Kongzhi yu Juece/Control and Decision (ISSN:1001-0920) is a monthly peer-reviewed scopus indexed journal originally founded in 1986. It is sponsored by the Ministry of Education, china and Northeastern University, china. Kongzhi yu Juece/Control and Decision (ISSN:1001-0920) has always adhered to the correct purpose of running the journal, and has been committed to gathering and disseminating excellent academic achievements, inspiring technological innovation, and promoting the development of disciplines in my country.Aiming at major national needs and international frontiers, this journal has published a large number of original and high-level research result. The journal was selected into the "China Science and Technology Journal Excellence Action Plan Project" in December 2019.In the future, it will strive to build an open innovation, collaborative integration.
This paper investigates the finite time prescribed performance control strategy for the trajectory tracking problem of quadrotor UAVs with time-vary disturbance, model uncertainty and constraint of the output error. Firstly, the UAVs dynamic model is decoupled into an sub atitude system and. Then, the finite time convergence of the conversion error is realized, and the output error of the original system is limited by introducing the error conversion function, performance constraint function and designing the fast terminal sliding surface reasonably. The designed controller can guarantee th
Aiming at the problems of low segmentation accuracy of small targets, high computational complexity, and slow convergence in the U-Net, an U-Net network based on dilated convolution and reconstructed sampling units (DSU-Net) is constructed. In the DSU-Net, in order to increase the receptive field of image feature extraction and fuse multi-scale information, dilated convolutional layers with different dilation rates are designed; in view of the shortcoming of losing a large amount of semantic information during the pooling process, sampling units which combine pooling and convolution are con
Accurate prediction of key parameters in tobacco primary processing plays a key role in its precise optimization and control. Existing prediction methods usually do not consider time dynamic characteristics, and the performance of multi-step prediction is not good, which cannot meet the actual index needs of tobacco primary processing. In response to the above problems, a multi-step prediction method for key parameters of tobacco primary processing based on the time-varying attention-temporal convolutional network (TVA-TCN) is proposed. Firstly, for the key information in the input variable
Because of the traditional ant colony algorithm based on positive feedback search way, lead to the existence of slow convergence speed and shortcoming of easily trapped in local minima. This paper propose a kind of extended ant colony algorithm based on mixed feedback mechanism(MF-ACO), the algorithm on the basis of traditional ant colony algorithm, define a kind of extension type ants which have strong global search ability, help algorithm out of local minima; In addition, the negative feedback balance mechanism based on stimulus-response model, the convergence ability and global search ab
Considering the high computational cost in multi-objective simulation optimization and the difficulty of obtaining black box function, a multi-objective parallel surrogate-based optimization method based on the dual weighted constraint expectation improvement strategy is proposed. Firstly, the Kriging model is established to estimate the prediction uncertainty of untested points. Secondly, the dual weighted constraint expectation improvement strategy is constructed, and the new strategy is integrated by the infill strategy matrix and distance aggregation method. Then, the integration strate