This article proposes a time-synchronized constrained controller to address the time-varying constrained reorientation problem in spacecraft attitude operations. The characteristics of time-varying attitude constraints are formulated and analyzed. The corresponding artificial potential field-based sliding mode surface is tailored for these constraints. This method utilizes the properties of time-synchronized stability to thoroughly contemplate the interrelationships among different state errors, enabling the synchronized convergence of individual error components while ensuring compliance with state constraints. This design leads to an increase in control efficiency compared with traditional sliding mode-based constrained attitude controllers without adding any computational burden. This efficiency is evident in terms of control energy consumption and convergence time. Theoretical analysis is presented to substantiate the rationality of the proposed method. To validate these advantages, the article conducts diverse numerical simulation analyses and designs corresponding hardware-in-the-loop (HiL) experiments to verify its practical deployment capabilities.

This article is devoted to real-time optimal attitude reorientation control of rigid spacecraft control. Particularly, two typical practical problems—actuator misalignment and forbidden pointing constraints are considered. Within the framework of adaptive dynamic programming (ADP), a novel constrained optimal attitude control scheme is proposed.In this design, a special reward function is developed to characterize the environment feedback and deal with the pointing constraints. Notably, a novel argument term is introduced to the reward function for overcoming the inevitable difficulty in the actuator misalignment. By virtue of the Lyapunov stability theory, the ultimate boundedness of state error and the optimality of the proposed method can be guaranteed. Finally, the effectiveness and performance of the developed ADP-based controller are evaluated by not only numerical simulations but also experimental tests with a hardware-in-loop platform.

This article proposes areinforcement learning (RL)-based six-degree-of-freedom (6-DOF) control scheme for the final-phase proximity operations of spacecraft. The main novelty of the proposed method are from two aspects: 1) The closed-loop performance can be improved in real-time through the RL technique, achieving an online approximate optimal control subject to the full 6-DOF nonlinear dynamics of spacecraft; 2) nontrivial motion constraints of proximity operations are considered and strictly obeyed during the whole control process. As a stepping stone, the dual-quaternion formalism is employed to characterize the 6-DOF dynamics model and motion constraints. Then, an RL-based control scheme is developed under the dual-quaternion algebraic framework to approximate the optimal control solution subject to a cost function and a Hamilton–Jacobi–Bellman equation. In addition, a specially designed barrier function is embedded in the reward function to avoid motion constraint violations. The Lyapunov-based stability analysis guarantees the ultimate boundedness of state errors and the weight of NN estimation errors. Besides, we also show that a PD-like controller under dual-quaternion formulation can be employed as the initial control policy to trigger the online learning process. The boundedness of it is proved by a special Lyapunov strictification method. Simulation results of prototypical spacecraft missions with proximity operations are provided to illustrate the effectiveness of the proposed method.

This article addresses the crucial requirements in spacecraft attitude control: prescribed performance guarantees under actuator saturation and real-time cost optimization. As an application-oriented study, an approximate optimal prescribed performance attitude control scheme is proposed for this objective. To be specific, the prescribed performance constraint is converted into the system dynamics and merged into the adaptive dynamic programming design philosophy. Subsequently, the online learning law is designed based on a special saturated Hamilton–Jacobi–Bellman error, in which a dynamical scale is introduced to adjust the learning gain by measured data. It enhances learning efficiency and applicability. Then, uniformly ultimately bounded stability of the whole system is achieved with guaranteed convergence of optimization by the Lyapunov-based stability analysis. Finally, both numerical simulation and hardware-in-the-loop experiments demonstrate the superiority and effectiveness of the proposed method. These attributes and outcomes attained will promote the development of practical space missions.

This paper proposes a learning-based robust control scheme for spacecraft proximity operations under motion constraints (i.e., approaching path and sensor field-of-view constraints), input saturation, and external disturbances. To enhance the robustness of the learning algorithm, a disturbance observer with finite-time convergence is first designed to provide accurate model information for online learning. By virtue of the barrier functions and hyperbolic tangent functions, a performance index is developed, which incorporates both motion and input constraints into the framework of adaptive dynamic programming (ADP). Then, following the actor-critic structure, an approximate optimal saturated control policy is obtained using two neural networks (NNs), wherein the weights of the NNs are updated online. It is shown that the derived controller can guarantee the boundedness of system states and network weight estimation errors, while ensuring the satisfaction of motion and input constraints despite the presence of external disturbances. Finally, numerical simulations are carried out for spacecraft proximity operations with a tumbling target to verify the effectiveness of our proposed method.

This paper aims to handle the optimal attitude tracking control tasks for rigid bodies via a reinforcement learning-based control scheme, in which a constrained parameter estimator is designed to compensate system uncertainties accurately. This estimator guarantees the exponential convergence of estimation errors and can strictly keep all instant estimates always within pre-determined bounds. Based on it, a critic-only adaptive dynamic programming (ADP) control strategy is proposed to learn the optimal control policy with respect to a user-defined cost function. The matching condition on reference control signals, which is commonly employed in relevant ADP design, is not required in the proposed control scheme. We prove the uniform ultimate boundedness of the tracking errors and critic weight's estimation errors under finite excitation conditions by Lyapunov-based analysis. Moreover, an easy-to-implement initial control policy is designed to trigger the real-time learning process. The effectiveness and advantages of the proposed method are verified by both numerical simulations and hardware-in-loop experimental tests.