This paper addresses a geometric control algorithm for the attitude tracking problem of the rigid spacecraft modeled on SO(3).Considering the topological and geometric properties of SO(3),we introduced a smooth positi...This paper addresses a geometric control algorithm for the attitude tracking problem of the rigid spacecraft modeled on SO(3).Considering the topological and geometric properties of SO(3),we introduced a smooth positive attitude error function to convert the attitude tracking issue on SO(3)into the stabilization counterpart on its Lie algebra.The error transformation technique was further utilized to ensure the assigned transient and steady state performance of the attitude tracking error with the aid of a well⁃designed assigned⁃time performance function.Then,using the actor⁃critic(AC)neural architecture,an adaptive reinforcement learning approximator was constructed,in which the actor neural network(NN)was utilized to approximate the unknown nonlinearity online.A critic function was introduced to tune the next phase of the actor neural network operation for performance improvement via supervising the system performance.A rigorous stability analysis was presented to show that the assigned system performance can be achieved.Finally,the effectiveness and feasibility of the constructed control strategy was verified by the numerical simulation.展开更多
基金the National Natural Science Foundation of China(Grant Nos.62103171,61773142)the Natural Science Foundation of Fujian Province of China(Grant Nos.2020J05095,2020J05096)the Jiangsu Provincial Double⁃Innovation Doctor Program(Grant Nos.JSSCBS20210993,JSSCBS20211009)。
文摘This paper addresses a geometric control algorithm for the attitude tracking problem of the rigid spacecraft modeled on SO(3).Considering the topological and geometric properties of SO(3),we introduced a smooth positive attitude error function to convert the attitude tracking issue on SO(3)into the stabilization counterpart on its Lie algebra.The error transformation technique was further utilized to ensure the assigned transient and steady state performance of the attitude tracking error with the aid of a well⁃designed assigned⁃time performance function.Then,using the actor⁃critic(AC)neural architecture,an adaptive reinforcement learning approximator was constructed,in which the actor neural network(NN)was utilized to approximate the unknown nonlinearity online.A critic function was introduced to tune the next phase of the actor neural network operation for performance improvement via supervising the system performance.A rigorous stability analysis was presented to show that the assigned system performance can be achieved.Finally,the effectiveness and feasibility of the constructed control strategy was verified by the numerical simulation.
