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.
In view of the insufficient accuracy of the orbital state prediction method based on the physical model in the space surveillance environment, and the insufficient reliability of the error compensation model based on machine learning, as well as the demand for uncertainty modeling in the SSA application, we reformulate the orbital state prediction error estimation problem as a probability prediction problem, and propose a method of using a gradient boosting machine to model the orbital state prediction error distribution. In order to quantify the uncertainty in the state error estimation, t
Image completion is an important research content in the field of digital image processing, and the completion of large area irregular missing images is a research hotspot in recent years. However, the existing image completion technology has some limitations. The method based on generative adversarial network ignores the edge structure information of the image, and it can"t restore the fine details. The method based on local discriminator can"t deal with the missing irregular image, and the completion task doesn"t conform to the actual application scene. Combined with the id
The sketch person re-identification requires to search for pedestrians with the same identity as the given sketch image in the color image gallery. Due to the difference of posture and viewpoint between the sketch image and the color image, the two images from two different modes often have different semantic information in the same spatial position, which leads to the lack of robustness of the extracted features. Previous studies focus on pedestrian feature extraction modal-invariant information, but ignore the issue of semantic misalignment between lean and different modals to features in
This paper proposes an adaptive optimal control method for active suspension systems subject to unknown dynamics and multiple performance indices. dynamics. Then, a cost function concerning the ride comfort, suspension stroke and control input is established, which aims at achieving a compromise between the performance indices. Furthermore, a single layer neural network (NN) is used to estimate the optimal, cost f by which the Hamiltonian function can be derived. To obtain the online solution, a novel adaptive law driven by the parameter estimation error is developed to update the unknown N
This paper presents a distributed multi-objective model predictive control (MPC) strategy for multi-objective control problems of nonlinear vehicle platoon systems subject to state and control constraints. Based on the predecessor-follower communication topology, the nonlinear longitudinal cruise control models of connected vehicle platoons are established. The lexicographic method is applied to formulate the distributed multi-objective optimization problem. Moreover, together with the triple elements of MPC, the constraints on string stability and contractive are designed to guarantee stab