For the challenging problem that a spacecraft approaches a tumbling target with non-cooperative maneuver, an anti-saturated proximity control method is proposed in this paper. First, a brand-new appointed-time convergent performance function is developed via exploring Bézier curve to quantitatively characterize the transient and steady-state behaviors of the pose tracking error system. The major advantage of the proposed function is that the actuator saturation phenomenon at the beginning can be effectively reduced. Then, an anti-saturated pose tracking controller is devised along with an adaptive saturation compensator. Wherein, the finite-time stability of both the pose and its velocity error signals are guaranteed simultaneously in the presence of actuator saturation. Finally, 2 groups of illustrative examples are organized and verify that the close-range proximity is effectively realized even with unknown target maneuver.

In froth flotation, the tailings grade and concentrate grade are the two key performance indexes. At present, the monitoring models of these two key grades mostly use the froth image or video from a flotation cell. However, flotation cells are closely related and coupled seriously. It is difficult to use a froth image or video from a flotation cell to represent the concentrate or tailings grade. Therefore, an encoder–decoder and Siamese time series network (ES-net) is proposed. First, an encoder–decoder (ED) model is designed to predict target grade (i.e., the zinc tailings or concentrate grade) by the video feature sequence of the first rougher and the measured target grade sequence. Meanwhile, a Siamese time series and difference network (STS-D net) is constructed to predict the target grade by the video feature sequences of target flotation cell (i.e., the last scavenger or cleaner) at current and previous moments and the previously measured target grade. After that, a multitask learning strategy is proposed to integrate the ED model and STS-D net. Experiments show that the proposed ES-net can effectively integrate multiple froth visual features from different flotation cells and obtain more accurate concentrate and tailings grades than the existing models.

In this paper, a novel adaptive model-free attitude tracking control method is investigated for rigid spacecraft with consideration of the external disturbance, unknown inertia matrix, and input saturation. First, the considered attitude tracking system with input saturation is transformed into a Lagrangian model, and a dead zone–based model is used to describe the saturation nonlinearity. Second, using the prescribed performance control theory, a static prescribed performance attitude control scheme is presented, by which the transient and steady-state performance (including the convergence rate, overshoot, and boundedness) of the attitude tracking system is proved to be guaranteed. Third, in order to improve the performance of the static prescribed performance control scheme, a novel learning-based supplementary control scheme is presented based on the approximate dynamic programming. Finally, two groups of numerical simulations are used to illustrate the effectiveness of the proposed learning-based prescribed performance attitude control method.