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This paper focuses on the development of a guidance methodology for the planning of multiple ground target acquisition. Specifically, the work addresses the problem of the lack of an attitude guidance planner (AGP) aboard a remote sensing satellite. In general, the guidance is computed offline and uploaded by ground control to the space segment, i.e., satellites are not responsible for the guidance generation but they only perform control algorithms to track the guidance profiles provided by the ground segment. Overall, this limits the mission flexibility and efficiency, affecting the capability of autonomous satellite decisions. This choice is driven by the fact that the numerical algorithms used to optimize the attitude guidance trajectory require high computational effort to be implemented directly on the satellite computer. Therefore, the aim of this work is to design an analytical AGP solution to solve this problem by requiring low computational effort, making it suitable for real-time applications on on-board flight hardware. In this way, the satellite's guidance, navigation, and control (GNC) module would become completely autonomous and independent of ground control, which will only have to indicate the targets to be acquired so that the satellite can generate its own guidance for the GNC module. The AGP analytical solution for multiple ground target acquisition is evaluated by means of phases: the first phase is named the APPG (attitude point-to-point generator) and it aims to generate the point-to-point guidance to start the ground target acquisition. The second phase is named the ATPG (attitude target pointing generator) and it generates the reference guidance to maintain the payload view axis pointing toward the ground target. The two phases joined together give the whole guidance needed to observe ground target points by means of an analytical closed-form solution. [ABSTRACT FROM AUTHOR]

Research on trajectory tracking is crucial for the development of autonomous vehicles. This paper presents a trajectory tracking scheme by utilizing model predictive control (MPC) and preview-follower theory (PFT), which includes a reference generation module and a MPC controller. The reference generation module could calculate reference lateral acceleration at the preview point by PFT to update state variables, and generate a reference yaw rate in each prediction point. Since the preview range is increased, PFT makes the calculation of yaw rate more accurate. Through physical constraints, the MPC controller can achieve the best tracking of the reference path. The MPC problem is formulated as a linear time-varying (LTV) MPC controller to achieve a predictive model from nonlinear vehicle dynamics to continuous online linearization. The MPC-PFT controller method performs well by increasing the effective length of the reference path. Compared with MPC and PFT controllers, the effectiveness and robustness of the proposed method are proved by simulations of two typical working conditions. [ABSTRACT FROM AUTHOR]