Publications

This paper investigates the role of the function x|x| as a chaos generator in nonautonomous systems. A Duffing-like nonautonomous oscillator is used for illustration. It is rigorously proven via the Melnikov function method that this particular quadratic function induces Smale horseshoes to the Duffing-like system. Moreover, its physical meaning as an energy function is demonstrated, which provides a critical value for the emergence of chaos. Simulations with bifurcation analysis are given for better understanding of the underlying dynamics.

To quantitatively measure the connectedness robustness of a complex network, a sequence of values that record the remaining connectedness of the network after a sequence of node- or edge-removal attacks can be used. However, it is computationally time-consuming to measure the network connectedness robustness by attack simulations for large-scale networked systems. In the present paper, an efficient method based on convolutional neural network (CNN) is proposed to train for estimating the network connectedness robustness. The new approach is motivated by the facts that 1) the adjacency matrix of a network can be converted to a gray-scale image and CNN is very powerful for image processing, and 2) CNN has proved very effective in predicting the controllability robustness of complex networks. Extensive experimental studies on directed and undirected, as well as synthetic and real-world networks suggest that: 1) the proposed CNN-based methodology performs excellently in the prediction of the connectedness robustness of complex networks as a process; 2) it performs fairly well as the indicator for the connectedness robustness, compared to other predictive measures.

This paper initiates a novel approach for generating multi-wing butterfly chaotic attractors from the piecewise-linear Chen system by constructing a duality-symmetric multi-piecewise linear function. It should be pointed out that this proposed method is different from the traditional techniques. Furthermore, its dynamical behaviors are then further investigated and the numerical simulations are also given.

This paper presents a new design of an innovation-based stealthy attack strategy against distributed state estimation over a sensor network. In the absence of network attack, an optimal distributed minimum mean-square error (MMSE) estimator is developed by fusing the interaction measurements from neighboring nodes in the sensor network. Also, the boundedness of distributed estimation covariance is discussed over a regionally observable sensor network, which weakens the requirement for local observability of each sensor. Then, a stealthy attack framework embedded with an adjustable parameter is proposed, under which the attack strategy is to maximize the distributed estimation covariance. Sufficient conditions on the boundedness of the compromised covariance are derived, and the tradeoff between attack stealthiness and attack effects is determined. Finally, numerical examples are shown to verify the developed techniques.

This paper initiates a systematic methodology for real-time chaos-based video encryption and decryption communications on the system design and algorithm analysis. The proposed system design and algorithm analysis have been validated on an FPGA hardware platform via Verilog Hardware Description Language (Verilog HDL). Based on the fundamental anti-control principles of dynamical systems, a 6-D real domain chaotic system is designed, and then the corresponding Verilog HDL algorithm is developed. The proposed Verilog HDL algorithm is utilized to design a real-time chaos-based secure video communication system, with a generalized design principle derived, which is implemented on an FPGA hardware platform equipped with an XUP Virtex-II chip. Following this line, the designed working mechanism is demonstrated by hardware experiments. The security performance is tested using the TESTU01 statistical test suites, the differential analysis, and the sensitivity of key parameters mismatch. Both theoretical analysis and experimental results validate the feasibility and reliability of the proposed system.

In this paper, observer-based finite/infinite time optimal hybrid tracking problems for analog neutral systems with multiple discrete and distributed time delays are formatted and studied. Two continuous-time optimal performance indices with a highgain property are used for tracking control specification. The Newton center interpolation formula and the linear interpolation formula, together with a newly modified rectangular ruler and an extended state vector, are utilized to construct the associate problems of extended discrete-time finite/infinite stage optimal regulators. Consequently, using the discrete-time optimal control theory and the new alternative digital redesign technique with a predictive feature, three effective digital trackers are designed for a given analog neutral system. When the states of the system are not available, a new optimal digital observer for the original analog neutral system is developed for achieving the desired goals of tracker design.

This paper investigates the distributed finite-time consensus tracking problem for a group of autonomous agents modeled by multiple non-identical second-order nonlinear systems. First, a class of distributed finite-time protocols are proposed based on the relative position and relative velocity measurements. By providing a topology-dependent Lyapunov function, it is shown that distributed consensus tracking can be achieved in finite time under the condition that the nonlinear errors between the leader and the followers are bounded. Then, a new class of observer-based algorithms are designed to solve the finite-time consensus tracking problem without using relative velocity measurements. The main contribution of this paper is that, by computing the value of the Lyapunov function at the initial point, the finite settling time can be theoretically estimated for second-order multi-agent systems with the proposed control protocols. Finally, the effectiveness of the analytical results is illustrated by an application in low-Earth-orbit spacecraft formation flying.

Advanced control method plays a key role in guaranteeing safe and reliable automatic train operation. This paper presents a neuro-adaptive robust control method for automatic train operation subject to unknown systematic time-varying dynamics. A general model for describing the train system dynamics is established. A control scheme using assumed bounded values of the unknown time-varying dynamics is proposed for achieving automatic train tracking control, based on which a more advance control scheme without requiring the bounded values is proposed. The closed-loop system is proved to be stable in the sense of Lyapunov. The effectiveness of the theoretical results is demonstrated by numerical simulations.

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