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Article
Publication date: 4 January 2019

Wenhao Wang, Rujing Shi, Wei Zhang, Haibin Sun, Xiaolu Ge and Chengfeng Li

The purpose of this paper is to improve the generation efficiency of singlet oxygen of methylene blue molecules through finely controlling their aggregation states in drug…

224

Abstract

Purpose

The purpose of this paper is to improve the generation efficiency of singlet oxygen of methylene blue molecules through finely controlling their aggregation states in drug carriers.

Design/methodology/approach

As a photosensitiser in photodynamic therapy, methylene blue (MB) was loaded on citrate-modified hydroxyapatite (HAp) through an electrostatic interaction and followed by encapsulation of coordination complexes of tannic acid (TA) and Fe(III) ions. Ultraviolet-visible absorption spectrum of the supernatant after incubation of samples was recorded at certain time interval to investigate the release behaviour of MB. Photodynamic activity of MB was determined by the oxidation reaction of uric acid by singlet oxygen generated by MB under illumination.

Findings

Almost all MB molecules were immediately released from HAp-MB, whilst an initial burst release of MB from HAp-MB@TA was followed by a sustainable and pH-sensitised release. In comparison with HAp-MB, photocatalystic reduction of HAp-MB@TA by titanium dioxide hardly occurred under illumination, indicating the stability against reduction to leukomethylene blue in vitro. Generation efficiency of singlet oxygen by MB released from HAp-MB@TA was significantly higher than that from HAp-MB because of the control of TA and Fe(III) ions complexes on molecular structures of released MB.

Originality/value

A facile method was herein demonstrated to optimise the generation efficiency of singlet oxygen by controlling aggregation states of PS molecules and improve PDT efficiency to damage tumour tissues.

Details

Pigment & Resin Technology, vol. 48 no. 2
Type: Research Article
ISSN: 0369-9420

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Article
Publication date: 15 March 2013

Sun Changhao and Haibin Duan

The purpose of this paper is to propose a new algorithm for pendulum‐like oscillation control of an unmanned rotorcraft (UR) in a reconnaissance mission and improve the…

388

Abstract

Purpose

The purpose of this paper is to propose a new algorithm for pendulum‐like oscillation control of an unmanned rotorcraft (UR) in a reconnaissance mission and improve the stabilizing performance of the UR's hover and stare.

Design/methodology/approach

The algorithm is based on linear‐quadratic regulator (LQR), of which the determinable parameters are optimized by the artificial bee colony (ABC) algorithm, a newly developed algorithm inspired by swarm intelligence and motivated by the intelligent behaviour of honey bees.

Findings

The proposed algorithm is tested in a UR simulation environment and achieves stabilization of the pendulum oscillation in less than 4s.

Research limitations/implications

The presented algorithm and design strategy can be extended for other types of complex control missions where relative parameters must be optimized to get a better control performance.

Practical implications

The ABC optimized control system developed can be easily applied to practice and can safely stabilize the UR during hover and stare, which will considerably improve the stability of the UR and lead to better reconnaissance performance.

Originality/value

This research presents a new algorithm to control the pendulum‐like oscillation of URs, whose performance of hover and stare is a key issue when carrying out new challenging reconnaissance missions in urban warfare. Simulation results show that the presented algorithm performs better than traditional methods and the design process is simpler and easier.

Details

Aircraft Engineering and Aerospace Technology, vol. 85 no. 2
Type: Research Article
ISSN: 0002-2667

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Article
Publication date: 3 October 2016

Yongbin Sun, Ning Xian and Haibin Duan

The purpose of this paper is to propose a new algorithm for linear-quadratic regulator (LQR) controller of a quadrotor with fast and stable performance, which is based on…

422

Abstract

Purpose

The purpose of this paper is to propose a new algorithm for linear-quadratic regulator (LQR) controller of a quadrotor with fast and stable performance, which is based on pigeon-inspired optimization (PIO).

Design/methodology/approach

The controller is based on LQR. The determinate parameters are optimized by PIO, which is a newly proposed swarm intelligent algorithm inspired by the characteristics of homing pigeons.

Findings

The PIO-optimized LQR controller can obtain the optimized parameters and achieve stabilization in about 3 s.

Practical implications

The PIO-optimized LQR controller can be easily applied to the flight formation, autonomous aerial refueling (AAR) and detection of unmanned aerial vehicles, especially applied to (AAR) in this paper.

Originality/value

This research applies PIO to optimize the tuning parameters of LQR, which can considerably improve the fast and stabilizing performance of attitude control. The simulation results show the effectiveness of the proposed algorithm.

Details

Aircraft Engineering and Aerospace Technology, vol. 88 no. 6
Type: Research Article
ISSN: 1748-8842

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Article
Publication date: 6 March 2017

Soyinka Olukunle Kolawole and Duan Haibin

Keeping satellite position within close tolerances is key for the utilization of satellite formations for space missions. The presence of perturbation forces makes control…

235

Abstract

Purpose

Keeping satellite position within close tolerances is key for the utilization of satellite formations for space missions. The presence of perturbation forces makes control inevitable if such mission objective is to be realised. Various approaches have been used to obtain feedback controller parameters for satellites in a formation; this paper aims to approach the problem of estimating the optimal feedback parameter for a leader–follower pair of satellites in a small eccentric orbit using nature-based search algorithms.

Design/methodology/approach

The chaotic artificial bee colony algorithm is a variant of the basic artificial bee colony algorithm. The algorithm mimics the behaviour of bees in their search for food sources. This paper uses the algorithm in optimizing feedback controller parameters for a satellite formation control problem. The problem is formulated to optimize the controller parameters while minimizing a fuel- and state-dependent cost function. The dynamical model of the satellite is based on Gauss variational equations with J2 perturbation. Detailed implementation of the procedure is provided, and experimental results of using the algorithm are also presented to show feasibility of the method.

Findings

The experimental results indicate the feasibility of this approach, clearly showing the effective control of the transients that arise because of J2 perturbation.

Originality/value

This paper applied a swarm intelligence approach to the problem of estimating optimal feedback control parameter for a pair of satellites in a formation.

Details

Aircraft Engineering and Aerospace Technology, vol. 89 no. 2
Type: Research Article
ISSN: 1748-8842

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Article
Publication date: 4 March 2014

Haibin Duan and Peixin Qiao

The purpose of this paper is to present a novel swarm intelligence optimizer — pigeon-inspired optimization (PIO) — and describe how this algorithm was applied to solve air robot…

2461

Abstract

Purpose

The purpose of this paper is to present a novel swarm intelligence optimizer — pigeon-inspired optimization (PIO) — and describe how this algorithm was applied to solve air robot path planning problems.

Design/methodology/approach

The formulation of threat resources and objective function in air robot path planning is given. The mathematical model and detailed implementation process of PIO is presented. Comparative experiments with standard differential evolution (DE) algorithm are also conducted.

Findings

The feasibility, effectiveness and robustness of the proposed PIO algorithm are shown by a series of comparative experiments with standard DE algorithm. The computational results also show that the proposed PIO algorithm can effectively improve the convergence speed, and the superiority of global search is also verified in various cases.

Originality/value

In this paper, the authors first presented a PIO algorithm. In this newly presented algorithm, map and compass operator model is presented based on magnetic field and sun, while landmark operator model is designed based on landmarks. The authors also applied this newly proposed PIO algorithm for solving air robot path planning problems.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 7 no. 1
Type: Research Article
ISSN: 1756-378X

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Article
Publication date: 2 May 2017

Qiang Xue and Duan Haibin

The purpose of this paper is to propose a new approach for aerodynamic parameter identification of hypersonic vehicles, which is based on Pigeon-inspired optimization (PIO…

295

Abstract

Purpose

The purpose of this paper is to propose a new approach for aerodynamic parameter identification of hypersonic vehicles, which is based on Pigeon-inspired optimization (PIO) algorithm, with the objective of overcoming the disadvantages of traditional methods based on gradient such as New Raphson method, especially in noisy environment.

Design/methodology/approach

The model of hypersonic vehicles and PIO algorithm is established for aerodynamic parameter identification. Using the idea, identification problem will be converted into the optimization problem.

Findings

A new swarm optimization method, PIO algorithm is applied in this identification process. Experimental results demonstrated the robustness and effectiveness of the proposed method: it can guarantee accurate identification results in noisy environment without fussy calculation of sensitivity.

Practical implications

The new method developed in this paper can be easily applied to solve complex optimization problems when some traditional method is failed, and can afford the accurate hypersonic parameter for control rate design of hypersonic vehicles.

Originality/value

In this paper, the authors converted this identification problem into the optimization problem using the new swarm optimization method – PIO. This new approach is proved to be reasonable through simulation.

Details

Aircraft Engineering and Aerospace Technology, vol. 89 no. 3
Type: Research Article
ISSN: 1748-8842

Keywords

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Article
Publication date: 2 October 2017

Shanjun Chen and Haibin Duan

The purpose of this paper is to propose an improved optimization method for image matching problem, which is based on multi-scale Gaussian mutation pigeon-inspired optimization…

226

Abstract

Purpose

The purpose of this paper is to propose an improved optimization method for image matching problem, which is based on multi-scale Gaussian mutation pigeon-inspired optimization (MGMPIO) algorithm, with the objective of accomplishing the complicated image matching quickly.

Design/methodology/approach

The hybrid model of multi-scale Gaussian mutation (MGM) mechanism and pigeon-inspired optimization (PIO) algorithm is established for image matching problem. The MGM mechanism is a nonlinear model, which can adjust the position of pigeons by mutation operation. In addition, the variable parameter (VP) mechanism is exploited to adjust the map and compass factor of the original PIO. Low-cost quadrotor, a type of electric multiple rotorcraft, is used as a carrier of binocular camera to obtain the images.

Findings

This work improved the PIO algorithm by modifying the search strategy and adding some limits, so that it can have better performance when applied to the image matching problem. Experimental results show that the proposed method demonstrates satisfying performance in convergence speed, robustness and stability.

Practical implications

The proposed MGMPIO algorithm can be easily applied to solve practical problems and accelerate convergence speed of the original PIO, and thus enhancing the speed of matching process, which will considerably increase the effectiveness of algorithm.

Originality/value

A hybrid model of the MGM mechanism and PIO algorithm is proposed for image matching problem. The VP mechanism and low-cost quadrotor is also utilized in image matching problem.

Details

Aircraft Engineering and Aerospace Technology, vol. 89 no. 6
Type: Research Article
ISSN: 1748-8842

Keywords

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Article
Publication date: 26 August 2024

Hong Long and Haibin Duan

The purpose of this paper is to present and implement a task allocation method based on game theory for reconnaissance mission planning of UAVs and USVs system.

78

Abstract

Purpose

The purpose of this paper is to present and implement a task allocation method based on game theory for reconnaissance mission planning of UAVs and USVs system.

Design/methodology/approach

In this paper, the decision-making framework via game theory of mission planning is constructed. The mission planning of UAVs–USVs is transformed into a potential game optimization problem by introducing a minimum weight vertex cover model. The modified population-based game-theoretic optimizer (MPGTO) is used to improve the efficiency of solving this complex multi-constraint assignment problem.

Findings

Several simulations are carried out to exhibit that the proposed algorithm obtains the superiority on quality and efficiency of mission planning solutions to some existing approaches.

Research limitations/implications

Several simulations are carried out to exhibit that the proposed algorithm obtains the superiority on quality and efficiency of mission planning solutions to some existing approaches.

Practical implications

The proposed framework and algorithm are expected to be applied to complex real scenarios with uncertain targets and heterogeneity.

Originality/value

The decision framework via game theory is proposed for the mission planning problem of UAVs–USVs and a MPGTO with swarm evolution, and the adaptive iteration mechanism is presented for ensuring the efficiency and quality of the solution.

Details

Aircraft Engineering and Aerospace Technology, vol. 96 no. 9
Type: Research Article
ISSN: 1748-8842

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Article
Publication date: 18 January 2024

Sa Xiao, Xuyang Chen, Yuankai Lu, Jinhua Ye and Haibin Wu

Imitation learning is a powerful tool for planning the trajectory of robotic end-effectors in Cartesian space. Present methods can adapt the trajectory to the obstacle; however…

1359

Abstract

Purpose

Imitation learning is a powerful tool for planning the trajectory of robotic end-effectors in Cartesian space. Present methods can adapt the trajectory to the obstacle; however, the solutions may not always satisfy users, whereas it is hard for a nonexpert user to teach the robot to avoid obstacles in time as he/she wishes through demonstrations. This paper aims to address the above problem by proposing an approach that combines human supervision with the kernelized movement primitives (KMP) model.

Design/methodology/approach

This approach first extracts the reference database used to train KMP from demonstrations by using Gaussian mixture model and Gaussian mixture regression. Subsequently, KMP is used to modulate the trajectory of robotic end-effectors in real time based on feedback from its interaction with humans to avoid obstacles, which benefits from a novel reference database update strategy. The user can test different obstacle avoidance trajectories in the current task until a satisfactory solution is found.

Findings

Experiments performed with the KUKA cobot for obstacle avoidance show that this approach can adapt the trajectories of the robotic end-effector to the user’s wishes in real time, including trajectories that the robot has already passed and has not yet passed. Simulation comparisons also show that it exhibits better performance than KMP with the original reference database update strategy.

Originality/value

An interactive learning approach based on KMP is proposed and verified, which not only enables users to plan the trajectory of robotic end-effectors for obstacle avoidance more conveniently and efficiently but also provides an effective idea for accomplishing interactive learning tasks under constraints.

Details

Industrial Robot: the international journal of robotics research and application, vol. 51 no. 2
Type: Research Article
ISSN: 0143-991X

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Article
Publication date: 3 January 2017

Jiaqi Jia and Haibin Duan

The purpose of this paper is to propose a novel target automatic recognition method for unmanned aerial vehicle (UAV), which is based on backpropagation – artificial neural…

467

Abstract

Purpose

The purpose of this paper is to propose a novel target automatic recognition method for unmanned aerial vehicle (UAV), which is based on backpropagation – artificial neural network (BP-ANN) algorithm, with the objective of optimizing the structure of backpropagation network, to increase the efficiency and decrease the recognition time. A hardware-in-the-loop system for UAV target automatic recognition is also developed.

Design/methodology/approach

The hybrid model of BP-ANN structure is established for aircraft automatic target recognition. This proposed method identifies controller parameters and reduces the computational complexity. Approaching speed of the network is faster and recognition accuracy is higher. This kind of network combines or better fuses the advantages of backpropagation artificial neural algorithm and Hu moment. with advantages of two networks and improves the speed and accuracy of identification. Finally, a hardware-in-the-loop system for UAV target automatic recognition is also developed.

Findings

The double hidden level backpropagation artificial neural can easily increase the speed of recognition process and get a good performance for recognition accuracy.

Research limitations/implications

The proposed backpropagation artificial neural algorithm can be ANN easily applied to practice and can help the design of the aircraft automatic target recognition system. The standard backpropagation algorithm has some obvious drawbacks, namely, converging slowly and falling into the local minimum point easily. In this paper, an improved algorithm based on the standard backpropagation algorithm is constructed to make the aircraft target recognition more practicable.

Originality/value

A double hidden levels backpropagation artificial neural algorithm is presented for automatic target recognition system of UAV.

Details

Aircraft Engineering and Aerospace Technology, vol. 89 no. 1
Type: Research Article
ISSN: 1748-8842

Keywords

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