H infinity control design for Eight-Rotor MAV attitude system based on identification by interval type II fuzzy neural network

Xiangjian CHEN,Kun SHU,Di LI 한국항공우주학회 2016 International Journal of Aeronautical and Space Sc Vol.17 No.2

In order to overcome the influence of system stability and accuracy caused by uncertainty, estimation errors and external disturbances in Eight-Rotor MAV, L2 gain control method was proposed based on interval type II fuzzy neural network identification here. In this control strategy, interval type II fuzzy neural network is used to estimate the uncertainty and nonlinearity factor of the dynamic system, the adaptive variable structure controller is applied to compensate the estimation errors of interval type II fuzzy neural network, and at last, L2 gain control method is employed to suppress the effect produced by external disturbance on system, which is expected to possess robustness for the uncertainty and non-linearity. Finally, the validity of the L2 gain control method based on interval type II fuzzy neural network identifier applied to the Eight-Rotor MAV attitude system has been verified by three prototy experiments.

H infinity control design for Eight-Rotor MAV attitude system based on identification by interval type II fuzzy neural network

CHEN, Xiangjian,SHU, Kun,LI, Di The Korean Society for Aeronautical and Space Scie 2016 International Journal of Aeronautical and Space Sc Vol.17 No.2

In order to overcome the influence of system stability and accuracy caused by uncertainty, estimation errors and external disturbances in Eight-Rotor MAV, L2 gain control method was proposed based on interval type II fuzzy neural network identification here. In this control strategy, interval type II fuzzy neural network is used to estimate the uncertainty and non-linearity factor of the dynamic system, the adaptive variable structure controller is applied to compensate the estimation errors of interval type II fuzzy neural network, and at last, L2 gain control method is employed to suppress the effect produced by external disturbance on system, which is expected to possess robustness for the uncertainty and non-linearity. Finally, the validity of the L2 gain control method based on interval type II fuzzy neural network identifier applied to the Eight-Rotor MAV attitude system has been verified by three prototy experiments.

Optimal type II fuzzy neural network controller for Eight-Rotor MAV

Xiangjian CHEN,Di Li,Xi-Bei Yang,Yuecheng Yu 제어·로봇·시스템학회 2017 International Journal of Control, Automation, and Vol.15 No.4

This paper focuses on modeling and intelligent control of the new eight-rotor MAV which is used to solvethe problem of low coefficient proportion between lift and gravity for QuadrotorMAV. The dynamic and kinematicalmodeling for the eight-rotor MAV.Neuro-Fuzzy adaptive controller is proposed which is composed of two type-IIfuzzy neural networks (T-IIFNNs) and one PD controller: The PD controller is adopted to control the attitude, oneof the T-IIFNNs is designed to learn the inverse model of eight-rotor MAV on-line, the other one is the copy of theformer one to compensate for model errors and external disturbances, both structure and parameters of T-IIFNNs aretuned on-line at the same time, and then the stability of the eight-rotor MAV closed-loop control system is provedusing Lyapunov stability theory. Meanwhile ,in order to reduce the computation work, the type-reduction andmodel construction process have been improved. For the issue of type reduction, a novel improved EKM algorithmis developed for improving the EKM algorithm. The proposed algorithm provides two improvements on the EKMalgorithm. For the issue of rules redundant, the concept of normalized difference is proposed to describe the changeof adjacent singular value so as to reflect the essential differences between redundant rules and important rules. Then the number of effective singular can be determined according to its critical point, and the type-2 fuzzy modelis constructed with rules located by TLS decomposition. Finally, the validity of the proposed control method hasbeen verified through real-time experiments. The experimental results show that the performance of Neuro-Fuzzyadaptive controller performs very well under sensor noise and external disturbances.

An Improved Type-2 Possibilistic Fuzzy C-Means Clustering Algorithm with Application for MR Image Segmentation

Xiangjian Chen,Di Li,Hongmei Li 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.11

This paper presents a new clustering algorithm named improved type-2 possibilistic fuzzy c-means (IT2PFCM) for fuzzy segmentation of magnetic resonance imaging, which combines the advantages of type 2 fuzzy set, the fuzzy c-means (FCM) and Possibilistic fuzzy c-means clustering (PFCM). First of all, the type 2 fuzzy is used to fuse the membership function of the two segmentation algorithms (FCM and PCM), the membership function is an interval distribution, the determined fuzzy values which are the outputs of the FCM and PCM. Secondly, the initialization of cluster center and the process of type-reduction are optimized in this algorithm, which can greatly reduce the calculation of IT2PFCM and accelerate the convergence of the algorithm. Finally, experimental results are given to show the effectives of proposed method in contrast to conventional FCM, PFCM and type 2 fuzzy c-means.

Modeling and Neuro-Fuzzy Adaptive Attitude Control for Eight-Rotor MAV

Xiangjian Chen,Di Li,Yue Bai,Zhijun Xu 제어·로봇·시스템학회 2011 International Journal of Control, Automation, and Vol.9 No.6

This paper focuses on modeling and intelligent control of the new Eight-Rotor MAV which is used to solve the problem of low coefficient proportion between lift and gravity for Quadrotor MAV. The dynamical and kinematical modeling for the Eight-Rotor MAV was developed which has never been proposed before. Based on the achieved dynamic modeling, two types of controller were presented. One type, a PID controller is derived in a conventional way with simplified dynamics and turns out to be quite sensitive to sensor noise as well as external perturbation. The second type controller is the Neuro-Fuzzy adaptive controller which is composed of two type-II fuzzy neural networks (T-IIFNNs) and one PD controller: The PD controller is adopted to control the attitude, one of the T-IIFNNs is designed to learn the inverse model of Eight-Rotor MAV on-line, the other one is the copy of the former one to compensate for model errors and external disturbances, both structure and parameters of T-IIFNNs are tuned on-line at the same time, and then the stability of the Eight-Rotor MAV closed-loop control system is proved using Lyapunov stability theory. Finally, the validity of the proposed control method has been verified through real-time experiments. The experimental results show that the performance of Neuro-Fuzzy adaptive controller performs very well under sensor noise and external disturbances, and has more superiority than traditional PID controller.

A novel data-driven rollover risk assessment for articulated steering vehicles using RNN

Xuanwei Chen,Wei Chen,Liang Hou,Huosheng Hu,Xiangjian Bu,Qingyuan Zhu 대한기계학회 2020 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.34 No.5

Articulated steering vehicles have outstanding capability operating but suffer from frequent rollover accidents due to their complicated structure. It is necessary to accurately detect their rollover risk for drivers to take action in time. Their variable structure and the variable center of mass exhibit nonlinear time-variant behavior and increase the difficulty of dynamic modelling and lateral stability description. This paper proposes a novel data-driven modelling methodology for lateral stability description of articulated steering vehicles. The running data is first collected based on the typical operations that prone to rollover and then classified into two types: Safety and danger. The data quality is further improved by wavelet transformation. Finally, an RNN model is built on the data. The experimental results show that the output of the RNN model can accurately quantify lateral stability of the vehicle, i.e., the risk of rollover, when it is turning and crossing uneven surfaces or obstacles.

An efficient error concealment algorithm for H.264/AVC using regression modeling-based prediction

Xiaoming Chen,Yuk Ying Chung,Changseok Bae,Xiangjian He,Wei-Chang Yeh IEEE 2010 IEEE transactions on consumer electronics Vol.56 No.4

<P>This paper presents a novel error concealment algorithm for H.264/AVC based on a regression model, which is constructed according to the spatial relationship between block locations and their motion activities. With the proposed algorithm, a corrupted macroblock is partitioned into subblocks and the motion vector of each sub-block is predicted through the regression model with the help of the neighbor motion vectors. The experimental results show that the proposed algorithm can achieve significant Peak Signal Noise Ratio (PSNR) improvement over existing methods with even reduced complexity. The implementation of the proposed algorithm is very simple and therefore it can be readily applied to real-time video applications running on various consumer electronic products such as mobile devices.</P>

High-efficiency solution-processed small-molecule solar cells featuring gold nanoparticles

Rashid bin Mohd Yusoff, Abd.,Lee, Seung Joo,Jang, Jin,Long, Guankui,Wan, Xiangjian,Chen, Yongsheng The Royal Society of Chemistry 2014 Journal of Materials Chemistry A Vol.2 No.47