Zhu Huayong

A new method for mitigation of end effect in empirical mode decomposition

Empirical mode decomposition(EMD) is a time-frequency analysis method for non-stationary and nonlinear signal. In the iteration process of EMD, at least one data point outside each end is required to build the spline. However, the extending data points which are not chosen properly will cause vibration and distortion phenomenon in EMD. In order to mitigate that end effect, a novel extrema points extending method is proposed: End mirror extending is used in high frequency, while least square polynomial extending is used in low frequency. Numerical result proves that the proposed method can mitigate the end effect in EMD effectively with minor decomposition error.

Notice of Retraction Semi-parametric regression model prediction method based on empirical mode decomposition

Semi-parametric regression model prediction method based on empirical mode decomposition was studied in this paper. Firstly, basic idea of the empirical mode decomposition was introduced, and the improved algorithm was proposed to mitigate the end effect in the iterative shift process. Secondly, least squares method was employed to estimate the parameter β based on the trend component of empirical mode decomposition, and the non-parametric g(·) was estimated through building the AR models of the intrinsic mode functions. The vector matrix was computed by Yule-Walker method. Finally, time series prediction of two nonlinear systems was analyzed based on the semi-parametric regression model. The results show that the proposed model predictive method is fit for nonlinear and non-stationary time series estimate.

UAVs flocking and reconfiguration control based on artificial physics

This paper investigates the flocking and reconfiguration control problem of multiple unmanned aerial vehicles (UAVs) system by using a modified artificial physics (AP) method. Firstly, in order to dirve the multi-UAV system with double-intergrator dynamics to form the uniform distribued standard formation, we redefine the attractive and repulsive forces in the traditional artificial physics method, and analyze the stability of the flocking control method using the LaSalle invariance principle accordingly. Immediately, we develop an improved strategy regards to the local optimal solution of the flocking problem in the serial number independent case. Then, we explore the reversible and exclusive bijective transformation between the standard formation and an arbitrary formation. Using the bijective transformation, we propose a flocking control method to form the arbitrary formation, and futher extend to the flocking reconfiguration problem, such as flocking contraction and expansion, formation switching and flocking with obstacle avoidence. In order to verify the effectiveness of the proposed flocking and reconfiguration control method, we construct a multiple quadrotors coodination simulation platform based on the robot operating system (ROS) and Gazebo simulator. The simulation results demonstrate the effectiveness of the flocking and reconfiguration control method. Finally, we expand our artificial physics based flocking control method to the multiple fixed-wing UAVs system, and the effectiveness is verified in a semi-physical simulation environment for multi-UAV coodination by using X-Plane flight simulaor and the UAV autopilot.

A Continuous-time Markov Decision Process Based Method on Pursuit-Evasion Problem

Abstract This paper presents a method to address the pursuit-evasion problem which incorporates the behaviors of the opponent, in which a continuous-time Markov decision process (CTMDP) model is introduced, where the significant difference from Markov decision process (MDP) is that the influence of the transition time between the states is taken into account. By introducing the concept of situation, the probabilities addressing average behaviors are obtained. Furthermore, these probabilities are introduced to construct the transition matrix in the CTMDP. A policy iteration method for solving the CTMDP is also given. To demonstrate the CTMDP method for pursuit-evasion, examples in a grid environment are computed. The CTMDP-based method presented in this paper offers a new approach to pursuit-evasion modeling and may be extended to similar problems in the sequential decision process.

Targets assinment for multi-UCAV cooperative using a game theroy approach

Multi-UCAV cooperative target assignment is a complex problem in military applications. This paper establishes outcome matrix, cost matrix as well as utility matrix based on the value and the possibility of damages of UCAVs and targets. Considering the effect of the enemys countermeasure, this paper proposes a target assignment algorithm with the theory of game. The simulation results demonstrate the efficacy of the algorithm.

The Lagrangian Relaxation based resources allocation methods for air-to-ground operations under uncertainty circumstances

The task of assigning weapons and sensors to targets is a crucial one in the military, and it is a resources allocation problem under uncertainty circumstances. Firstly, the integer programm based formal model of resources allocation is put forward. The formulation can be solved using Lagrangian Relaxation (LR) to decouple the multi-target problem into many single-target POMDPs, and they are small enough to solve fastly. Then, the POMDP based single target multi-stage optimization, which reflects the uncertainty in task execution output and decision-making, is bring forward to modeling and solving low level sub-problems. And sub-gradients algorithm is used in top-level search processes to offer the marginal resources price for POMDP sub-problems, so as to coordinate the resources consumption of low level problems. Lastly, the method of construction feasible solutions based on the solutions of Lagrangian dual problem is put forward. Simulation results illustrate the validity of our method.