Delin Luo

Obstacle avoidance and formation regrouping strategy and control for UAV formation flight

Obstacle avoidance (OA) and formation regrouping (FR) are very important issues for implementation of automatic UAVs formation flight. In this paper, the OA strategy and control for UAV leader is investigated first. Then, it is followed by investigation of the OA strategy and control for UAV follower. In the process of OA, in order to conduct OA reasonably and efficiently, the formation is split first and then all the UAVs regroup to fly in the original formation after OA process is completed. To this end, formation splitting and regrouping strategies in four OA scenarios are presented for UAV formation flight. Simulation experimental results demonstrate that the presented OA and FR strategies and control are effective.

UAV formation flight control and formation switch strategy

During the formation flight time, the Unmanned Aerial vehicles (UAVs) have to transform their flight formation in many cases, such as environmental change, the modification of the task and some UAVs leaving of the formation. During the time to change the formation, the most important thing is to avoid UAVs crash into each other, then, they should finish the transformation process in certain time. This paper firstly focuses on the UAV formation keeping by using Proportion Integration Differentiation (PID) control method. Secondly, several control strategy are designed for formation transformation. Then, use the above methods to complete the formation transformation. Finally, the effectiveness of UAVs formation transformation is taken into account by simulated verification and comparative analysis.

A kind of BP neural network algorithm based on grey interval

In order to improve the learning ability of a forward neural network, in this article, we incorporate the feedback back-propagation (FBBP) and grey system theory to consider the learning and training of a neural network new perspective. By reducing the input grey degree we optimise the input of the neural network to make it more rational for learning and training of neural networks. Simulation results verified the efficiency of the proposed algorithm by comparing its performance with that of FBBP and classic back-propagation (BP). The results showed that the proposed algorithm has the characteristics of fast training and strong ability of generalisation and it is an effective learning method.

Hybrid Ant Colony Multi-Objective Optimization for Flexible Job Shop Scheduling Problems

In this paper, considering multiple objectives, a Hybrid Ant Colony Optimization (HACO) is proposed to deal with the Flexible Job-Shop Scheduling Problem (FJSSP). In the HACO, ant colony optimization is used to assign operations to machines, where a new combined heuristic is designed to balance the workloads between machines while ants tend to select the machine with less processing time for those operations. After that, SPT scheduling rule is applied to sequence the operations on each machine to shorten the makespan as well as to meet the desired delivery time window. To improve the globe search performance, a designed local search is used to search the neighborhood of an obtained optimal solution for possible better solutions by the criterions of less total workloads and their variance for all machines. Simulation results show that the proposed HACO is very efficient compared with the basic ACO and other algorithms in dealing with FJSSPs.

Path planning for a reconnaissance UAV in uncertain environment

To obtain intelligence information from uncertain environment in wartime, it will exist high threat factors for the HALE-UAVs flight safety because of long endurance, wide scope and large number objects of military reconnaissance and surveillance operation. To address such a situation, we develop a dynamic path planning method in this paper for UAV in-flight routing. We consider several problems using this method: (a) building a reference plan by the priori information; (b) consideration of two models, one is an environment information model based on the acquisition probability of targets, and the other is a threat model based on airborne sensors; (c) exploration of the decision system as a decision-maker which can permit UAVs to enter into threat zones with a known probability of being attacked in order to execute a reconnaissance mission; (d) determination the optimal reconnaissance flight path; and (e) evaluation the results of flight path planning.

UAV FORMATION FLIGHT CONTROL AND FORMATION SWITCH STRATEGY

During the formation flight time, the Unmanned Aerial vehicles (UAVs) have to transform their flight formation in many cases, such as environmental change, the modification of the task and some UAVs leaving of the formation. During the time to change the formation, the most important thing is to avoid UAVs crash into each other, then, they should finish the transformation process in certain time. This paper firstly focuses on the UAV formation keeping by using Proportion Integration Differentiation (PID) control method. Secondly, several control strategy are designed for formation transformation. Then, use the above methods to complete the formation transformation. Finally, the effectiveness of UAVs formation transformation is taken into account by simulated verification and comparative analysis.

Docking control of autonomous aerial refueling for UAV based on LQR

Unmanned Aerial Vehicle (UAV) has many advantages, such as miniature,light weight, low cost, excellent and maneuverability and so on, which make it get more and more attention from the military of many countries. A critical limitation for the current use of UAV is their limited range. This paper describes the results of an effort on the modeling of the UAV aerial refueling problem and on how to realize the docking procedure accurately without considering wake effects and natural oscillation of the probe-drogue refueling system. Desirable performances are achieved by the LQR-based control laws for the docking of the UAV to the probe-drogue refueling system. By using the simulation platform of Matlab/Simulink, the simulation results of the tracks of probe and drogue in the docking phrase of aerial refueling are verified. This simulations reveal that the design of the full state feedback controller can realize docking between the tanker and the unmanned aerial vehicle fast and smoothly.

Research on the Grey Rough Set Modeling and Its Properties Using Constructive Method

Based on the combination of rough set (RS) theory and grey system (GS) theory, this paper presents the grey-rough set modeling using constructive method (CM). Considering grey system theory having good ability in data preprocessing, grey number is used to describe the imprecise, uncertain, vague information in the information system through grey mapping, and then the grey information system is built. In this paper, CM is applied to investigate the RS. the definition of four pairs of approximate operators of grey rough set (GRS) is proposed using CM. At the same time, we investigated the GRS-related fundamental theory and delivered the algebra system of the GRS. It makes that the methods of GS classification can be used to describe the incomplete information more precisely, and enhance the ability of grey information covering, and therefore the connotative knowledge and rule in grey information system can be discovered more effectively and exactly.

Autopilot design for bank to turn missile

Bank to Turn (BTT) missile is a nonlinear control system with severe coupling between its three channels. Therefore, independent control system design method for three channels cannot be employed directly for BTT autopilot. In this paper, the linearized mathematical model for BTT missile is established first. Then the coupling factors between three channels are removed using coordinated control theory in order to design the pitch, yaw and roll channel autopilots. After that, coordinated loops are introduced between channels and the corresponding parameters are designed to ensure the side slip angle to be zero or near zero during the fight of BTT missile. Finally, six degree target interception mathematical simulations are performed to demonstrate the effectiveness of the designed BTT missile autopilot.

Research on maneuvering decisions for multi-UAVs Air combat

The maneuvering decision-making for multi-UAVs cooperative air combat is an intelligent process. The process is described by the influence diagram analysis method, and one decision-making model is established. The cooperative process is to transform many-to-many air combat model to one-to-one version. The transformation process happens according to real-time situation evaluation which was made by using Bayesian theorem. Simulation results show that the proposed model is effective for maneuvering decision-making for multi-UAVs cooperative air combat.