Adaptive event-triggered distributed model predictive control for tracking consensus of multiagent systems

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.

This article deals with the extended-dissipativity-based adaptive event-triggered (AET) control problem for stochastic polynomial fuzzy singular systems (SPFSSs). The purpose of this work is to design a state-feedback controller through the AET mechanism, which not only makes the closed-loop system admissible and extended dissipative, but also saves the network resources. First, with a suitable Lyapunov–Krasovskii functional and an integral inequality in stochastic setting, an AET control criterion of SPFSSs considering asynchronous premise is established to ensure the mean-square admissibility and extended dissipativity of the closed-loop singular system. Additionally, a simple lemma is employed to approximate the nonconvex sum-of-squares (SOS) conditions with the convex SOS conditions due to the difficulty in solving the nonconvex SOS design conditions. Then, both the desired feedback controller gain and the event-triggered weighting polynomial matrix are codesigned based on the derived criterion. Finally, simulation examples are provided to illustrate the effectiveness of the proposed approaches.

This article investigates adaptive control problems for unknown second-order nonlinear multiagent systems (MASs) via an event-triggered approach. An adaptive event-triggered consensus controller is given to second-order MAS with unknown nonlinear dynamics. We prove that the proposed consensus controller is free from Zeno behavior. Next, an adaptive event-triggered tracking controller is developed for leader-follower MAS with the leader having bounded nonzero control input. Both consensus and tracking controllers are fully distributed, which means that event-triggered controllers only use local cooperative information. Finally, an unknown second-order nonlinear MAS is used to verify the given event-triggered controllers.

Dynamic obstacle avoidance of unmanned ship based on event-triggered adaptive nonlinear model predictive control

This paper studies an adaptive event-triggered (AET) control method for sampled-data systems to enhance communication efficiency and conserve resources. Initially, a dynamic event-triggering mechanism is introduced to minimize redundant data transmissions while maintaining system performance. Then, a state feedback control law is designed by optimizing the event-triggered threshold condition, with the system's stability verified using a Lyapunov function. The gain parameters are computed through a cone complementarity linearization algorithm, ensuring computational efficiency and robustness. Additionally, the proposed approach addresses the trade-off between communication resource utilization and control performance by dynamically adjusting the triggering conditions based on system states. This ensures a reduction in redundant transmissions without compromising system reliability. Finally, this method provides a systematic and effective framework for improving communication efficiency, with potential applications in industrial automation and resource-constrained networked control systems.

In this paper, an adaptive event-triggered consensus control scheme is designed for nonlinear multi-agent systems suffering from actuator faults and false data injection (FDI) attacks. To counteract the corruption of input states caused by sensor-level FDI attacks, a novel coordinate transformation is proposed, enabling accurate state reconstruction. By modifying local control laws and adaptive laws, the negative effects of actuator faults are counteracted. Unknown time-varying attack weights are addressed using Nussbaum-type functions. In addition, an adaptive event-triggering mechanism based on coordinate transformation is established to reduce the communication frequency of the network. The validity of the proposed control protocol is evidenced by two simulation examples.

This paper investigates the consensus problem of linear multi-agent systems subject to external disturbances via distributed event-triggered adaptive control. First, a distributed event-triggered adaptive output feedback control strategy is proposed for each agent. It is shown that under this control strategy, the consensus problem can be solved for any connected undirected communication graph in a fully distributed manner without using any global information. Then a distributed self-triggered adaptive output feedback control strategy is designed with which continuous monitoring of the measurement error is no longer needed. It is further shown that for the proposed event-triggered and self-triggered control strategies, no agent will exhibit Zeno behavior. Finally, the effectiveness of the proposed two control strategies is illustrated on a group of two-mass-spring systems.

A novel compensation control method for heterogeneous multiagent systems under Denial-of-Service (DoS) attacks and transmission delays is investigated in this article. This control method has all the advantages of the cloud-based computation strategy, the adaptive event-triggered strategy, and the predictive control scheme. The adaptive event-triggering mechanism can adjust the event numbers adaptively, the predictive control can reduce or eliminate the negative effects brought out by both DoS attacks and transmission delays actively, while the cloud-based computation strategy can eliminate the negative effects completely as the same as there are no DoS attacks and transmission delays. Through the interval decomposition skill and the augmented system modeling method, the compensated geschlossenes system model is established. Moreover, the joint design for the feedback gain matrices and the event-triggered parameters is implemented. In the simulation part, five VTOL aircraft are used to demonstrate the theoretical results.

Distributed model predictive control for linear systems under communication noise: Algorithm, theory and implementation

Path tracking control is crucial for autonomous ground vehicles (AGVs), but it faces challenges from model nonlinearity, parameter variations, and external disturbances. This article proposes a novel model-free path tracking controller that achieves appointed-time performance and alleviates the communication burden. Initially, the path tracking model is abstracted as an unknown nonlinear system, bypassing the complexities of time-varying nonlinearity. A proximate appointed-time prescribed performance function (PPF) is then proposed, whose settling time can be preset arbitrarily using a nonpiecewise function with less conservatism. Subsequently, by utilizing an error scaling function to manage the entry capture problem, a dual-level constrained prescribed performance control (PPC) is developed to ensure proximate appointed-time stability of the preview error and its derivative regardless of initial constraints. Furthermore, an adaptive event-triggered mechanism, inspired by the encoder-decoder concept, transmits only two-bit signals aperiodically, substantially decreasing communication bandwidth requirements. Experimental results confirm the effectiveness, robustness, and efficiency of the proposed scheme.

Aiming at the existence of bounded disturbance and data loss, a comprehensive algorithm of adaptive event-triggered model predictive control based on state observer and adaptive event-triggered scheme is proposed in this paper. Firstly, the mathematical model of nonlinear network control system is established by using interval Type-2 Takagi-Sugeno (T-S) fuzzy model. Then, the system observer is established to predict the state of the system, and the adaptive event-triggered scheme is addressed to avoid wasting network resources; A model predictive controller is proposed by optimizing the infinite horizon objective function which considers data loss and bounded disturbance via the S-procedure and LMI techniques. Finally, simulation results verify the effectiveness of the proposed control algorithm.

In this article, the leader-following consensus problem via the event-triggered control technique is studied for the nonlinear strict-feedback systems with unmeasurable states. The follower's nonlinear dynamics is approximated using the fuzzy-logic systems, and the fuzzy weights are updated in a nonperiodic manner. By introducing a fuzzy state observer to reconstruct the system states, an observer-based event-triggered adaptive fuzzy control and a novel event-triggered condition are designed, simultaneously. In addition, the nonzero positive lower bound on interevent intervals is presented to avoid the Zeno behavior. It is proved via an extension of the Lyapunov approach that ultimately bounded control is achieved for the leader-following consensus of the considered multiagent systems. One remarkable advantage of the proposed control protocol is that the control law and fuzzy weights are updated only when the event-triggered condition is violated, which can greatly decrease the data transmission and communication resource. The simulation results are provided to show the effectiveness of the proposed control strategy and the theoretical analysis.

Vehicle platoon, as an important application of connected automated vehicles (CAVs), is a hot topic in the intelligent transportation systems (ITSs). Most existing results on platoon control used the exact feedback linearization method to transform nonlinear vehicle dynamics into linear ones, which required complete prior knowledge of parameters. As the measurement errors and the environment disturbance exist, the accurate parameters are difficult to obtain in real world. This paper considers a nonlinear uncertain vehicle platoon under a network topology (NT) modeled by a spanning tree in a digraph. Radial basis function neural networks (RBFNNs) are used to approximate the uncertain function and an adaptive distributed controller is designed by backstepping control method. To reduce the update rate of the controllers, an event-triggered control scheme is proposed with two different kinds of triggering conditions. It is proven that vehicle platoon practical consensus can be achieved under the Zeno-free adaptive event-triggered control scheme. Moreover, for bidirectional (BD) NT and two predecessor-following (TPF) NT, vehicle platoon reaches the string stability. Finally, a vehicle platoon under three different kinds of NTs is simulated to demonstrate the effectiveness of the theoretical results.

Networked control systems with probabilistic time-varying delay based on event-triggered non-fragile [formula omitted] control

SummaryThis article studies the adaptive model predictive control with extended state observers (ESO) to deal with multiple unmanned aerial vehicles formation flight in presence of external disturbances and system uncertainties. Specifically, to deal with the mismatch of predictive model caused by external disturbances and system uncertainties, ESOs are introduced to estimate the lumped disturbances, where the ultimately bounded property of observer system can be guaranteed by using the Lyapunov stability theorem. With these observations, the distributed adaptive model predictive controller is designed to achieve trajectory tracking and disturbance rejection simultaneously for multiple unmanned aerial vehicles, as well as taking the state and input saturation into account. Moreover, the stability of proposed model predictive controller is analyzed. Finally, the simulation examples are provided to illustrate the validity of the proposed control scheme.