Cui M.,Nanyang Normal University |
Cui M.,Oil equipment intelligent control engineering laboratory of Henan province |
Liu W.,Nanyang Normal University |
Liu W.,Oil equipment intelligent control engineering laboratory of Henan province |
And 5 more authors.
Nonlinear Dynamics | Year: 2015
Based on the uncertain nonlinear kinematic model of the differential-driving mobile robots, an adaptive sliding mode control method is used to design a controller for trajectory tracking of the differential-driving mobile robots with unknown parameter variations and external disturbances. The total uncertainties of the robot are estimated online by an improved linear extended state observer (ESO) with the error compensating term. The adaptive sliding mode controller with the switching gain is adjustable real-time online is developed by selecting the appropriate PID-type sliding surface. The convergence of the tracking errors for wheeled mobile robots is proved by the Lyapunov stability theory. Moreover, the simulation and real experiment results all show that the effectiveness and superiority of the proposed the adaptive sliding mode control method, in comparison with the traditional sliding model control and backstepping control method. © 2015 Springer Science+Business Media Dordrecht
Zhang Y.-Y.,Nanyang Normal University |
Zhang Y.-Y.,Oil Equipment Intelligent Control Engineering Laboratory of Henan Province |
Wang Z.-P.,Nanyang Normal University |
Lv X.-D.,Nanyang Normal University
Circuits, Systems, and Signal Processing | Year: 2016
We present a novel visual saliency detection method using covariance matrices on a Riemannian manifold. After over-segmentation, superpixels are generated and featured by the region covariance matrix. The superpixels on image boundary are regarded as possible background cues and are used to build the background dictionary. A sparse model is then constructed based on the background dictionary, where a kernel method, embedding Riemannian manifolds into reproducing kernel Hilbert space, is used. For each superpixel, we compute sparse reconstruction errors as a saliency measurement, which are then weighted based on the local context and global context information. Finally, multi-scale reconstruction errors are integrated to reduce the effect of the scale problem, and an object-biased Gaussian model is adopted to refine the saliency map. The main contribution of this paper is using a kernel sparse representation of the region covariance descriptors for saliency detection. Experiments with public benchmark dataset show that the proposed algorithm outperforms the state-of-the-art methods in terms of precision, recall, and mean absolute error, which demonstrate that our method is more effective in uniformly highlighting salient objects and is robust to background noise. © 2016, Springer Science+Business Media New York.
Hai T.,Harbin Engineering University |
Hai T.,Nanyang Normal University |
Hai T.,Oil Equipment Intelligent Control Engineering Laboratory of Henan Province |
Xi Z.-H.,Harbin Engineering University
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | Year: 2016
In order to improve the zoomed effect of the method based on partial differential equation, combining the improved complex diffusion model and the nonlocal means filter, the image enlargement method is proposed. Having the advantage of precision location of the edges, with the edges sharpened by the shock filter, the improved anisotropic complex diffusion couples to the nonlocal means filter to keep with the similarity of the diffused image, the high resolution image is reconstructed. Not only using the local information of the image but the image's nonlocal information,the method makes the zoomed image more natural, at the same time, attenuates the edge's over-sharpen, therefore the enlarged image has better visual effective. The simulations prove the prominent performance of the proposed method. © 2016, Chinese Institute of Electronics. All right reserved.