Class Of Nonlinear Multi-agent Systems Research Articles

Semiglobal Periodic Event-Triggered Output Regulation for Nonlinear Multiagent Systems

This study focuses on periodic event-triggered (PET) cooperative output regulation problem for a class of nonlinear multiagent systems. The key feature of PET mechanism is that event-triggered conditions are required to be monitored only periodically. This approach is beneficial for Zeno behavior exclusion and saving of battery energy of onboard sensors. At first, new PET distributed observers are proposed to estimate the leader information. We show that the estimation error converges to zero exponentially with a known convergence rate under asynchronous PET communication. Second, a novel PET output feedback controller is designed for the underlying strict feedback nonlinear multiagent systems. Based on a state transformation technique and a local PET state observer, the cooperative semiglobal output regulation problem can be solved by the proposed new control design technique. Simulation results of multiple Lorenz systems illustrate that the developed control scheme is effective.

Adaptive constrained output feedback optimal consensus tracking for uncertain nonlinear multi-agent systems and its application

This paper addresses the output feedback optimal consensus tracking control problem for a class of nonlinear multi-agent systems (MASs) with unknown internal dynamics and input saturation. Firstly, an adaptive observer is designed to reconstruct the unmeasurable states using neural networks (NNs) based on the augmented dynamic model. Subsequently, a novel distributed feedforward controller is proposed using the backstepping method, where an auxiliary state is introduced to deal with the input saturation problem. Unlike the traditional recursive design technique, the procedures we take can reduce the consensus tracking control problem into the optimal regulation issue, which is then solved by the adaptive dynamic programming (ADP) method. Therefore, the designed consensus control protocol consists of a distributed feedforward controller and a distributed optimal feedback controller. Moreover, the stability of the MASs is guaranteed by the Lyapunov theory. Numerical simulation on multi-missile guidance problem demonstrates the effectiveness of the proposed control scheme.

Sampled‐data bipartite consensus control of nonlinear multi‐agent systems under denial‐of‐service attacks

SummaryThis article concentrates on the sampled‐data secure bipartite consensus problem for a class of nonlinear multiagent systems under intermittent denial‐of‐service attacks. The network communication channels are destroyed when the attacks occur, which causes all the system states to be unavailable. A novel distributed sampled‐data output feedback control protocol is developed by using the discontinuous sensor data. It is proved by the Lyapunov stability analysis that the considered nonlinear multiagent systems can achieve bipartite consensus exponentially in light of the designed control protocol. Then, the control algorithm is extended to systems with both state and input delay. Finally, a simulation example is presented to verify the effectiveness of the proposed control protocol.

ℋ∞ observer-based consensus for nonlinear multiagent systems with actuator saturation and input delays

In this paper, H ∞ observer-based consensus for a class of nonlinear multiagent systems (MASs) with actuator saturation and input delays are estimated. A new delay-dependent region partitioning approach has been introduced in multiagent systems. The state information of the leader can be obtained by designing a distributed observer for each follower. Based on the state information of the follower and the estimated state information of the neighbouring agents, a distributed controller has been developed. With the help of a novel Lyapunov–Krasovakii functional (LKF), using a region partitioning approach combined with Wirtinger inequalities, a sufficient condition is established in terms of Linear Matrix Inequality (LMI). Finally, simulation examples are presented to the applicability of the proposed theoretical results.

Fully Distributed Consensus of Switched Heterogeneous Nonlinear Multi-Agent Systems With Bouc-Wen Hysteresis Input

This paper studies the adaptive fully distributed consensus problem for a class of nonlinear multi-agent systems (MASs) whose structures are heterogeneous, where agents with first-order dynamics and second-order dynamics exist simultaneously. Moreover, each agent subjects to Bouc-Wen hysteresis input. Due to the changes of the internal and external environment, all agents are considered to possess multiple modes and display the switching characteristic. To achieve the consensus tracking control of the considered MAS, first, the totally unknown nonlinear functions representing the system uncertainties are approximated by utilizing radial basis function neural networks (RBFNNs). Then, an adaptive neural consensus control protocol, which can enable the consensus objective to be achieved for the MAS, is designed via using the fully distributed design scheme and adaptive neural control method. It is noted that the information on the number of agents and the interaction topology is not required during the protocol design. Based on the Lyapunov stability theory, the proposed consensus protocol can ensure all signals of the considered MAS is bounded. Finally, simulation results are presented to show the feasibility and effectiveness of the proposed consensus protocol.

Event-Triggered Adaptive Fault-Tolerant Control for a Class of Nonlinear Multiagent Systems With Sensor and Actuator Faults

This paper investigates the leader-following consensus control problem for a class of nonlinear multiagent systems subject to sensor and actuator faults under a fixed directed graph. First, a fault compensation mechanism is proposed because of multiple faults wherein the adaptive parameters substitute the fault coefficients. Then, the command filtering method is employed to avoid the burst of complexity rendered by the duplicative differentiation of the virtual control signal. Furthermore, the neural networks-based state observers are designed to reconstruct the unmeasurable states of the nonlinear multiagent systems. According to the given design approach, a switching threshold-based event-triggered adaptive fault-tolerant control strategy is developed and ensures all the signals in the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB). Finally, the simulation result is provided to demonstrate the validity of the presented method.

Model-Free Adaptive Iterative Learning Bipartite Containment Control for Multi-Agent Systems.

This paper studies the bipartite containment tracking problem for a class of nonlinear multi-agent systems (MASs), where the interactions among agents can be both cooperative or antagonistic. Firstly, by the dynamic linearization method, we propose a novel model-free adaptive iterative learning control (MFAILC) to solve the bipartite containment problem of MASs. The designed controller only relies on the input and output data of the agent without requiring the model information of MASs. Secondly, we give the convergence condition that the containment error asymptotically converges to zero. The result shows that the output states of all followers will converge to the convex hull formed by the output states of leaders and the symmetric output states of leaders. Finally, the simulation verifies the effectiveness of the proposed method.

Leader-Following Consensus for a Class of Nonlinear Multiagent Systems Under Event-Triggered and Edge-Event Triggered Mechanisms.

Considering that there are many systems with limited network bandwidth in practice, this article studies the leader-following consensus problem for a class of nonlinear multiagent systems (MASs). The purpose of this article is to reduce unnecessary information transmission between any pair of adjacent agents including the leader in the MASs through intermittent communication. The novel event-triggered and asynchronous edge-event triggered mechanisms are designed for the leader and all edges, respectively. The static and dynamic consensus protocols under these mechanisms are proposed to address the leader-following consensus problem for MASs with Lipschitz dynamics, and the systems will not exhibit Zeno behavior under these two control schemes. Note that the dynamic consensus protocol does not rely on any global values of MASs, it is a fully distributed way. Finally, a practice simulation example is introduced to illustrate the theoretical results obtained.

Distributed Adaptive Control for Nonlinear Heterogeneous Multi-agent Systems with Different Dimensions and Time Delay

A distributed neural network adaptive feedback control system is designed for a class of nonlinear multi-agent systems with time delay and nonidentical dimensions. In contrast to previous works on nonlinear heterogeneous multi-agent with the same dimension, particular features are proposed for each agent with different dimensions, and similar parameters are defined, which will be combined parameters of the controller. Second, a novel distributed control based on similarity parameters is proposed using linear matrix inequality (LMI) and Lyapunov stability theory, establishing that all signals in a closed loop system are eventually ultimately bounded. The consistency tracking error steadily decreases to a field with a small number of zeros. Finally, simulated examples with different time delays are utilized to test the effectiveness of the proposed control technique.

Finite-time [formula omitted] cluster consensus control for nonlinear multi-agent systems with aperiodically intermittent control

This paper investigates the finite-time H∞ cluster consensus for a class of nonlinear multi-agent systems with uncertainties and external disturbances. First, a new aperiodic intermittent controller is designed, which considers both intermittent control and cluster consensus control. Second, the problem of finite-time H∞ cluster control when the system encounters external disturbances is investigated. With the help of the Lyapunov stability criterion and linear matrix inequality analysis techniques(LMI), sufficient conditions for the system to achieve finite-time H∞ cluster consensus control under the action of intermittent control strategy are obtained. Finally, the feasibility of the theoretical analysis is verified by numerical simulation.

Fixed-time flocking formation of nonlinear multi-agent system with uncertain state perturbation

This paper aims to solve the control problem of fixed-time rigid formation for a class of nonlinear multi-agent systems with uncertain state perturbation and external disturbance. Firstly, in order to compensate the system uncertainty with uncertain dynamics and disturbances in fixed-time, an adaptive disturbance state observer independent of the initial state is therefore designed. Secondly, a fixed-time flocking control strategy is proposed by combining backstepping control method with fixed time control. It is strictly proved that the designed controller realises the fixed-time boundedness of the closed-loop error system independent of the initial state based on the rigidity theory and Lyapunov framework. Finally, the performance of the observer and controller is verified by numerical simulation, and the flocking formation control of a class of UAV group is realised on the simulation platform based on UE4.

Adaptive Fuzzy Control for Multi-Agent Systems With Unknown Measurement Sensitivity via a Simplified Backstepping Approach

This brief investigates the adaptive tracking control problem for a class of nonlinear multi-agent systems with unknown measurement sensitivity using a simplified backstepping approach. By introducing Nussbaum gain, a new approach is proposed that effectively relaxes the restrictive condition that the unknown measurement sensitivity be within a particular range. A simplified backstepping approach is then presented to alleviate the structural complexity and high computational costs. It is proven that the tracking error converges to a small neighborhood of the origin and that all of the signals are bounded. Finally, simulation analysis is conducted to verify the effectiveness of the proposed control strategy.

Distributed fault-tolerant containment control for a class of non-linear multi-agent systems via event-triggered mechanism

An issue of actuator faults in non-linear multi-agent systems with multiple leaders is addressed in this work. Through an event-triggered mechanism, a novel fault-tolerant containment control protocol is designed to eliminate the adverse effects of actuator faults on the systems, and the communication resources can be saved efficiently in the meantime. The self-regulation of the event-triggered mechanism is enhanced significantly by utilizing the adaptive updating strategies, and the proposed triggering thresholds are both time-dependent and state-dependent. A sufficient condition is derived to make the systems with aperiodic sampling stabilizable. Moreover, it is proved that Zeno behavior can be avoided in this article. A numerical simulation is also presented to exhaustively illustrate the effectiveness of the designed scheme.

Adaptive backstepping for distributed optimization

This paper presents an adaptive backstepping approach to distributed optimization for a class of nonlinear multi-agent systems with each agent represented by the parametric strict-feedback form. In particular, this paper does not assume known gradient functions of the local objective functions, and uses the measured gradient values depending on the agents’ real-time outputs instead. A stepwise method is presented to derive novel distributed adaptive optimization algorithms that steer the outputs of all the agents to the optimal solution of the total objective function. First, a novel distributed adaptive optimization algorithm is developed for first-order nonlinear uncertain multi-agent systems, supported by stability analysis and convergence proofs using Lyapunov arguments. Second, by means of Lyapunov arguments in the spirit of backstepping, a distributed adaptive optimization algorithm is presented for high-order strict-feedback systems with parametric uncertainty. Interesting extensions of the main result to practically important classes of systems with unknown virtual control coefficients, output feedback, and relative-measurement feedback are also discussed.

Inverse Optimal Control of a Class of Nonlinear Multi-Agent Systems With Applications to Ship Slew Rate Control

Abstract This paper deals with the inverse optimal leader-following consensus problem of a class of high-order nonlinear multi-agent systems in strict-feedback form under directed communication topology. By employing the backstepping methodology and introducing an integral type Lyapunov function, a nonlinear distributed control law is constructed using the relative information between neighboring agents. The proposed control law solves the inverse optimal leader-following consensus problem under directed communication graph that contains a spanning tree with the root node being the leader agent. A practical example of ship slew rate control system is given to verify the effectiveness of the theoretical results.

Adaptive consensus control for output-constrained nonlinear multi-agent systems with actuator faults

This paper addresses the problem of leader-follower consensus fault-tolerant control for a class of nonlinear multi-agent systems with output constraints. Specifically, a new nonlinear state transformation function is proposed to deal with the asymmetric constraint on output. Moreover, by integrating backstepping and radial basis function neural network approaches, an adaptive consensus control framework is developed with a single parameter estimator, which mitigates the computation of control algorithm in comparison with conventional adaptive approximation based control techniques. Then an adaptive compensation method is proposed to eliminate the effect of actuator failure. Under the proposed control scheme, all the closed-loop signals of the systems are bounded and the consensus tracking error converges to an adjustable small neighborhood of zero. To evaluate the developed control algorithm, a group of four networked two-stage chemical reactors is used to illustrate the effectiveness of the theoretic results obtained.

Predictor-Based Neural Dynamic Surface Control for Bipartite Tracking of a Class of Nonlinear Multiagent Systems.

This article is concerned with bipartite tracking for a class of nonlinear multiagent systems under a signed directed graph, where the followers are with unknown virtual control gains. In the predictor-based neural dynamic surface control (NDSC) framework, a bipartite tracking control strategy is proposed by the introduction of predictors and the minimal number of learning parameters (MNLPs) technology along with the graph theory. Different from the traditional NDSC, the predictor-based NDSC utilizes prediction errors to update the neural network for improving system transient performance. The MNLPs technology is employed to avoid the problem of "explosion of learning parameters". It is proved that all closed-loop signals steered by the proposed control strategy are bounded, and the system achieves bipartite consensus. Simulation results verify the efficiency and effectiveness of the strategy.

Bipartite consensus for a class of nonlinear multi-agent systems under switching topologies: A disturbance observer-based approach

This paper considers the leader-following bipartite consensus for a class of nonlinear multi-agent systems (MASs) subject to exogenous disturbances under directed fixed and switching topologies, respectively. Firstly, two new output feedback control protocols involving signs of link weights are introduced based on relative output measurements of neighboring agents. In order to estimate the disturbances produced by an exogenous system, a disturbance observer-based approach is developed. Then, sufficient conditions for leader-following bipartite consensus with directed fixed topologies are derived. Furthermore, by assuming that each switching topology contains a directed spanning tree, it is proved that the leader-following bipartite consensus can be realized with the designed output feedback control protocol if the dwell time is larger than a non-negative threshold. Finally, numerical simulations inspired by a real-world DC motors are provided to illustrate the effectiveness of the proposed controllers.

Point-to-point consensus tracking control for unknown nonlinear multi-agent systems using data-driven iterative learning

This paper considers the point-to-point consensus tracking control for a class of nonlinear multi-agent systems with completely unknown dynamics, where the consensus is concerned with some given desired points instead of the entire desired trajectory. It is assumed that the multi-agent system executes repetitive coordination tasks in a finite time interval and the iterative learning control is also utilized to design a consensus protocol with learning ability. To deal with the unknown nonlinear agent’s dynamic, the relationship between agent’s output at these given points and agent’s control input is first derived and then a data-based model referring to the agent’s dynamic is established by utilizing the iteration-domain dynamical linearization technique. Then, a data-driven iterative learning protocol is developed by optimizing two performance indexes, which contains a control input updated algorithm, a parameter estimation algorithm and a reset algorithm. The results show that the proposed design can achieve the point-to-point consensus tracking task only by using the I/O data of the agent. Finally, simulation examples are provided to verify the effectiveness of the proposed protocol.

Event‐triggered leader‐following consensus for a class of nonlinear multiagent systems with time‐varying delay

AbstractThis article studies the consensus problem for discrete‐time nonlinear multiagent systems with time delay. Based on an asynchronous event‐triggered mechanism where local information of a communication graph is involved, a distributed consensus protocol is proposed to reduce the network communication congestion for achieving state consensus for multiagent systems with nonlinear perturbations. To avoid complications involving data fusion, this distributed consensus protocol allows every agent to broadcast its state at its own triggering condition instead of using continuous updates and without continuous monitoring of the triggering condition. To save the energy consumption involved in a consensus protocol, the proposed event‐triggering guaranteed cost is investigated in this article for multiagent systems. We select an agent as a leader that is constrained by its neighbors and controlled by the proposed protocol as other agents. It has been theoretically demonstrated that the consensus problem of multiagent systems with nonlinear perturbation and time delays can be achieved if the control gains and the triggering parameters are appropriately selected. Sufficient conditions are given to guarantee that the agents achieve consensus. Moreover, the gain matrix and triggering parameters of the proposed protocol are cast into matrix inequalities conditions. A simulation example is given to illustrate the effectiveness of the theoretical results.