Spatiotemporal dynamic network for regional maritime vessel flow prediction amid COVID-19.

The COVID-19 pandemic has stifled international trade and the global maritime industry. Its impact on the routing of the regional vessel traffic flow provides supportive data to port authorities, ship owners, shippers, and consignees. This study proposes a spatiotemporal dynamic graph neural network (STDGNN) model that includes the usual primary part of the vessel flow and an auxiliary part of newly confirmed COVID-19 cases near the port. The primary part consists of a time-embedding (TE) block, two dynamic graph neural network (DGNN) blocks, and a gated recurrent unit block, to capture the spatiotemporal dependence in the regional vessel traffic flow. The auxiliary part is made of multiple blocks to exploit the dynamic temporal relationships in hours, days, and weeks. Moreover, the performance of the STDGNN model is verified by utilising real vessel traffic flow data (i.e. inflow, outflow, and volume) and the new cases of COVID-19 near the port of New York, USA, provided by the automatic identification system and the Johns Hopkins University Centre for Systems Science and Engineering. The 2-h prediction result shows a 37.7%, 17.23%, and 11.4% improvement in the mean absolute error (MAE) over the gated recurrent unit (GRU), STGCN, and TGCN models, respectively. The delicate and adaptable prediction of vessel traffic flow could help the port relieve congestion, enhance efficiency, and further assist the recovery of regional maritime industries in the post-COVID era.

Adaptive neural network control for uncertain dual switching nonlinear systems.

Dual switching system is a special hybrid system that contains both deterministic and stochastic switching subsystems. Due to its complex switching mechanism, few studies have been conducted for dual switching systems, especially for systems with uncertainty. Usually, the stochastic subsystems are described as Markov jump systems. Based upon the upstanding identity of RBF neural network on approaching nonlinear data, the tracking models for uncertain subsystems are constructed and the neural network adaptive controller is designed. The global asymptotic stability almost surely (GAS a.s.) and almost surely exponential stability (ES a.s.) of dual switching nonlinear error systems are investigated by using the energy attenuation theory and Lyapunov function method. An uncertain dual switching system with two subsystems, each with two modes, is studied. The uncertain functions of the subsystems are approximated well, and the approximation error is controlled to be below 0.05. Under the control of the designed adaptive controller and switching rules, the error system can obtain a good convergence rate. The tracking error is quite small compared with the original uncertain dual switching system.

Mean-square H∞ antagonistic formations of second-order multi-agent systems with multiplicative noises and external disturbances.

In physical systems, since the acceleration is always regard as the control input, it is meaningful to study the coordination problems of the second-order multi-agent system. This paper devotes to the mean-square H∞ antagonistic formation control of second-order multi-agent systems with multiplicative noises and external disturbances under directed signed topologies. To force all agents achieve antagonistic formation and attenuate the effect of communication noises and external disturbances, a novel distributed consensus control protocol with a time-invariant control gain is proposed where only the information that received from neighbors is utilized. And then, by combining the theories of graph, robust H∞ control and stochastic analysis, some matrix inequalities conditions are deduced. It is proved that under the designed control protocol, the state of each agent converge to its own desired formation in its allied groups in the sense of mean square. Furthermore, numerical simulations are given for the purpose of showing that the proposed theoretical results are effective.

Multiagent Containment Control With Nonconvex States Constraints, Nonuniform Time Delays, and Switching Directed Networks.

This brief studies the constrained containment control problem of continuous-time multiagent systems with nonconvex states constraints, nonuniform time delays, and switching directed networks. A distributed containment control algorithm consisting of nonlinear projection, nonlinear constraints, and communication delays is proposed. Based on the model transformation technique, convex analysis method, and the Lyapunov function, we prove that all agents finally converge into the convex hull formed by the given stationary points with arbitrarily bounded communication delays and arbitrarily switching networks as long as each agent jointly has at least one path from the given points to itself. Finally, a numerical example is included to show the effectiveness of theoretical results.

A further result on consensus problems of second-order multi-agent systems with directed graphs, a moving mode and multiple delays.

This paper considers a consensus problem of a class of second-order multi-agent systems with a moving mode and multiple delays on directed graphs. Using local information, a distributed algorithm is adopted to make all agents reach a consensus while moving together with a constant velocity in the presence of delays. To study the effects of the coexistence of the moving mode and delays on the consensus convergence, a frequency domain approach is employed through analyzing the relationship between the components of the eigenvector associated with the eigenvalue on imaginary axis. Then based on the continuity of the system function, an upper bound for the delays is given to ensure the consensus convergence of the system. A numerical example is included to illustrate the obtained theoretical results.