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In this paper, the distributed tracking problem for heterogeneous second-order multiagent systems with nonlinear dynamics is studied. Both the dynamics among the followers and the dynamics between the leader and each follower are heterogeneous. A sampled-data-based consensus protocol is proposed. In addition, the communication delays are considered. Based on Lyapunov stability theory and linear matrix...
Energy flow control (EFC) of residential microgrids (RMGs) equipped with renewable generations (RGs) is an essential component for the future smart grid that contributes to enhance renewable energy consumption and reduce cost. Different from most existing papers that devote to offline EFC to against the uncertainties caused by RGs and local load demand in RMGs, this paper focuses on online EFC framework...
One of the most challenging problems in aerospace engineering is the analysis of the separation trajectories by which a missile ejected from an aircraft. The attitudes of some static unstable missile are divergent quickly due to aerodynamic interference by the aircraft. Therefore, it is disadvantageous to missile's separation from aircraft safely. The attitude control strategy must be applied to avoid...
Understanding human behavior is crucial for planning evacuation strategies when an emergency occurs. The social force model, which is a successful quantitative model, has been widely used in investigating human behavior. In this paper, we propose a gradient descent based parameter optimization method to learn the parameters of the social force model from experimental data. Although the original social...
State space reconstruction is usually the first step of nonlinear time series analysis. Among many state space reconstruction approaches, the method of delays (MOD) has been a popular method in noise-free situations. Unfortunately, many real-world time series are usually noisy so that the reconstruction performance can be of low quality. In this paper, we propose an autoencoder-based approach that...
Using dictionary atoms to reconstruct input vectors is of great interest in spare representation. However, a key challenge is how to find a proper dictionary. In this paper, we introduce an active dictionary learning (ADL) method which incorporates active learning criteria to select atoms for dictionary construction with the consideration of both classification and reconstruction errors. Specifically,...
In this paper, coordinated sliding mode control (SMC) and adaptive dynamic programming (ADP) strategy is proposed for near-space aerospace vehicle (NSASV) adaptive attitude tracking control. In this design, the NSASV attitude angle control is implemented as classical cascade control scheme with two control loops in the model. The outer one is a slow control loop for the attitude angle tracking, and...
In this paper, we study the adaptive-critic-based event-driven robust state feedback stabilization for a class of uncertain nonlinear systems. The novel idea lies in bringing adaptive dynamic programming, a self-learning optimization approach, into nonlinear robust control area under uncertain environment and event-triggering framework. Through theoretical analysis, the nonlinear robust stabilization...
Due to the faster-is-slower phenomenon in emergency escape, it is desirable to regulate pedestrian flow at the exit or a bottleneck. Modification of pedestrian facilities was previously studied to increase the efficiency and safety by the transportation community. We propose a robot-assisted pedestrian regulation scheme and study passive human-robot interaction (HRI), where the robot acts as a dynamic...
In this paper, we consider the problems of state estimation and false data injection detection in smart grid when the measurements are corrupted by colored Gaussian noise. By modeling the noise with the autoregressive process, we estimate the state of the power transmission networks and develop a generalized likelihood ratio test (GLRT) detector for the detection of false data injection attacks. We...
Smart Grid security has motivated numerous researches from multiple disciplines. Among the recently discovered security challenges, the False Data Injection (FDI) has drawn great attention from power and energy, computer, and communication research community, because of its potential to manipulate measurements in state estimation (SE) without being identified by conventional bad data detection (BDD)...
In recent years, renewable energy has been largely integrated into power grids as well as micro-grids. The intermittent power injection will affect the stability of the systems, especially for the small-scale ones, such as the micro-grids. Among all the stability issues, load frequency control (LFC) of smart grid with renewable energy integration has become critical in the community. This paper studies...
Understanding human mobility is important for the development of intelligent mobile service robots as it can provide prior knowledge and predictions of human distribution for robot-assisted activities. In this paper, we propose a probabilistic method to model human motion behaviors which is determined by both internal and external factors in an indoor environment. While the internal factors are represented...
The threat of false data injection (FDI) attacks have raised wide interest in the research and development of smart grid security. This paper presents a comparative study on the utilization of supervised learning classifiers to detect direct and stealth FDI attacks in the smart grid. A detailed formulation of the problem for detection with classifiers is first described with proper assumptions and...
Adaptive dynamic programming (ADP) has been investigated for its new architectures, algorithms and applications for years. Recently, the goal representation (Gr) design has been demonstrated with promising results to improve ADP control performance from certain perspectives. This paper is focused on the theoretical analysis of the goal representation dual heuristic dynamic programming (GrDHP). Starting...
A novel event-triggered approach for a class of nonlinear continuous-time system is proposed in this paper to reduce the computation cost of the dual heuristic dynamic programming (DHP) algorithm. Two neural networks are included in our design. A critic network is used to estimate the partial derivatives of the cost function with respect to its inputs, and an action network is used to approximate...
The modern electric power grid has become highly integrated in order to increase reliability of power transmission from the generating units to end consumers. This integrated nature and its upgrade toward an intelligent smart grid make the power grid vulnerable when facing cyber or physical attacks as well as intentional attacks. Therefore, determining the most vulnerable components (e.g., buses or...
In this paper, an online learning algorithm based on policy iteration is established to solve the optimal control problem for weakly coupled nonlinear continuous-time systems. Using the weak coupling theory, the original problem is transformed into three reduced-order optimal control problems. To obtain the optimal control laws without system dynamics, we construct an online data-based integral policy...
In this paper, we present a new wrapper feature selection approach based on Jensen-Shannon (JS) divergence, termed feature selection with maximum JS-divergence (FSMJ), for text categorization. Unlike most existing feature selection approaches, the proposed FSMJ approach is based on real-valued features which provide more information for discrimination than binary-valued features used in conventional...
In this paper, we investigate the self-learning optimal guaranteed cost control problem of input-affine continuous-time nonlinear systems possessing dynamical uncertainty. The cost function related to the original uncertain system is discussed sufficiently, with the purpose of developing the optimal guaranteed cost and the corresponding feedback control input. Through theoretical analysis, the optimal...
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