Observer-Based Dynamic Event-Triggered Control for Multiagent Systems With Time-Varying Delay

Adaptive Prescribed-Time Event-Triggered Control of Nonlinear Networked Systems Under Dynamic Quantization

This article addresses the issue of adaptive event-triggered and quantized control for a category of uncertain nonlinear systems, utilizing a prescribed-time (PT) control framework. We begin by introducing a dynamic event-triggering mechanism and a dynamic event-driven quantizer to develop a discrete control framework, without assuming the constraint of input-to-state stability (ISS). The aperiodic discrete control method can effectively improve the data transmission efficiency of the networked control system. Then, according to the adaptive parameter estimation, a novel PT event-triggered adaptive controller and a PT sampled and quantized adaptive controller are proposed. Compared with the backstepping control method, the designed "one-step-controller" decreases the computational loads of the virtual controllers. Moreover, the global PT stability of the nonlinear system is assured, and the Zeno phenomenon of the event-triggered sampling does not happen. Finally, the practicability and availability of the designed control method are validated via a numerical system and a manipulator system.

Uniform-performance-constrained fixed-time neuro-control for stochastic nonlinear systems under dynamic event triggering

This article studies the implementation of practical fixed-time control in stochastic nonlinear systems, implementing event-triggered communication between the controller and the actuator. Firstly, to accomplish the problem of uniform tracking error performance constraints, the improved performance function is investigated, which is combined with the asymmetric barrier Lyapunov function to achieve fast convergence speed and steady state accuracy. Secondly, the practical fixed-time stability is applied in the stochastic nonlinear closed-loop system, which fuses fixed-time command filtering and improved filtering error compensation mechanisms to avoid computational explosion issue. Furthermore, in order to relieve the communication load on the controller and actuator, adjustable trigger thresholds are designed, based on which dynamic event triggering mechanisms are presented for stochastic nonlinear systems. Additionally, the uncertain system behavior is estimated using RBF neuro-networks and the designed controller avoids the singularity problem. Finally, the proposed controller verifies that the system error converges to zero in a fixed time under the Lyapunov stability theory, and that the system output is within the preset boundaries, realizing boundedness of all signals. The superiority of the control method is further demonstrated by three simulation studies including two practical examples.

Multiple Mittag-Leffler Stability of Almost Periodic Solutions for Fractional-Order Delayed Neural Networks: Distributed Optimization Approach

This article proposes new theoretical results on the multiple Mittag-Leffler stability of almost periodic solutions (APOs) for fractional-order delayed neural networks (FDNNs) with nonlinear and nonmonotonic activation functions. Profited from the superior geometrical construction of activation function, the considered FDNNs have multiple APOs with local Mittag-Leffler stability under given algebraic inequality conditions. To solve the algebraic inequality conditions, especially in high-dimensional cases, a distributed optimization (DOP) model and a corresponding neurodynamic solving approach are employed. The conclusions in this article generalize the multiple stability of integer- or fractional-order NNs. Besides, the consideration of the DOP approach can ameliorate the excessive consumption of computational resources when utilizing the LMI toolbox to deal with high-dimensional complex NNs. Finally, a simulation example is presented to confirm the accuracy of the theoretical conclusions obtained, and an experimental example of associative memories is shown.

Fuzzy Adaptive Event-Triggered Consensus Control for Nonlinear Multiagent Systems Under Jointly Connected Switching Networks

This article studies the fuzzy adaptive event-triggered (ET) consensus control issue of nonlinear multiagent systems (NMASs) under jointly connected switching networks. Since the leader and its high-order derivatives are unknown under jointly connected switching networks, a novel distributed ET reference generator equipped with an ET mechanism is constructed to estimate them. Meanwhile, the continuous information transmission among agents is avoided and the network channel utilization is optimized. Subsequently, fuzzy logic systems (FLSs) are employed to approximate unknown dynamics, and a fuzzy adaptive ET consensus control algorithm only using intermittent communication is designed by backstepping control methodology. It is demonstrated that all the closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB), with the tracking errors converging to a small neighborhood around zero. Finally, we apply the developed fuzzy adaptive ET consensus control algorithm to unmanned surface vehicles (USVs), and the simulation results verify the effectiveness of the proposed ET consensus control algorithm.

Distributed State Observer for Systems with Multiple Sensors under Time-Delay Information Exchange

The issues of state estimations based on distributed observers for linear time-invariant (LTI) systems with multiple sensors are discussed in this paper. We deal with the scenario when the information exchange has known time delays, and aim at designing a distributed observer for each subsystem such that each distributed observer can estimate the system state asymptotically by rejecting the time delay. To begin with, by rewriting the target system in a connecting form, a subsystem which is affected by the time-delay states of other nodes is established. And then, for this subsystem, a distributed observer with time delay is constructed. Moreover, an equivalent state transformation is made for the observer error dynamic system based on the observable canonic decomposition theorem. Further, in order to ensure that the distributed observer error dynamic system is asymptotically stable even if there exists a time delay, a linear matrix inequality (LMI) which is relative to the Laplace matrix is elaborately set up, and a special Lyapunov function candidate based on the LMI is considered. Next, based on the Lyapunov function and Lyapunov stability theory, we prove that the error dynamic system of the distributed observer is asymptotically stable, and the observer gain is determined by a feasible solution of the LMI. Finally, a simulation example is given to illustrate the effectiveness of the proposed method.

Observer-based HOFA predictive cooperative control for networked multi-agent systems under time-variant communication constraints

This research focuses on a cooperative control problem for networked multi-agent systems (NMASs) under time-variant communication constraints (containing time-variant communication delays and time-variant data losses) in the forward and feedback channels. From the perspective of high-order fully actuated (HOFA) system theory, a HOFA system model is adopted to describe the NMAS, which is called the networked HOFA multi-agent system (NHOFAMAS). Because of complicated working scenarios over the network, the states of NMASs are immeasurable and the communication constraints are always present, such that an observer-based HOFA predictive control (OB-HOFAPC) method is designed to implement the cooperative control when existing the immeasurable states and time-variant communication constraints. In this method, a HOFA observer is established to estimate the immeasurable states for constructing a consensus control protocol. Then, an incremental prediction model (IPM) in a HOFA form is developed via a Diophantine equation to take the place of a reduced-order prediction model. Through this IPM, multi-step output ahead predictions are derived to optimize the cooperative control performance and compensate for time-variant communication constraints in real-time. The depth discussion gives a sufficient and necessary criterion to analyze the simultaneous consensus and stability for closed-loop NHOFAMASs. The capability and advantage of OB-HOFAPC method are illustrated via numerical simulation and experimental verification on a cooperative flying-around task of three air-bearing spacecraft simulators.

Data-based bipartite formation control for multi-agent systems with communication constraints

This article investigates data-driven distributed bipartite formation issues for discrete-time multi-agent systems with communication constraints. We propose a quantized data-driven distributed bipartite formation control approach based on the plant's quantized and saturated information. Moreover, compared with existing results, we consider both the fixed and switching topologies of multi-agent systems with the cooperative-competitive interactions. We establish a time-varying linear data model for each agent by utilizing the dynamic linearization method. Then, using the incomplete input and output data of each agent and its neighbors, we construct the proposed quantized data-driven distributed bipartite formation control scheme without employing any dynamics information of multi-agent systems. We strictly prove the convergence of the proposed algorithm, where the proposed approach can ensure that the bipartite formation tracking errors converge to the origin, even though the communication topology of multi-agent systems is time-varying switching. Finally, simulation and hardware tests demonstrate the effectiveness of the proposed scheme.

Synergetic learning for unknown nonlinear H∞ control using neural networks

The well-known H∞ control design gives robustness to a controller by rejecting perturbations from the external environment, which is difficult to do for completely unknown affine nonlinear systems. Accordingly, the immediate objective of this paper is to develop an on-line real-time synergetic learning algorithm, so that a data-driven H∞ controller can be received. By converting the H∞ control problem into a two-player zero-sum game, a model-free Hamilton-Jacobi-Isaacs equation (MF-HJIE) is first derived using off-policy reinforcement learning, followed by a proof of equivalence between the MF-HJIE and the conventional HJIE. Next, by applying the temporal difference to the MF-HJIE, a synergetic evolutionary rule with experience replay is designed to learn the optimal value function, the optimal control, and the worst perturbation, that can be performed on-line and in real-time along the system state trajectory. It is proven that the synergistic learning system constructed by the system plant and the evolutionary rule is uniformly ultimately bounded. Finally, simulation results on an F16 aircraft system and a nonlinear system back up the tractability of the proposed method.

Exponential Consensus of Multi-Agent Systems under Event-Triggered Impulsive Control with Actuation Delays

This paper investigates the exponential consensus problem for a class of nonlinear leader-following multi-agent systems using impulsive control, where impulses are generated by the event-triggered mechanism and are subjected to actuation delays. It is proved that Zeno behavior can be avoided, and by employing the linear matrix inequality technique, some sufficient conditions for realizing exponential consensus of the considered system are derived. Actuation delay is an important factor affecting the consensus of the system, and our results show that increasing the actuation delay can enlarge the lower bound of the triggering interval, while it harms the consensus. To demonstrate the validity of the obtained results, a numerical example is provided.