptive observers to estimate the state and external attack information. Subsequently, a distributed hybrid controller is proposed to achieve global output containment, ensuring uniform ultimate boundedness. Additionally, a sufficient condition for exponential stability is derived to tackle the intermittent control problem. This criterion employs the characterization of average intermittent intervals. The effectiveness of the proposed adaptive hybrid control strategy is demonstrated through simulation examples, showcasing its ability to address the challenges posed by intermittent communication and deception attacks.

This article investigates the fixed-time distributed average tracking (DAT) problem for nonlinear multiagent systems with unity relative degree under external disturbances. A distributed control framework is developed to guarantee fixed-time convergence of all agents' outputs to the target trajectory, which is defined as the average of multiple nonlinear reference signals. The approach consists of three main components. First, a steady-state generator is introduced to reconstruct the desired trajectory. Using this generator, a distributed observer is designed to estimate the target trajectory while ensuring robustness against initialization errors. Subsequently, an observer-based output-feedback controller is developed to guarantee the convergence of each agent's output to its corresponding reference signal within a fixed time. Through rigorous theoretical analysis, it is proved that the proposed control architecture ensures fixed-time convergence to the target trajectory, effectively solving the fixed-time DAT problem. The effectiveness of the proposed method is validated through numerical simulations.

Inspired by the empirical decision-making (EDM) phenomenon, wherein agents assimilate the opinion learned from social neighbors as their cognitive inertia and progressively rely on their own cognitive inertia sequence (CIS) for decision-making over time, we propose a novel CIS model paradigm and extend it based on the bounded confidence rule. In the extended CIS model, before agents obtain their acquired opinions, they will reconstruct the weight coefficients by evaluating the credibility of the opinions of social neighbors based on a comprehensive trust degree, composed of the opinion similarity and the centrality degree. Then, agents update their opinions through weighted aggregation of their CISs and acquired opinions. Finally, we apply the proposal to the Zachary?s karate club network, providing a comparison analysis between the extended CIS model and the HK model. Simulation results indicate that the number of opinion clusters increases as the trust threshold increases, and the extended CIS model has a shorter convergence time than the HK model, illustrating the effectiveness of the proposed model.

This paper introduces the liberating effect under group pressure into the Hegselmann-Krause (HK) model and proposes a novel expressed and private opinion dynamics model. Agents in the group hide their honest opinions because of the group pressure, and each agent has a private and expressed opinion. The liberating effect is divided into two stages. In the first stage, one agent in the group is the first to liberate when the number of times it feels pressure exceeds a specific limit and the cumulative pressure is the maximum. The liberating agent will express its opinion authentically, with private opinion consistent with the expressed opinion. In the second stage, the other agents in the group are influenced by the liberating neighbors and also liberate until the group evolution reaches a stable state. Through simulations, we study the effects of confidence level and pressure threshold on group opinion evolution. The experimental results show that both confidence level and pressure threshold are critical. All agents liberate when they are smaller than the critical value; when they are greater than the critical value, the liberating effect disappears. We also find that the liberating effect can accelerate the group opinion evolution.

In this paper, a fixed-time tracking control problem is investigated for an uncertain high-order nonlinear pure-feedback systems with practical state constraints. To this end, a new nonlinear transformation function with lower change rate at the state constraint boundary is first proposed, which can not only handle both constrained and unconstrained states in a unified way, but also reduce the control magnitude at the constraint boundary. With the help of the proposed transformation function, the original system is transformed to a new system without state constraints. Then, a non-singular fixed-time adaptive tracking controller is designed by applying an adding a power integrator technique and an adaptive neural network method. It is shown that the practical fixed-time stability can be guaranteed for the closed-loop system under the proposed tracking controller. Finally, two numerical examples are presented to demonstrate the proposed fixed-time tracking control strategy.

In this paper, an event-triggered optimal tracking control of discrete-time multi-agent systems is addressed by using reinforcement learning. In contrast to traditional reinforcement learning-based methods for optimal coordination and control of multi-agent systems with a time-triggered control mechanism, an event-triggered mechanism is proposed to update the controller only when the designed events are triggered, which reduces the computational burden and transmission load. The stability analysis of the closed-loop multi-agent systems with event-triggered controller is described. Further, to implement the proposed scheme, an actor-critic neural network learning structure is proposed to approximate performance indices and to on-line learn the event-triggered optimal control. During the training process, event-triggered weight tuning law has been designed, wherein the weight parameters of the actor neural networks are adjusted only during triggering instances compared with traditional methods with fixed updating periods. Further, a convergence analysis of the actor-critic neural network is provided via Lyapunov method. Finally, two simulation examples show the effectiveness and performance of the obtained event-triggered reinforcement learning controller.

In this article, we consider an adaptive optimal output regulation problem for general linear systems. The purpose of the optimal output regulation problem is to guarantee the stability of the closed-loop system and disturbance rejection, as well by minimizing some predefined performance indices. It can be realized by using an optimal controller, in which both optimal feedback control gain and optimal feedforward control gain are included. First, an adaptive dynamic programming (ADP) technique is used to solve the optimal feedback control gain. Next, the unknown system matrices of the plant are explicitly computed. In addition, based on the property of the minimal polynomial, the coefficient of the exogenous disturbance in the expression of the regulated output can also be calculated. Finally, according to the regulator equation, an extra cost function is given, which aims to obtain the optimal feedforward control gain. The linear vector space optimization methods are used to solve the optimal problem. As a result, the linear optimal output regulation problem can be solved by the approximately optimal feedback and feedforward control gains.

In this article, a novel reinforcement learning (RL) method is developed to solve the optimal tracking control problem of unknown nonlinear multiagent systems (MASs). Different from the representative RL-based optimal control algorithms, an internal reinforce Q-learning (IrQ-L) method is proposed, in which an internal reinforce reward (IRR) function is introduced for each agent to improve its capability of receiving more long-term information from the local environment. In the IrQL designs, a Q-function is defined on the basis of IRR function and an iterative IrQL algorithm is developed to learn optimally distributed control scheme, followed by the rigorous convergence and stability analysis. Furthermore, a distributed online learning framework, namely, reinforce-critic-actor neural networks, is established in the implementation of the proposed approach, which is aimed at estimating the IRR function, the Q-function, and the optimal control scheme, respectively. The implemented procedure is designed in a data-driven way without needing knowledge of the system dynamics. Finally, simulations and comparison results with the classical method are given to demonstrate the effectiveness of the proposed tracking control method.

In this study, a finite-time velocity-free rendezvous control method is considered for multiple autonomous underwater vehicle (AUV) systems with intermittent undirected communication. First, we develop a distributed finite-time observer for each AUV to estimate its own state information. Second, we design a rendezvous control algorithm that utilizes the estimated state information intermittently through a communication network in the absence of velocity measurement. A homogeneous method is used to prove that all AUVs in the group can achieve rendezvous in finite time for a network with intermittent communication, even without velocity measurements. The proposed method is shown to reduce the communication load of the system. More importantly, the control algorithm achieves the control goal of the system and is proven to be viable for many practical applications of multiple AUV systems from both economic and security perspectives. Finally, the effectiveness of the proposed control protocol is demonstrated via numerical simulations.

This article investigates an output antisynchronization problem of multiagent systems by using an input-output data-based reinforcement learning approach. Till now, most of the existing results on antisynchronization problems required full-state information and exact system dynamics in the controller design, which is always invalid in practical scenarios. To address this issue, a new system representation is constructed by using just the available input/output data from the multiagent system. Then, a novel value iteration algorithm is proposed to compute the optimal control laws for the agents; moreover, a convergence analysis is presented for the proposed algorithm. In the implementation of the data-based controllers, an actor-critic network structure is established to learn the optimal control laws without the requirement of information of the agent dynamics. An incremental weight updating rule is proposed to improve the learning performance. Finally, simulation results are presented to demonstrate the effectiveness of the proposed antisynchronization control strategy.

Lower limb exoskeleton (LLE) has received considerable interests in strength augmentation, rehabilitation and walking assistance scenarios. For walking assistance, the LLE is expected to have the capability of controlling the affected leg to track the unaffected leg’s motion naturally. An important issue in this scenario is that the exoskeleton system needs to deal with unpredictable disturbance from the patient, which requires the controller of exoskeleton system to have the ability to adapt to different wearers. This paper proposes a novel Data-Driven Reinforcement Learning (DDRL) control strategy to adapt different hemiplegic patients with unpredictable disturbances. In the proposed DDRL strategy, the interaction between two lower limbs of LLE and the legs of hemiplegic patient are modeled in the context of leader-follower framework. The walking assistance control problem is transformed into a optimal control problem. Then, a policy iteration (PI) algorithm is introduced to learn optimal controller. To achieve online adaptation control for different patients, based on PI algorithm, an Actor-Critic Neural Network (ACNN) technology of the reinforcement learning (RL) is employed in the proposed DDRL. We conduct experiments both on a simulation environment and a real LLE system. Experimental results demonstrate that the proposed control strategy has strong robustness against disturbances and adaptability to different pilots.

This article considers a two-way multiple-input multiple-output (MIMO) Rayleigh block fading wiretap channel with two full-duplex nodes (Alice and Bob) exchanging messages simultaneously and wiretapped by a $ {N}_{ {e}}$ -antennas eavesdropper (Eve). The channel state remains unchanged over a coherence interval T and is unknown to all terminals, including Alice, Bob, and Eve, at the beginning of the coherence interval. We first show the expression of the optimal signal waveform for Alice and Bob, which maximizes the sum secrecy rate, should be the products of independent random diagonal matrices and isotropically distributed unitary matrices. Then, conditioned on large T and small $ {N}_{ {e}}$ , the upper-bound for the secure degree of freedom (s.d.o.f.) of the two-way wiretap channel is derived, and a constant-norm-signaling (CNS) scheme is presented to achieve this theoretical bound. Finally, we formulate an optimization problem of the s.d.o.f. achieved by the CNS scheme for general cases with arbitrary T and $ {N}_{ {e}}$ , and solve this problem by exploring the monotonicity of the achievable s.d.o.f.. Our result shows that the optimal s.d.o.f. can be independent of coherence time T and becomes the product of the numbers of legitimate nodes's transmitter antennas (NLNTA) for large $ {N}_{ {e}}$ and small NLNTA.

In this paper, a bipartite consensus problem is considered for a high-order multiagent system with unknown disturbances and cooperative-competitive interactions. Two control strategies are proposed to guarantee bipartite consensus for two cases with and without an exogenous system (called leader for simplicity), respectively. Linearly parameterized approaches are applied to describe the time-varying unknown disturbances. Distributed adaptive laws are then designed for the unknown parameters in the disturbances. Adaptive consensus controllers are designed in a fully distributed fashion, which does not rely on any global information. Simulation results are presented to demonstrate the formation of bipartite consensus under the proposed adaptive control strategies.

Lower exoskeleton has gained considerable interests in walking assistance applications for both paraplegia and hemiplegia patients. In walking assistance of hemiplegia patients, the exoskeleton should have the ability to control the affected leg to follow the unaffected leg's motion naturally. One critical issue of walking assistance for hemiplegia patients is how to adapt the controller of both lower limbs with different patients. This paper presents a novel learning-based walking assistance control strategy for lower exoskeleton with hemiplegia patients. In the proposed control strategy, we modeled the control system of lower exoskeleton with hemiplegia patient as a Leader-Follower Multi-Agent System (LF -MAS). In order to adapt different patients with different conditions, reinforcement learning framework is utilized to adapt controllers online. In reinforcement learning framework with LF-MAS, we employed a Policy Iteration Adaptive Dynamic Programming (PI-ADP) algorithm, which aims to achieve better tracking control performance for lower exoskeleton with hemiplegia patient. We demonstrate the efficiency of proposed learning-based walking assistance control strategy in an exoskeleton system with healthy subjects who simulate hemiplegia patients. Experimental results indicate that the proposed control strategy can adapt different pilots with good tracking performance.

In this paper, an antisynchronization problem is considered for an array of linearly coupled reaction-diffusion neural networks with cooperative-competitive interactions and time-varying coupling delays. The interaction topology among the neural nodes is modeled by a multilayer signed graph. The state evolution of a neuron in each layer of the coupled neural network is described by a reaction-diffusion equation (RDE) with Dirichlet boundary conditions. Then, the collective dynamics of the multilayer neural network are modeled by coupled RDEs with both spatial diffusion coupling and state coupling. An edge-based adaptive antisynchronization strategy is proposed for each neural node to achieve antisynchronization by using only local information of neighboring nodes. Furthermore, when the activation functions of the neural nodes are unknown, a linearly parameterized adaptive antisynchronization strategy is also proposed. The convergence of the antisynchronization errors of the nodes is analyzed by using a Lyapunov-Krasovskii functional method and a structural balance condition. Finally, some numerical simulations are presented to demonstrate the effectiveness of the proposed antisynchronization strategies.

In this paper, a bipartite tracking problem is considered for a group of autonomous agents on a coopetition network, which is modeled by a signed graph. The leader and the followers are subjected to unknown disturbances, which are represented by linearly parameterized models. The agent dynamics is described by a double integrator. The relative position measurements and the relative velocity measurements are utilized as the new state variables to yield a new distributed system. An adaptive tracking control for each follower is designed by virtue of the two relative measurements. Moreover, the convergence of the bipartite tracking error and the parameter estimation are analyzed even when no more global information about the bounds of the unknown disturbances is available to all the followers.

In this paper, a distributed tracking control is proposed for a second-order multi-agent system with an active leader, based on a decentralized event-triggered scheduling strategy and a novel distributed velocity estimation technique. The acceleration of the leader is assumed to be time-varying and partially unknown. By applying dynamic tracking control with an event-triggered strategy to the system, the ultimate boundedness stability of the tracking error is ensured by Lyapunov stability theory. Some numerical simulation results are given to demonstrate the effectiveness of the new approach.