Search

This paper proposes a prescribed performance control policy for spacecraft attitude tracking control. The designed controller employs a kind of performance envelope function that can configure the convergence rate arbitrarily. Moreover, the controller design is based on the barrier Lyapunov function and indirect adaptive methods, which can suppress the fluctuation of external disturbances to obtain lower steady-state errors. Compared to prior studies on spacecraft attitude tracking, the proposed policy can arbitrarily configure the system convergence performance to achieve performance trade-offs in different scenarios. Moreover, the barrier Lyapunov function design and adaptive methods can achieve low steady-state errors with low computational resource consumption. Simulations that verify its effectiveness and superiority are provided herein. [ABSTRACT FROM AUTHOR]

Perovskite solar cells (PSCs) have experienced exceptional development in recent years, due to their outstanding photoelectronic properties and low‐cost solution processing. Many state‐of‐the‐art PSC designs have been effectively demonstrated using a stacked 3D perovskite/2D perovskite heterostructure, yet limitations arise due to the low conductivity of the 2D perovskite, the hidden buried interface of 3D perovskite, and halide ion migration within 3D/2D PSC device under operational bias. Here, these limitations are overcome by developing a novel and universal post‐synthetic metal (Zn2+) doping strategy and realizing 3D/2D PSCs with superior efficiency and stability. Informed by ab initio calculations and synchrotron fine structure experiments, it is revealed that the introduced zinc ions are energetically favored at interstitial crystal sites, subsequently hindering the migration of halide ions and producing a beneficial shift toward a more n‐type character in the buried 3D perovskite interface. Combined with extensive photophysical characterization, the Zn2+‐modified 3D/2D perovskite thin film is shown to strongly recover its photo‐carrier conductivity compared with the 3D/2D perovskite film, boosting the efficiency (22.90%) of PSCs while exhibiting improved humidity and operational stability. [ABSTRACT FROM AUTHOR]

Summary: This article investigates the issue of orbit coordination control for a class of multi‐spacecraft formation systems in presence of limited communication and external disturbance. To solve the limitation of communication sources, a dynamic event trigger (DET) mechanism is developed to reduce the communication frequency between the follower spacecrafts. Subsequently, we explore a robust DET mechanism‐based distributed self‐learning sliding mode control design, in which a variable learning intensity‐based iterative learning algorithm is designed to approximate and compensate space perturbation. This approach can guarantee an event triggering sequence without Zeno phenomenon and accurate coordination control for formation configuration simultaneously. Compared with the traditional event‐triggered control and other state‐of‐the‐art approaches, the distributed DET control scheme achieves higher control accuracy of formation configuration meanwhile requires less communication resource. Finally, a series of numerical simulations demonstrate the feasibility and superiority of the event triggered control method. [ABSTRACT FROM AUTHOR]

The modular robotic arms can achieve desired performances in different scenarios through the combination of various modules, and concurrently hold the potential to exhibit geometric symmetry and uniform mass symmetry. Therefore, selecting the appropriate combination of modules is crucial for realizing the functions of the robotic arm and ensuring the elegance of the system. To this end, this paper proposes a double deep Q-network (DDQN)-based configuration design algorithm for modular robotic arms, which aims to find the optimal configuration under different tasks. First, a library of small modules of collaborative robotic arms consisting of multiple tandem robotic arms is constructed. These modules are described in a standard format that can be directly imported into the software for simulation, providing greater convenience and flexibility in the development of modular robotic arms. Subsequently, the DDQN design framework for module selection is established to obtain the optimal robotic arm configuration. The proposed method could deal with the overestimation problem in the traditional deep Q-network (DQN) method and improve the estimation accuracy of the value function for each module. In addition, the experience replay mechanism is improved based on the SumTree technique, which enables the algorithm to make effective use of historical experience and prevents the algorithm from falling into local optimal solutions. Finally, comparative experiments are carried out on the PyBullet simulation platform to verify the effectiveness and superiority of the configuration design method developed in the paper. The simulation results show that the proposed DDQN-based method with experience replay mechanism has higher search efficiency and accuracy compared to the traditional DQN scheme. [ABSTRACT FROM AUTHOR]