Publications

This paper presents a general control method based on radial basis function networks (RBFNs) for chaotic dynamical systems. For many chaotic systems that can be decomposed into a sum of a linear and a nonlinear part, under some mild conditions the RBFN can be used to well approximate the nonlinear part of the system dynamics. The resulting system is then dominated by the linear part, with some small or weak residual nonlinearities due to the RBFN approximation errors. Thus, a simple linear state-feedback controller can be devised, to drive the system response to a desirable set-point. In addition to some theoretical analysis, computer simulations on two representative continuous-time chaotic systems (the Duffing and the Lorenz systems) are presented to demonstrate the effectiveness of the proposed method.

A two-layered, interconnected network-based structure for urban rail emergency evacuation combines emergency and dispatch networks, creating more efficient evacuation. The authors also present a more efficient, in-depth two-step hub identification method to facilitate evacuation.

The optimal consensus control problem of constrained multi-agent systems is studied in this paper. Considering the transient performance, the final consensus state and constraints simultaneously, the problem is formulated as a constrained non-separable optimization problem accounting for the control inputs and the final consensus state. A distributes hybrid gradient projection alternating direction method of multipliers is designed to separate the coupled problem to independent subproblems solved by each agent, with a fully distributed condition derived to ensure the convergence of the proposed algorithm. To guarantee the stability of the closed-loop system, the finite-time control input sequence is extended online by using the receding horizon control method. The stability of the closed-loop system is analyzed by the Lyapunov method, deriving distributed conditions for parameter selection. Numerical simulations demonstrate the effectiveness of the proposed algorithm.

The conventional LoRa system is not able to sustain long-range communication over fading channels. To resolve the challenging issue, this paper investigates a two-hop opportunistic amplify-and-forward relaying LoRa system. Based on the best relay-selection protocol, the analytical and asymptotic bit error rate (BER), achievable diversity order, coverage probability, and throughput of the proposed system are derived over the Nakagami-m fading channel. Simulative and numerical results show that although the proposed system reduces the throughput compared to the conventional LoRa system, it can significantly improve BER and coverage probability. Hence, the proposed system can be considered as a promising platform for low-power, long-range and highly reliable wireless-communication applications.

In this paper, the robust distributed optimal coordination (DOC) problem over unbalanced directed communication networks is investigated for heterogeneous multi-agent systems subjected to external disturbances. First, by introducing an external disturbance observer, a balanced compensation variable and using the output regulation technique, a new robust continuoustime DOC algorithm is developed, ensuring that the sum of the local convex objective functions is optimal and the behaviors of all agents are coordinated. Then, a robust event-triggered DOC algorithm is designed, which consists of an event-triggered closedloop controller and an event-triggered balanced compensator. Compared with the existing distributed optimization algorithms, the algorithms of this paper solve the DOC problem over unbalanced directed communication network. The heterogeneous agent dynamics with external disturbances considered in this paper are in a general form. Further, the introduction of the event-triggering mechanism can avoid continuous information transmission and improve the efficiency of system resources utilization. Finally, numerical simulations are shown to demonstrate the effectiveness of the theoretical results.

Analyzing the spatiotemporal characteristics of meteorological droughts (MD) and agricultural droughts (AD) and their propagation in different climate zones is important for effective drought management, climate adaptation, and food security. This study takes a unique approach by comparing irrigated and rainfed croplands. A comprehensive framework is developed using drought indices, statistical analysis, trend tests, and wavelet transforms. The spatiotemporal evolution patterns, trends, and correlations of MD and AD in Xinjiang and the Middle-lower Yangtze Plain (MYP) are investigated. The main results showed that severe MD events (e.g., Xinjiang 2005–2009 and MYP 2004–2009) significantly impacted rainfed agricultural systems, leading to a decline in vegetation condition. Long-term irrigation can substantially alleviate AD under MD conditions. From 2000 to 2019, AD on irrigated croplands in Xinjiang continuously improved, while rainfed croplands deteriorated significantly during MD events. In contrast, although overall AD in MYP was mitigated, the benefits of irrigation were only evident during severe AD periods and weakened after 2013. Correlation and wavelet analyses revealed different drought propagation mechanisms between irrigated and rainfed croplands, highlighting the key role of local climate conditions and spatial heterogeneity in determining irrigation efficiency. The findings provide important guidance for optimizing drought management strategies, agricultural planning, and sustainable water resource management.

This article is concerned with the rapid classification issue for dynamical patterns consisting of sampling sequences in a relatively large-scale dynamical dataset constructed by benchmark Rossler systems. Specifically, based on a recently developed deterministic learning mechanism, a rapid dynamical pattern classification method is developed, which contains a modeling stage and a classification stage. In the modeling stage, a deterministic learning scheme is employed to accurately learn/model the inherent dynamics of the training dynamical patterns and store the acquired knowledge in a set of constant radial basis function (RBF) networks. In the classification stage, based on the trained RBF networks, a set of dynamical estimators is developed for real-time dynamic comparison. The generating recognition errors are then used to effectively represent the dynamic differences in real-time. To this end, the associated class label of the minimum recognition error is assigned to the test pattern also in real-time. To demonstrate the effectiveness of the proposed method, a relatively large-scale dynamical pattern dataset containing various dynamical behaviors is constructed by utilizing a deterministic chaos prospector (DCP) technique. The simulation results show that the new method achieves competitive classification performances compared to the state-of-the-art time-series classification method for the dynamical system classification task. In addition to performance advantages, the new method can perform real-time time-series classification with the first 10% of data achieving over 95% of accuracy based on the full-length data. Besides, the superiority of our method is demonstrated from various datasets in the UCR time-series classification (TSC) archive.