Unknown Nonlinear Multi-agent Systems Research Articles

Model-free adaptive consensus design for a class of unknown heterogeneous nonlinear multi-agent systems with packet dropouts

This paper studies the consensus problem for a class of unknown heterogeneous nonlinear multi-agent systems via a network with random packet dropouts. Based on the dynamic linearization technique, novel model-free adaptive consensus protocols with the data compensation mechanism are designed for both leaderless and leader-following cases. The advantage of this approach is that only neighborhood input and output data of the agents are required in the protocol design. For the stability analysis, a new Squeeze Theorem based method is developed to derive the theoretic results instead of the traditional contraction mapping principle used in model-free adaptive control. It is shown that the consensus can be achieved for both leaderless and leader-following cases if the communication topology is strongly connected. Finally, numerical simulations verifying the correctness of the theoretical results are given.

Model-free adaptive bipartite consensus control for unknown heterogeneous nonlinear MASs with different input delays

In this paper, a novel model-free adaptive bipartite consensus control scheme is proposed for unknown heterogeneous nonlinear multi-agent systems (MASs) with different input delays. Firstly, an equivalent data model is established for each agent by using the dynamic linearisation method. Secondly, based on an established equivalent data model and constructed predictive compensation algorithm, the distributed model-free adaptive bipartite consensus control scheme (DMFABCC) is designed by only utilising the measured input/output data rather than the state-space model of MASs. Next, by selecting an appropriate Lyapunov function, the convergence of bipartite tracking error is rigorously proved. Finally, numerical simulations are conducted to demonstrate the effectiveness of the proposed DMFABCC scheme for achieving the bipartite consensus target.

Quantized Iterative Learning Bipartite Containment Tracking Control for Unknown Nonlinear Multi-agent Systems

This paper proposes a quantized model-free adaptive iterative learning control (MFAILC) algorithm to solve the bipartite containment tracking problem of unknown nonlinear multi-agent systems, where the interactions between agents include cooperation and antagonistic interactions. To design the controller, the agent’s dynamics is transformed into the linear data model based on the dynamic linearization method, and then a quantized MFAILC algorithm is established based on the quantized values of the relative output measurements. The designed controller only depends on the input and output data of the agent. We prove that under the quantized MFAILC algorithm, the multi-agent systems can achieve the bipartite containment, that is, the output trajectories of followers converge to the convex hull formed by the leaders’ trajectories and the leaders’ symmetric trajectories. Finally, we provide simulations to illustrate the effectiveness of our theoretical results.

Human-in-The-Loop Fuzzy Iterative Learning Control of Consensus for Unknown Mixed-Order Nonlinear Multi-Agent Systems

Human-in-The-Loop Fuzzy Iterative Learning Control of Consensus for Unknown Mixed-Order Nonlinear Multi-Agent Systems

Event-Triggered Distributed Data-Driven Iterative Learning Bipartite Formation Control for Unknown Nonlinear Multiagent Systems.

In this study, we investigate the event-triggering time-varying trajectory bipartite formation tracking problem for a class of unknown nonaffine nonlinear discrete-time multiagent systems (MASs). We first obtain an equivalent linear data model with a dynamic parameter of each agent by employing the pseudo-partial-derivative technique. Then, we propose an event-triggered distributed model-free adaptive iterative learning bipartite formation control scheme by using the input/output data of MASs without employing either the plant structure or any knowledge of the dynamics. To improve the flexibility and network communication resource utilization, we construct an observer-based event-triggering mechanism with a dead-zone operator. Furthermore, we rigorously prove the convergence of the proposed algorithm, where each agent's time-varying trajectory bipartite formation tracking error is reduced to a small range around zero. Finally, four simulation studies further validate the designed control approach's effectiveness, demonstrating that the proposed scheme is also suitable for the homogeneous MASs to achieve time-varying trajectory bipartite formation tracking.

Global Bipartite Exact Consensus of Unknown Nonlinear Multi-Agent Systems With Switching Topologies: Iterative Learning Approach

Global Bipartite Exact Consensus of Unknown Nonlinear Multi-Agent Systems With Switching Topologies: Iterative Learning Approach

Adaptive Neural Consensus of Unknown Non-Linear Multi-Agent Systems with Communication Noises under Markov Switching Topologies

In this paper, the adaptive consensus problem of unknown non-linear multi-agent systems (MAs) with communication noises under Markov switching topologies is studied. Based on the adaptive control theory, a novel distributed control protocol for non-linear multi-agent systems is designed. It consists of the local interfered relative information and the estimation of the unknown dynamic. The Radial Basis Function networks (RBFNNs) approximate the nonlinear dynamic, and the estimated weight matrix is updated by utilizing the measurable state information. Then, using the stochastic Lyapunov analysis method, conditions for attaining consensus are derived on the consensus gain and the weight of RBFNNs. The main findings of this paper are as follows: the consensus control of multi-agent systems under more complicated and practical circumstances, including unknown nonlinear dynamic, Markov switching topologies and communication noises, is discussed; the nonlinear dynamic is approximated based on the RBFNNs and the local interfered relative information; the consensus gain k must to be small to guarantee the consensus performance; and the proposed algorithm is validated by the numerical simulations finally.

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

This paper studies the point-to-point consensus tracking problem for a class of nonlinear non-affine multi-agent systems, where the tracking target is a series of some given ideal output points rather than a complete ideal trajectory. For the unknown nonlinear dynamics of every agent, the relationship between its control input and the corresponding output signal at these given points is acquired, then a data-driven model is constructed based on adjacent-agent dynamic linearization technology. Furthermore, by optimizing two performance index functions, we design a novel point-to-point iterative learning tracking control scheme, which not only ensures the consensus tracking results of all agents only utilizing the I/O data of every agent at the given ideal output points, but also makes all agents reach consensus behavior at the rest of the time instants. The effectiveness of our algorithm is confirmed through rigorous theoretical analysis and two experimental simulations.

Predefined-Time Adaptive Fault-Tolerant Control for Switched Odd-Rational-Power Multi-Agent Systems

This paper studies the distributed adaptive neural network (NN) predefined-time (PT) fault-tolerant consensus tracking control for unknown non-strict feedback nonlinear multi-agent systems (MASs) with time-varying full state constraints (TVFSCs), arbitrary switching behavior functions (possibly asynchronous), actuator faults and high powers. Compared with existing results, the convergence time estimation bound in this paper is less conservative due to the upper bound of convergence time presents as a tuning parameter. This paper avoids the control gain of each virtual controller to appear in the next virtual controller by using separable function technique, so it can reduce the control action complexity. Besides, this paper does not use any filters or piecewise continuous function in the controller design process, which can effectively avoid the potential singularity problem. An adaptive NN fault-tolerant consensus tracking controller is designed via backstepping technique and tangent barrier Lyapunov functions (BLF-Tans), which can ensure that all the closed-loop signals are bounded within a PT and the TVFSC bounds are not violated. Finally, numerical simulation and practical example further verify the effectiveness of the presented control algorithm. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Many practical (underactuated, weakly coupled and unstable mechanical systems) control systems can be modeled as uncertain high-order MAS with high powers. Due to the influence of environment and other factors, the values of some variables in the practical systems are limited and cannot be arbitrarily selected. In addition, the actual systems may fail due to human factors and in most of the relevant literatures, it is often assumed that the powers are equal to 1 and only achieve the controlled systems asymptotic stability as time approaches infinity. Therefore, in this paper, an adaptive PT fault-tolerant control scheme is developed for MASs with high powers, time-varying full state constraints and actuator faults. The effectiveness of the control scheme is illustrated based on simulation study.

Fixed-time adaptive tracking control of two moving targets for a class of non-linear multi-agent systems

In this research paper, the fixed-time adaptive tracking of two moving targets for a class of unknown non-linear multi-agent systems based on a distributed competition approach is studied. Each agent simultaneously participates in two competitions based on the k-Winner Take All (k-WTA) approach to obtain its allocation status. Using a mean value estimator, the agent can calculate its allocation status only by its neighbor's information. Since an agent can only track one target, an extended tracking error is introduced, where if the agent wins one of the competitions, it will track the assigned target, and if the agent wins both competitions, by selecting one of the targets, track the selected target. Also, the agent which is not win any competitions, return to a specified point. Then, using neural networks and adaptive techniques, an adaptive fixed-time controller is introduced for the agents to track the target based on the allocation status or to return to the specified point. Analysis of the stability of the closed-loop system through the Lyapunov theorem is investigated. Finally, a simulation study to show the efficiency of the theoretical results is presented.

Adaptive Fuzzy Control for Unknown Nonlinear Multiagent Systems With Switching Directed Communication Topologies

Addressing for the consensus control of multi-agent system (MAS) under the conditions that the directed communication topologies are switching and system nonlinearities are completely unknown, a global consensus control with fully distributed manner is proposed combining with fuzzy logic systems (FLS). FLS are used to approximate the unknown disturbances to enhance system robustness. To deal with the switching topologies of MAS, a reconstructing mechanism using a novel piecewise differentiable function is firstly proposed for the state and consensus errors of agents, which renders the state and consensus errors zero at each switching time instant and facilitates to the control design based on barrier functions, against the unexpected controller action at switching time instant. Incorporating with this reconstructing mechanism, a novel distributed controllers is designed, which is featured with low-complexity structure, fully distributed manner and adaptively reconstructing ability. These technical attempts contribute to some new results. Firstly, the global consensus of MAS with switching directed topologies and unknown nonlinearities is firstly achieved. Secondly, the consensus errors can be explicitly regulated to a prespecified arbitrary small residual set in the sense that the range of the set is the predefined functions given by designer. Finally, simulation results demonstrate the claims.

Data‐driven consistent control with data compensation for a class of unknown nonlinear multiagent systems with constraints

AbstractTo solve the problem of longitudinal cooperative formation driving control of multiple vehicles, a novel model‐free adaptive control algorithm with data compensation under constraint conditions (COM‐cMFAC) is proposed in this manuscript. In the COM‐cMFAC algorithm, the pseudo partial derivative (PPD), which is a time‐varying parameter, is used to linearize the nonlinear dynamics of the multivehicle cooperative system by using dynamic linearization technology. Then, a COM‐cMFAC controller is designed. For the case of data packet loss, the proposed controller uses a data compensation mechanism that estimates and compensates for the lost data through the data collected at the previous moment to perform packet loss control. Additionally, the controller considers the constrained input and output problems that will occur in the actual control process and imposes input and output constraints. The main advantage of the COM‐cMFAC algorithm is that the entire control process only needs the input and output data of the multivehicle cooperative system, and it also has a good control effect in the case of packet loss. The stability of the proposed method is verified through strict mathematical analysis, and its effectiveness is verified by semiphysical experiments based on a MATLAB/Simulink and Carsim platform connection environment.

A novel actor–critic–identifier architecture for nonlinear multiagent systems with gradient descent method

The infinite-horizon optimal consensus control problem for continuous-time unknown nonlinear multiagent systems (MASs) is solved via an online adaptive reinforcement learning approach. Three different neural network (NN) structures are proposed as part of a novel actor–critic–identifier (ACI) to approximate the Hamilton–Jacobi–Bellman (HJB) equations. The actor and critic NNs approximate the optimal controls and optimal value functions, respectively, and identifier NN approximates the unknown system model. The ACI architecture is simpler than most known architectures, which has the benefit of allowing actor, critic, and identifier learning to occur concurrently and continuously without the system model. It is important to note that the tuning laws for the three different NNs are designed using gradient descent method. Adaptive control techniques based on Lyapunov method are used to examine the algorithm’s convergence. To verify the viability of the proposed approach, we offer a simulated example.

Distributed global adaptive bipartite consensus of multi-agent systems with signed communication topology structure

This paper focuses on designing a fully distributed adaptive fuzzy bipartite consensus control protocol for unknown nonlinear multi-agent systems with a class of undirected signed graph. Utilizing the fuzzy logical system universal approximator as a compensator to describe the uncertain nonlinear dynamics, a fully distributed adaptive fuzzy control protocol is proposed by means of re-parameterized approach and bounding technique, the global bipartite consensus of leader-following multi-agent systems without using any dynamics of the leader is proven via constructing a new Lyapunov function. The proposed protocol overcomes the drawback of the semi-global consensus in existing literature. Furthermore, as an extension of the former result, a bipartite formation control problem is also solved. Finally, the proposed methods are validated by simulation examples.

Data-Driven Bipartite Consensus Tracking for Nonlinear Multiagent Systems With Prescribed Performance

In this article, a data-driven control strategy with prescribed performance is proposed to address the distributed bipartite consensus problem for unknown nonlinear multiagent systems (MASs) undersigned directed graphs. The dynamics of the agents in each topology are completely unknown and the desired trajectory information is only communicated to a subset of agents. First, the unknown nonlinear dynamic of each agent is transformed into an equivalent time-varying linearized model by utilizing the dynamic linearization method. Then, by considering the prescribed performance, a strictly increasing function is given to transform the constrained tracking error condition of the nonlinear MASs into an equivalent unconstrained error condition. Meanwhile, a new control protocol that only uses the input and output data of the agents is designed in the case of fixed or switched topologies. The proposed method can not only guarantee the bipartite consensus but also ensure the bipartite tracking error always converges to the prescribed region. Moreover, the designed control parameters can be adjusted appropriately to obtain better tracking performance. Finally, the convergence of bipartite consensus tracking error is guaranteed through rigorous theoretical analysis. The control scheme is further verified by two examples.

Cooperative Learning Control of Unknown Nonlinear Multi-agent Systems with Time-varying Output Constraints Using Neural Networks and Barrier Lyapunov Functions via Backstepping Design

Cooperative Learning Control of Unknown Nonlinear Multi-agent Systems with Time-varying Output Constraints Using Neural Networks and Barrier Lyapunov Functions via Backstepping Design

Distributed impulsive control consensus for a class of unknown nonlinear multi-agent systems based on event-triggered scheme

In this study, we are concerned with the impulsive consensus control problem for a class of nonlinear multi-agent systems (MASs) which have unknown dynamics and directed communication topology. The neural networks (NNs) method is the first utilized to construct distributed event-triggered impulsive consensus protocol. In contrast to the existing impulsive consensus protocol, the consensus protocol proposed in this paper does not need the dynamics of agents, which enhances the system robustness, and realizes distributed event-triggered communication between agents, which can reduce unnecessary consumption of communication resources. Sufficient conditions are derived to ensure the consensus of the controlled MASs and the exclusion of Zeno-behavior. Finally, simulation examples are presented to illustrate the effectiveness of the proposed control protocol.

Integral Reinforcement-Learning-Based Optimal Containment Control for Partially Unknown Nonlinear Multiagent Systems.

This paper focuses on the optimal containment control problem for the nonlinear multiagent systems with partially unknown dynamics via an integral reinforcement learning algorithm. By employing integral reinforcement learning, the requirement of the drift dynamics is relaxed. The integral reinforcement learning method is proved to be equivalent to the model-based policy iteration, which guarantees the convergence of the proposed control algorithm. For each follower, the Hamilton-Jacobi-Bellman equation is solved by a single critic neural network with a modified updating law which guarantees the weight error dynamic to be asymptotically stable. Through using input-output data, the approximate optimal containment control protocol of each follower is obtained by applying the critic neural network. The closed-loop containment error system is guaranteed to be stable under the proposed optimal containment control scheme. Simulation results demonstrate the effectiveness of the presented control scheme.

Neural network‐based event‐triggered cluster quasi‐consensus for unknown multiagent systems with directed topology

AbstractThis paper studies cluster quasi‐consensus problem for a class of unknown nonlinear multiagent systems (MASs) with directed communication topology. First, a distributed continuous neural network (NN)‐based adaptive protocol is presented for solving this problem by introducing reference model to each agent. Then, taking limited communication resource and energy consumption into account, a distributed event‐triggered cluster quasi‐consensus protocol is proposed. Different from the existing results, two event‐triggered mechanisms are constructed in the proposed event‐triggered protocol to reduce communication load and control update frequency as possible. The sufficient conditions that guarantee cluster quasi‐consensus under the both proposed protocols are obtained, respectively. Zeno behavior is proved to be excluded. Finally, simulation results verify the effectiveness of the proposed protocols.

Model-free adaptive iterative learning containment control for unknown heterogeneous nonlinear MASs with disturbances

A novel model-free adaptive iterative learning control scheme is proposed for the containment control problem of unknown heterogeneous nonlinear multi-agent systems (MASs) with bounded measurable and unmeasurable disturbances. In details, the proposed scheme includes following parts. First, the agent dynamics are transformed into a partial form dynamic linearization data model along the iteration axis by using the novel concept of the pseudo gradient. Second, the distributed containment control scheme is designed for MASs under fixed topology based on the obtained data model at each working point. Then, the scheme is extended to the case of iteration-switching topologies. Finally, the convergences have been proved by rigorous mathematical analysis. The whole containment control process is characterized with data-driven nature by only using the input and output data. Simulation results illustrate the effectiveness of the schemes.