Earth observation satellite task scheduling research plays a key role in space-based remote sensing services. An effective task scheduling strategy can maximize the utilization of satellite resources and obtain larger objective observation profits. In this paper, inspired by the success of deep reinforcement learning in optimization domains, the deep deterministic policy gradient algorithm is adopted to solve a time-continuous satellite task scheduling problem. Moreover, an improved graph-based minimum clique partition algorithm is proposed for preprocessing in the task clustering phase by considering the maximum task priority and the minimum observation slewing angle under constraint conditions. Experimental simulation results demonstrate that the deep reinforcement learning-based task scheduling method is feasible and performs much better than traditional metaheuristic optimization algorithms, especially in large-scale problems.

Aiming at solving the model-free fault-tolerant spacecraft attitude control problem, a data-driven adaptive control scheme is proposed to the spacecraft in the presence of actuator faults and performance constraints. First, the discrete-time attitude dynamic model is transformed into an affine nonlinear system based on local dynamic linearization. Then, a fuzzy logic-based lazily adapted constant kinky inference rule is introduced to predict the arbitrarily continuous nonlinear actuator faults and model uncertainties by supervised learning with insufficient prior knowledge. To satisfy time-varying deferred asymmetric constraints of the attitude tracking error, a virtual control law is proposed in the attitude control loop using the back-stepping approach, which is derived from a deferred switching transformation and barrier Lyapunov function. The stability of the data-driven adaptive fault-tolerant attitude control for the nonlinear discrete-time spacecraft attitude dynamics is analyzed rigorously with the aid of contraction mapping principle and discrete-time Lyapunov theory. Compared with existing methods, the proposed one considers nominal non-global Lipschitz nonlinear system with arbitrarily continuous unmodeled uncertainties and time-varying actuator faults, and achieves smaller prediction error bound than general kinky inference scheme and better closed-loop performance by estimating and compensating unknown dynamics. Finally, numerical simulation verifies the effectiveness of the proposed control scheme.

This paper presents a space mission planning tool, which was developed for LEO (Low Earth Orbit) observation satellites. The tool is focused on a two-phase planning strategy with clustering preprocessing and mission planning, where an improved clustering algorithm is applied, and a hybrid algorithm that combines the genetic algorithm with the simulated annealing algorithm (GA–SA) is given and discussed. Experimental simulation studies demonstrate that the GA–SA algorithm with the improved clique partition algorithm based on the graph theory model exhibits higher fitness value and better optimization performance and reliability than the GA or SA algorithms alone.

In this paper, for an Low-Earth Orbit (LEO) satellite network with inter-satellite links, a routing optimization method is developed in the case of stochastic link failure. First, a discrete-time strategy is used for the satellite network to acquire several static topological graphs during a cycle. Based on the static topological graphs regarding stochastic link failure, a constraint model is established that constructs the task revenue, switching times and routing cost as indicators. Then, an improved Genetic Algorithm based on A* is proposed to optimize the topology under the constraint model. In particular, to reduce the cost of computation, a new generation strategy for the initial solution is presented which combines the roulette wheel operator and the A* algorithm. Finally, the effectiveness of the proposed method is illustrated by a group of numerical simulations for the network with stochastic link failure.

In this paper, for multi-spacecraft systems (MSSs) with a directed complete communication topology and a time-varying virtual leader, an adaptive saturated attitude controller is proposed to achieve attitude consensus and attitude tracking under arbitrary initial attitude, mixed attitude constraints, input saturation and external disturbances. Firstly, considering the time-varying desired attitude provided by the virtual leader in a directed complete topology, an MSS attitude error function and an MSS attitude error dynamics based on SO(3) are developed. Next, an effective mixed potential function for the MSS on SO(3) is proposed for the static attitude-forbidden zones, the relative dynamic attitude-forbidden zones and the attitude-mandatory zones. In particular, different from the existing potential functions, the proposed mixed potential function is suitable for arbitrary initial attitude of the spacecraft in MSS, relaxing the restriction on the initial attitude associated with each static and dynamic attitude constraint zones. Then, an adaptive saturated attitude controller is designed to realize attitude consensus and tracking for the MSSs on SO(3) under arbitrary initial attitude, mixed attitude constraints, saturation constraints and external disturbances. Finally, simulation results of an MSS with a time-varying virtual leader are demonstrated to illustrate the efficiency of the proposed attitude controller.