Browsing by Author "Chen, Kaiyuan"

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    Adaptive multivariate reusable launch vehicles reentry attitude control with pre-specified performance in the presence of unmatched disturbances
    (Elsevier, 2024-01-11) Ge, Tianshuo; Chai, Runqi; Zhu, Qinghua; Chen, Kaiyuan; Tsourdos, Antonios; Farhan, Ishrak M. D.
    This paper introduces a novel adaptive multivariable attitude control method for a reusable launch vehicle (RLV) to track desired attitude trajectories in the presence of unknown external disturbances and uncertainties. Unlike most existing designs that overlook mismatched disturbances, this method employs an adaptive finite-time observer (AFO) to estimate the unknown states. Based on the outputs of the AFO and the prescribed performance function, a time-varying adaptive gain that is not overestimated is designed to establish the adaptive multivariable attitude control for the RLV system. The simulations demonstrate that the proposed approach successfully guides the RLV to follow desired attitude signals despite the presence of unmatched disturbances and uncertainties.
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    Efficient and near-optimal global path planning for AGVs: a DNN-based double closed-loop approach with guarantee mechanism
    (Institute of Electrical and Electronics Engineers (IEEE), 2024) Zhang, Runda; Chai, Runqi; Chen, Kaiyuan; Zhang, Jinning; Chai, Senchun; Xia, Yuanqing; Tsourdos, Antonios
    In this article, a novel global path planning approach with rapid convergence properties for autonomous ground vehicles (AGVs) named neural sampling rapidly exploring random tree (NS-RRT*) is proposed. This approach has a three-layer structure to obtain a feasible and near-optimal path. The first layer is the data collection stage. Utilizing the target area adaptive rapidly exploring random tree (TAA-RRT*) algorithm to establish a collection of paths considering the initial noise disturbance. To enhance network generalization, an optimal path backward generation (OPBG) strategy is introduced to augment the dataset size. In the second layer, the deep neural network (DNN) is trained to learn the relationships between the states and the sampling strategies. In the third layer, the trained model is used to guide RRT* sampling, and an efficient guarantee mechanism is also designed to ensure the feasibility of the planning task. The proposed algorithm can assist the RRT* algorithm in efficiently obtaining optimal or near-optimal strategies, significantly enhancing search efficiency. Numerical results and experiments are executed to demonstrate the feasibility and efficiency of the proposed method.