The problem of optimal attitude stabilization control of rigid spacecraft despite external disturbances is investigated. An online reinforcement learning-based intelligent and robust control approach is presented via the adaptive dynamic programming technique. In this approach, a critic-only neural network is developed to learn the optimal control policy of the spacecraft attitude system with external disturbance. A new estimation law is synthesized to estimate the weights of that network online. The learned controller can achieve near-optimal control performance. Then, a robust control effort is designed and added into the learned controller to formulate an intelligent and robust controller. It is proven that the closed-loop attitude system obtained from the proposed controller is uniformly ultimately bounded and that the weight estimation error of the Critic NN is convergent by Lyapunov theory. Comparison with the traditional actor-critical neural network-based control schemes shows that with less computation complexity and great robustness to external disturbances, the proposed control approach is less dependent of the persistent excitation condition. Simulation results verify the superior control performance of the proposed approach.

Probabilistic swarm guidance enables autonomous microsatellites to generate their individual trajectories independently so that the entire swarm converges to the desired distribution shape. However, it is essential to avoid crowding for reducing the possibility of collisions between microsatellites. To determine the collision-free guidance trajectory of each microsatellite from the current position to the target space, a collision avoidance algorithm is necessary. A synthesis method is proposed that generate the collision avoidance trajectories. The idea is that the trajectory planning is divided into macro-planning and micro-planning; macro-planning guides where the microsatellites move step by step from the initial cube to the target cube by probabilistic swarm guidance with Centroidal Voronoi tessellation, while the micro-planning is to generate the optimal path for each step and finally reach the specified position in the target cube by model predictive control. Simulation results are presented for the collision-free guidance trajectory of microsatellites to verify the benefits of this planning scheme.

Navigation and positioning is an important and challenging problem in many control engineering applications. It provides feedback information to design controllers for systems. In this paper, a bibliographical review on factor graph based navigation and positioning is presented. More specifically, the sensor modeling, the factor graph optimization methods, and the topology factor based cooperative localization are reviewed. The navigation and positioning methods via factor graph are considered and classified. Focuses in the current research of factor graph based navigation and positioning are also discussed with emphasis on its practical application. The limitations of the existing methods, some solutions for future techniques, and recommendations are finally given.