Search

This article deals with the fully distributed pull-based event-triggered bipartite fixed-time output consensus problem of heterogeneous linear multiagent systems (HLMASs) with an active leader, whose information can be merely accessed by a small fraction of followers. First, a class of fully distributed fixed-time observers is proposed for each follower to estimate the leader's system matrices, position, and control input under the signed communication topology, respectively. Then, based on the estimations of leader's system matrices, two adaptive algorithms are given to solve the regulator equations. Furthermore, the fully distributed fixed-time observer-based controllers associated with state feedback and output feedback are, respectively, proposed by employing the pull-based event-triggered mechanism (ETM) where each agent merely updates controller at its own triggering instants. Correspondingly, some sufficient criteria and the rigorous proofs are provided to ensure the implementation of bipartite output consensus in fixed time by using the Lyapunov stability theory and fixed-time stability theory. Moreover, the strictly positive lower bounds of intervals between two adjacent event-triggered times are derived, which means the Zeno behavior is ruled out. Finally, numerical simulations are performed to demonstrate the theoretical analysis.

International audience

Autonomous vehicle technology has developed at an unprecedented rate in recent years. An increasing number of vehicles are equipped with different levels of driving assist systems to reduce the human driver’s burden. However, because of the conservative design of its programming framework, there is still a large gap between the performance of current autonomous driving systems and experienced veteran drivers. This gap can cause drivers to distrust decisions or behaviors made by autonomous vehicles, thus affecting the effectiveness of drivers’ use of auto-driving systems. To further estimate the expected acceptance of autonomous driving systems in real human–machine co-driving situations, a characterization model of driver confidence has to be constructed. This paper conducts a survey of driver confidence in riding autonomous vehicles. Based on the analysis of results, the paper proposes a confidence quantification model called “the Virtual Confidence (VC)” by quantifying three main factors affecting driver confidence in autonomous vehicles, including (1) the intrusive movements of surrounding traffic participants, (2) the abnormal behavior of the ego vehicle, and (3) the complexity of the driving environment. The model culminates in a dynamic confidence bar with values ranging from 0 to 100 to represent the levels of confidence. The validation of the confidence model was verified by doing comparisons between the real-time output of the VC and the real-time feeling of human drivers on an autonomous vehicle simulator. The proposed VC model can potentially identify features that need improvement for auto-driving systems in unmanned tests and provide data reference.

In this article, the containment control problem of multiagent systems (MASs) with active leaders under a directed communication graph is addressed. A novel pull-based event-triggered protocol combined with aperiodic sampled-data mechanism is first presented, where each agent merely updates controller at its own triggering instants. Under the proposed control protocols, the information is only exchanged and calculated at the aperiodic sampling instants, therefore, it can reduce communication congestion and save computing resources. Furthermore, the Zeno behavior is naturally excluded because the trigger instants are in the set of aperiodic sampled instants. By virtue of the algebraic graph theory and Lyapunov stability analysis, it is shown that the active leaders can reach their desired states predefined arbitrarily and all followers are driven to the convex hull formed by the leaders. Finally, an illustrative example is given to validate the theoretical results and emphasize the advantages of the proposed protocols.

Accurate state of charge (SOC) is crucial to achieving safe, reliable, and efficient use of batteries. This article proposes an adaptive neural network (NN)-based event-triggered observer to estimate SOC. First, a stochastic battery equivalent circuit model (ECM) is established, where an adaptive NN is employed to approximate the unknown nonlinear part. The learning process of network weight is conducted online to observe the variations of model parameters and avoid time-consuming processes for parameter extraction. Besides, for the purpose of saving computational cost, an event-triggered mechanism (ETM) is employed in the weight updating law, which means the weights only update when it is necessary. Then, an adaptive radial basis function (RBF) NN-based SOC observer is designed, and its stability is proven by the Lyapunov theory. Moreover, the strictly positive lower bound of interevent time is derived, and undesirable Zeno behavior can be excluded. Finally, the accuracy and robustness of the proposed observer are evaluated by experiments and simulations. Results show that the proposed method can estimate SOC accurately in the presence of initial deviation and sensor noises.

In this study, polyaluminum sulfate (PAS) coagulant was selected to evaluate the coagulation performance of polystyrene microplastics. Overall, polystyrene removal efficiency was 90.4 % at the optimal dosage of 7.5 g/L of PAS. In addition to the type of coagulants (e.g. polyaluminum chloride, iron(III) chloride, and polyferric sulfate), surface characteristics such as densities, particle sizes, morphologies, adsorbed substances, and functional groups can also significantly impact the coagulation performance. The coagulation ratios are reduced to (2.6 ± 0.1)% when the densities of microplastics decrease. Aging treatments involving NaOH, H

The fully distributed consensus tracking of stochastic nonlinear multiagent systems (MASs) is investigated with Markovian switching topologies and intermittent control strategy, where the dynamics of agents are depicted by Ito differential equations and the leader's information is just known for a fraction of followers. The switching mechanism of interaction topologies is modeled as a Markov process. A novel class of fully distributed control protocols is proposed via intermittent control method, which is only associated with the relative state measurements of neighbors and does not involve any global information. Meanwhile, the control gains are designed to be intermittently adaptive, which can effectively reduce energy consumption and avoid the gains being larger than those needed in practice. Several sufficient conditions and corresponding proofs are provided by using the Lyapunov stability theory. Finally, numerical simulation is presented to state the feasibility of the theoretical results.