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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. [ABSTRACT FROM AUTHOR]

This article 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 predetermined 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-the-loop experimental tests. [ABSTRACT FROM AUTHOR]