{"title":"Attitude control research for quad-rotor UAV","authors":"Hu Qiong, L. Tian, Fei Qing, Geng Qing-bo","doi":"10.1109/ICICIP.2014.7010311","DOIUrl":"https://doi.org/10.1109/ICICIP.2014.7010311","url":null,"abstract":"Quad-rotor helicopter is a popular platform for unmanned aerial vehicle (UAV) research due to its simplicity of structure and maintenance as well as the capability of hovering and vertical take-off and landing. The attitude controller is of great importance since it ensures the vehicle to keep balance and perform the desired maneuver. In this paper, sliding mode controller for attitude regulation is designed based on variable structure theorem according to the mathematical model of the 3-DOF Quanser hover system. The control objective of the attitude controller is to asymptotically track the different demanded signals, even if there exist unknown disturbances. Considering the chattering existing in the sliding model control system, the high-slope saturation function is utilized instead of the sign function. To validate the effectiveness and efficiency of the proposed method, the comparison among sliding mode, backstepping and PID methods is carried out. The results from both digital simulations and experiments on the hover system show that the sliding mode control law can perform adequately as an attitude controller in terms of better tracking performance and robustness compared with the other two methods.","PeriodicalId":408041,"journal":{"name":"Fifth International Conference on Intelligent Control and Information Processing","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126197086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A low-cost GPS/INS integration based on UKF and BP neural network","authors":"Qian Zhang, Baokui Li","doi":"10.1109/ICICIP.2014.7010322","DOIUrl":"https://doi.org/10.1109/ICICIP.2014.7010322","url":null,"abstract":"Nowadays, low-cost Global Positioning System (GPS)/inertial Navigation System (INS) integration is widely used. Numerous techniques based on Kalman Filter (KF) and Artificial Neural Networks (ANNs) are proposed to fuse the GPS and INS data. Kalman filter is an optimal real-time data fusion method for GPS/INS integration while GPS signal is available. But when GPS outages, Kalman filter cannot provide estimated position errors for INS. Without compensation, navigation accuracy will deteriorate badly along with time. ANNs are able to handle the problem of non-linearity and map input-output relationships without prior knowledge. In order to provide continuous, accurate and reliable navigation solution even during GPS outages, we proposed a novel model of combining UKF and BP neural network algorithms for INS errors compensation. UKF is an implementation of KF with great performance and used to ensure the high accuracy when GPS is available. BP is a most widely used method of training a multi-layer Feed-Forward Artificial Neural Networks (FFANNs). On the basis of enough training, it can predict INS position error when GPS signal is blocked. The model has been verified to have good performance for fusing GPS and INS data, even when GPS signal is unavailable.","PeriodicalId":408041,"journal":{"name":"Fifth International Conference on Intelligent Control and Information Processing","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114889582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}