Uncertain Nonlinear Multi-agent Systems Research Articles

Event-based adaptive formation and tracking control with predetermined performance for nonlinear multi-agent systems

The article focuses on the event-based predetermined performance formation tracking control for uncertain nonlinear multi-agent systems. Each agent is subject to actuator saturation constraint and full-state constraints. To meet the full-state constraints, a barrier function-based nonlinear mapping method is employed instead of the barrier Lyapunov function, such that the undesirable “feasibility conditions” are eradicated. To overcome actuator saturation, the auxiliary systems are utilized to let the controller be designed under the framework of backstepping. The approximation capacity of RBF NNs and adaptive methods are adopted, which can solve unknown uncertainties. An event-triggered predetermined performance formation tracking control strategy is designed, which can guarantee that the agents form the prescribed formation shape in a predetermined settling time and have a fine transient and steady-state tracking performance, while reducing the communication burden from the controller to the actuator. At last, an example of unmanned aerial vehicles is given to test the availability of the results.

Funnel-Based Cooperative Output Regulation Control for Uncertain Nonlinear Multiagent Systems Under Input Saturation

Funnel-Based Cooperative Output Regulation Control for Uncertain Nonlinear Multiagent Systems Under Input Saturation

Fully distributed dynamic event-triggered reliable COR of nonlinear MASs with communication link faults and actuator faults for directed graphs

The issue of fault-tolerant cooperative output regulation (COR) for nonlinear uncertain multiagent systems (MASs) under communication link faults (LFs) and actuator faults is investigated in this paper. This is the first attempt to study the fault-tolerant COR problem for the aforementioned MASs under directed graphs. To handle unknown LFs and avoid using global information, adaptive event-triggered (ET) observers are designed to estimate the exosystem state. That is, resilience against communication LFs is translated to distributed observer design. To overcome the “explosion of complexity” and obtain asymptotically stable results, dynamic surface control (DSC) techniques are introduced. Unlike existing linear DSC techniques that generate boundary layer errors, this paper introduces nonlinear filters with compensation terms into dynamic surfaces to eliminate boundary layer errors. Adaptive control laws that can achieve asymptotically regulated output are then developed based on the improved DSC techniques. It is shown that the developed control strategy can achieve fault-tolerant COR for the considered MASs. Additionally, the implemented dynamic event-triggered mechanism (ETM) ensures strictly positive minimum event-triggered intervals (ETIs) and avoids Zeno behavior. Finally, a practical simulation example reveals the effectiveness of the suggested scheme.

Iterative learning resilient consensus of uncertain nonlinear multi-agent systems vulnerable to false data injection attacks

This work investigates the iterative learning resilient consensus of uncertain nonlinear high-order multi-agent systems (MASs) against false data injection (FDI) attacks. First, in order to reduce the impact of FDI attacks on the nonlinear MASs, a novel coordinate transformation technique is proposed, which is composed of the states after being attacked, and the Nussbaum gain technique is adopted to address the problem of unknown attack gains resulting from FDI attacks. Then, by employing compromised state variables, a fuzzy adaptive iterative learning resilient control method is presented based on the composite energy function, where unknown nonlinear functions are handled using fuzzy logic systems. The presented approach can guarantee that the outputs of all followers precisely track the leader in a limited time interval while ensuring that all closed-loop signals are bounded. Finally, a numerical simulation is provided to demonstrate the efficacy of the designed control strategy.

Adaptive fixed-time fuzzy containment control for uncertain nonlinear multiagent systems with unmeasurable states

This paper addresses the adaptive fixed-time fuzzy containment control for uncertain nonlinear multiagent systems, where the states and nonlinear functions are not feasible for the controller design. To address the issue of unmeasurable states, a state observer is developed, and fuzzy logic systems are utilized to approximate unknown nonlinear functions. Under the framework of fixed-time Lyapunov function theory and cooperative control, an adaptive fixed-time fuzzy containment control protocol is derived via the adaptive backstepping and adding one power integrator method. The derived fixed-time containment controller can confirm that the closed-loop systems are practical fixed-time stable, which implies that all signals in the systems are bounded and all follower agents can converge to the convex hull formed by the leader agents within fixed-time in the presence of unmeasurable states and unknown nonlinear functions . Simulation examples are conducted to test the validity of the present control algorithm.

Adaptive fixed-time neural consensus control for a class of uncertain nonlinear multi-agent systems with full state constraints

This paper is concerned with the fixed-time consensus control problem for non-strict feedback multi-agent systems with asymmetric output constraints and full state constraints. Considering the feasibility of controlling execution, a novel practical virtual control signal is developed utilizing both saturation function and hyperbolic tangent function to ensure that this signal can remain within the same restricted range as the corresponding state variable throughout entire operation process. In backstepping steps, the design of ideal virtual control signal also adopts a different form of piecewise function than before, introducing high-order polynomial functions to avoid singularity problems in the derivation process. In addition, function approximation ability of radial basis function neural networks technique is applied to estimate uncertainties derived from the system functions and controller design procedure. Moreover, universal barrier Lyapunov function approach is improved for constructing an adaptive constrained synchronization control scheme. By fixed-time stability theory, it is shown that the tracking errors of the MAS converge to an adjustable region around the origin in a fixed time and the state variables always obey their constraints. And the upper bound of the settling time is merely dependent on design parameters, which is not affected by the initial states of MAS. The effectiveness of the proposed control strategy is shown by a numerical simulation example at last. Two scenarios are provided to demonstrate the advantages of the control protocol proposed in this paper.

Fast Finite-Time Observer-Based Event-Triggered Consensus Control for Uncertain Nonlinear Multiagent Systems with Full-State Constraints.

This article studies a class of uncertain nonlinear multiagent systems (MASs) with state restrictions. RBFNNs, or radial basis function neural networks, are utilized to estimate the uncertainty of the system. To approximate the unknown states and disturbances, the state observer and disturbance observer are proposed to resolve those issues. Moreover, a fast finite-time consensus control technique is suggested in order to accomplish fast finite-time stability without going against the full-state requirements. It is demonstrated that every signal could be stable and boundless, and an event-triggered controller is considered for the saving of resources. Ultimately, the simulated example demonstrates the validity of the developed approach.

Multiple dynamic targets enclosing control for a class of nonlinear uncertain multiagent systems

The multiple dynamic targets enclosing control protocol is proposed in this paper for a class of nonlinear uncertain multiagent systems. To accomplish the mission of hunting multiple targets with double-integral nonlinear dynamics, a distributed estimator is established firstly for each agent by employing the signum function, leading to an estimation of the average position of multiple targets. Specifically, only partial information about the dynamic targets is utilized to construct the enclosing formation reference beacon. Furthermore, due to the existence of the system uncertainties, it is a challenging task to design the enclosing controller. To overcome this difficulty, an augmented system is constructed to compensate for the effect caused by the uncertainties, which is introduced afterwards into the control protocol design along with comprehensive theoretical analysis. Sufficient conditions are derived and proved under Lyapunov stability criterion to ensure the closed-loop stability of nonlinear multiagent systems. The effectiveness of the proposed control scheme is finally verified through the numerical simulations. More precisely, by appropriately picking control parameters α and β based on Lemma 3.1, the designed formation reference beacon estimator is validated by achieving convergence of errors between the actual state and estimated states to zero. Meanwhile, the feasibility of the proposed enclosing control protocol is demonstrated simultaneously as it enables the regulation of enclosing errors using a self-designed parameter γ0. Additionally, the sensitivity analysis of the control parameters is presented, which indicates that the proposed reference beacon estimator remains robust to the variation of its control parameters, while larger values for the control parameters in the designed controller result in faster error convergence rates.

Adaptive consensus control of uncertain nonlinear multi-agent systems with communication delays and relative output information

This paper explores the challenges of distributed adaptive consensus control problems in nonlinear multi-agent systems with unknown parameters and communication delays. To address these issues, a novel backstepping-based adaptive state feedback control protocol is proposed by utilizing a reference dynamic system. Furthermore, an output-feedback consensus control algorithm incorporating a state observer is introduced. Through the application of Laplace transformation, matrix properties, and graph theory, it is demonstrated that the closed-loop multi-agent system ensures the boundedness of all signals and achieves output consensus, even in the presence of nonuniform communication delays. Numerical simulations on a group of inverted pendulums illustrate the effectiveness of these algorithms.

Distributed Adaptive Formation Tracking for a Class of Uncertain Nonlinear Multiagent Systems: Guaranteed Connectivity Under Moving Obstacles.

This article explores a guaranteed network connectivity problem during moving obstacle avoidance within a distributed formation tracking framework for uncertain nonlinear multiagent systems with range constraints. We investigate this problem based on a new adaptive distributed design using nonlinear errors and auxiliary signals. Within the detection range, each agent regards other agents and static or dynamic objects as obstacles. The nonlinear error variables for formation tracking and collision avoidance are presented, and the auxiliary signals in formation tracking errors are introduced to maintain network connectivity under the avoidance mechanism. The adaptive formation controllers using command-filtered backstepping are constructed to ensure closed-loop stability with collision avoidance and preserved connectivity. Compared with the previous formation results, the resulting features are as follows: 1) the nonlinear error function for the avoidance mechanism is considered an error variable, and an adaptive tuning mechanism for estimating the dynamic obstacle velocity is derived in a Lyapunov-based control design procedure; 2) network connectivity during dynamic obstacle avoidance is preserved by constructing the auxiliary signals; and 3) owing to neural networks-based compensating variables, the bounding conditions of time derivatives of virtual controllers are not required in the stability analysis.

Fuzzy Fixed-Time Event-Triggered Consensus Control for Uncertain Nonlinear Multiagent Systems With Memory-Based Learning

Fuzzy Fixed-Time Event-Triggered Consensus Control for Uncertain Nonlinear Multiagent Systems With Memory-Based Learning

Event-triggered adaptive tracking for constrained multi-agent systems with saturated inputs and actuator faults

An fault-tolerant tracking control of uncertain nonlinear multi-agent systems with state constraints and input saturation is investigated. A saturated controller is processed by converting the saturation function into a linear form of the control input. The uncertain nonlinear dynamics are approximated by fuzzy logic systems and the unknown parameters are estimated by adaptive laws. By utilizing the nonlinear state-dependent function, the constrained system is converted into the unconstrained one. An adaptive distributed event-triggered control mechanism for consensus tracking is developed utilizing the backstepping approach. By stability analysis, it is ensured that all system signals are bounded, consensus tracking can be achieved with bounded errors, and no Zeno behavior happens. Finally, simulation is done to validate the theoretical results.

A new control method for leader–follower consensus problem of uncertain constrained nonlinear multi-agent systems

The design of Adaptive Fuzzy Sliding-Mode Control (AFSMC) is discussed in this study for the distributed leader–follower consensus problem of nonlinear uncertain multi-agent systems with input and state constraints. Avoiding the critical circumstances related to the violation of input and state constraints is an important issue in the design of consensus control systems. The limitations of existing adaptive consensus controllers developed for full-state constrained systems motivated the present novel method without restrictive structural assumptions. To convert the original problem to an unconstrained consensus control problem, the proposed control system uses a barrier function-based state transformation method and the input-state linearization methodology. As a result, each agent’s adaptive fuzzy sliding-mode control input, which consists of a fuzzy system and a robust term, is constructed based on the derived representation of the system dynamics. The fuzzy system is used to approximate the dynamic equations of the agent, its neighbors, and maybe the leader in each local feedback control law structure, and the robust term is designed to compensate for the fuzzy approximation error. The magnitudes of the robust terms and the output vectors of the fuzzy systems are also determined using the proposed adaptation laws. The second Lyapunov theorem and Barbalat’s lemma are used to verify the closed-loop system’s asymptotic stability. The effectuality of the proposed methodology is confirmed by numerical simulations for several Autonomous Underwater Vehicles (AUVs). Simulation results show that by means of the proposed AFSMC, the leader–follower consensus is achieved without state constraint violation and performance degradation.

Event-Triggered Distributed Adaptive Leaderless Consensus of Uncertain Heterogenous Nonlinear Multi-Agent Systems

Event-Triggered Distributed Adaptive Leaderless Consensus of Uncertain Heterogenous Nonlinear Multi-Agent Systems

Time-varying formation of uncertain nonlinear multi-agent systems via adaptive feedback control approach with event-triggered impulsive estimator

The time-varying formation problem under directed topology is investigated for the uncertain nonlinear multi-agent systems (MASs) via the neural networks-based adaptive feedback control (NNAFC). Compared with the simple adaptive control, the designed control protocol consists of two parts: NNAFC and the event-triggered impulsive estimator. To save communication costs, communications among followers are only carried out at discrete instants determined by the event-triggered impulsive rule. Two kinds of estimators and controllers are designed for two cases with or without communication delay, respectively. Further, NNAFC can be further improved to eliminate errors due to uncertain function approximation for some special cases. Utilizing the Lyapunov stability theory and impulsive control theory, we obtain some sufficient conditions to implement time-varying formation task. Finally, three examples are given to show the feasibility of theoretical analysis.

Adaptive Fuzzy Distributed Optimization for Uncertain Nonlinear Multiagent Systems

Adaptive Fuzzy Distributed Optimization for Uncertain Nonlinear Multiagent Systems

Adaptive Output Synchronization for a Class of Uncertain Nonlinear Multi-Agent Systems Over Switching Networks

Adaptive Output Synchronization for a Class of Uncertain Nonlinear Multi-Agent Systems Over Switching Networks

Nonsingular adaptive finite-time consensus control for uncertain nonlinear multi-agent systems with input quantization

This paperinvestigates the distributed consensus control for nonlinear multi-agent systems with input quantization in a directed communication topology. A nonsingular adaptive finite-time control (NAFTC) scheme is proposed by combining the filtered backstepping method with neural network control. Both chattering and singularity problems of the control signals are avoided thanks to its [Formula: see text] continuity, so that the contradiction between discontinuous- and [Formula: see text] continuous finite-time control with input quantization does not exist in our scheme. At the same time, a novel inequality for the input-output relationship of hysteresis quantizer is derived, which can simply handle the input quantization without requiring additional stability analysis. What is more, some common assumptions, such as Lipschitz assumption and Holder-continuity assumption, are not required in our scheme. Finally, stability analysis and numerical simulation are provided.

Low-Complexity Control With Funnel Performance for Uncertain Nonlinear Multiagent Systems

Low-Complexity Control With Funnel Performance for Uncertain Nonlinear Multiagent Systems

Adaptive Fixed-Time Optimal Formation Control for Uncertain Nonlinear Multiagent Systems Using Reinforcement Learning

Adaptive Fixed-Time Optimal Formation Control for Uncertain Nonlinear Multiagent Systems Using Reinforcement Learning