How to improve the position tracking accuracy of electro-hydraulic servo system is a hot issue today. Full state feedback control has received widespread attention for its ability to significantly improve control performance, however, its practical application range is limited in view of the large influence of measurement noise. In terms of this issue, we propose an adaptive robust controller based on improved structure and desired compensation. Firstly, to reduce the impact of measurement noise, the actual state value is substituted by the corresponding desired value in the controller design based on model compensation and the adaptive model compensator. Then, we introduce a new auxiliary variable into the controller to optimize its structure. In addition, nonlinear robust control laws are integrated in the controller to balance unstructured uncertainties. Simulation analysis shows that the proposed control strategy not only achieves the asymptotic tracking when parameter perturbation exists, but also ensures a specified transient response and final tracing precision under the combined influence of structured and unstructured uncertainties. The results indicate that the control strategy has good control accuracy as well as strong robustness.
Triangular-pyramidal ω-Bi_2O_3 is successfully synthesized via a one-step wet-chemical method.XRD,SEM,and UV-vis have been employed to characterize the as-prepared samples.Structural characterization by XRD confirms the formation of triclinic ω-Bi_2O_3 with high purity.The well-defined flowerlike Bi_2O_3 structures consisted of many triangular-pyramids are formed.Preparative parameters,such as concentration of PEG 6000,have great effects on the morphology and the particle size.The obvious absorption edge for ω-Bi_2O_3 powder is located at about 493 nm,which corresponds to the optical band gap energy of2.73 eV. 相似文献
In view of the limitation of fixed complete orthogonal transformation, represented by two-dimensional wavelet transform and discrete cosine transform in compressed sensing high-resolution image reconstruction, this paper proposes a new method for high-resolution image reconstruction based on adaptive redundant dictionary sparse representation with the total variation constraint.The algorithm takes the intermediate image in the process of iteration as the training sample to get a redundant dictionary suitable for sample characteristics by adaptive learning. It makes full use of the correlation between dictionary atoms and the image to get an ideal complete sparse representation, thus reducing the sampling rate and improving the quality of image reconstruction. Finally, the algorithm takes the total variation as a constraint and uses the split Bregman iterative method to solve the sparse optimization problem. Simulation shows that the proposed method can reconstruct high quality images under a low sampling rate. 相似文献