Semiglobal Consensus of a Class of Heterogeneous Multi-Agent Systems With Saturation

Fully Distributed Event-Triggered Anti-Windup Consensus of Heterogeneous Systems With Input Saturation and an Active Leader

This article addresses the event-based fully distributed consensus problem for linear heterogeneous multiagent systems (MASs) subject to input saturation. A leader with unknown but bounded control input is also considered. Based on an adaptive dynamic event-triggered protocol, all the agents can reach output consensus without knowing any global knowledge. Moreover, by applying a multiple-level saturation technique, the input-constrained leader-following consensus control is achieved. The given event-triggered algorithm can be utilized for the directed graph containing a spanning tree with the leader as the root. One distinct feature compared with previous works is that the proposed protocol can achieve saturated control without any a priori condition, instead, the local information is needed. Finally, the numerical simulations are illustrated to verify the performance of the proposed protocol.

Consensus of Stochastic Dynamical Multiagent Systems in Directed Networks via PI Protocols

With the rapid development of swarm intelligence, the consensus of multiagent systems (MASs) has attracted substantial attention due to its broad range of applications in the practical world. Inspired by the considerable gap between control theory and engineering practices, this article is aimed at addressing the mean square consensus problems for stochastic dynamical nonlinear MASs in directed networks by designing proportional-integral (PI) protocols. In light of the general algebraic connectivity, consensus underlying PI protocols for a directed strongly connected network is investigated, and due to the M -matrix approaches, consensus with PI protocols for a directed network containing a spanning tree is studied. By constructing appropriate Lyapunov functions, combining with the stochastic analysis technique and LaSalle's invariant principles, some sufficient conditions are derived under which the stochastic dynamical MASs realize consensus in mean square. Numerical simulations are finally presented to illustrate the validity of the main results.

Learning From Architectural Redundancy: Enhanced Deep Supervision in Deep Multipath Encoder-Decoder Networks

Deep encoder-decoders are the model of choice for pixel-level estimation due to their redundant deep architectures. Yet they still suffer from the vanishing supervision information issue that affects convergence because of their overly deep architectures. In this work, we propose and theoretically derive an enhanced deep supervision (EDS) method which improves on conventional deep supervision (DS) by incorporating variance minimization into the optimization. A new structure variance loss is introduced to build a bridge between deep encoder-decoders and variance minimization, and provides a new way to minimize the variance by forcing different intermediate decoding outputs (paths) to reach an agreement. We also design a focal weighting strategy to effectively combine multiple losses in a scale-balanced way, so that the supervision information is sufficiently enforced throughout the encoder-decoders. To evaluate the proposed method on the pixel-level estimation task, a novel multipath residual encoder is proposed and extensive experiments are conducted on four challenging density estimation and crowd counting benchmarks. The experimental results demonstrate the superiority of our EDS over other paradigms, and improved estimation performance is reported using our deeply supervised encoder-decoder.

Topology Identification of Multilink Complex Dynamical Networks via Adaptive Observers Incorporating Chaotic Exosignals

Topology identification of complex networks is an important and meaningful research direction. In recent years, the topology identification method based on adaptive synchronization has been developed rapidly. However, a critical shortcoming of this method is that inner synchronization of a network breaks the precondition of linear independence and leads to the failure of topology identification. Hence, how to identify the network topology when possible inner synchronization occurs within the network has been a challenging research issue. To solve this problem, this article proposes improved topology identification methods by regulating the original network to synchronize with an auxiliary network composed of isolated chaotic exosystems. The proposed methods do not require the sophisticated assumption of linear independence. The topology identification observers incorporating a series of isolated chaotic exosignals can accurately identify the network structure. Finally, numerical simulations show that the proposed methods are effective to identify the structure of a network even with large weights of edges and abundant connections between nodes.

Global Consensus of High-Order Discrete-Time Multi-Agent Systems with Communication Delay and Saturation Constraint

This article aimed to study the global consensus problem of high-order multi-agent systems with a saturation constraint and communication delay. Among them, all agents are described by discrete-time systems. Firstly, in order to compensate for the communication delay, a networked predictive control method is adopted and a predictive-based control protocol is designed. Secondly, for the neutrally stable agent model, leaderless and leader-following situations are considered and it is proven that, under a fixed communication topology, adopting the prediction-based control protocol makes the multi-agent systems with saturation constraint and communication delay achieve a global consensus. Finally, the results are illustrated via numerical simulation.

Observer-Based Consensus Control for Heterogeneous Multi-Agent Systems with Output Saturations

This paper studies the consensus problem for heterogeneous multi-agent systems with output saturations. We consider the agents to have different dynamics and assume that the agents are neutrally stable and that the communication graph is undirected. The goal of this paper is to achieve the consensus for leaderless and leader-following cases. To solve this problem, we propose the observer-based distributed consensus algorithms, which consists of three parts: the nonlinear observer, the reference generator, and the regulator. Then, we analyze the consensus based on the Lasalle’s Invariance Principle and the input-to-state stability. Finally, we provide numerical examples to demonstrate the validity of the proposed algorithms.