航天器输入受限的鲁棒自适应姿态跟踪控制

研究含模型不确定性的刚性航天器输入受限时的姿态跟踪控制设计问题.针对修改的罗德里格斯姿态参数描述的航天器姿态跟踪动力学模型,基于一种有界非线性连续函数和修改的罗德里格斯姿态参数自身有界性,设计鲁棒自适应状态反馈受限控制器,不确定参数的自适应更新律可保证在线估计参数的有界性.通过所提出的输入受限控制设计方法给出输入受限幅...

Robust adaptive attitude tracking control of spacecraft with constrained inputs

Aiming at the dynamic optimization of structure and parameters of the radial basis function(RBF) neural network, an optimization algorithm based on sensitivity analysis(SA) and particle swarm optimization(PSO) for the RBF neural network(SAPSO-RBF) is proposed. Firstly, the number of particle information is randomly initialized, and the particle information is added and deleted by the sensitivity analysis in the learning phase, and the network structure of the algorithm in first convergence is determined. Then, after the algorithm reaches convergence, we analyzes the sensitivity of the optimal particles, deletes the redundant information, and makes the algorithm re-divergent. An inertia weight update method is proposed to make the algorithm perform multiple divergence and convergence in the solution space, which enhances algorithm search ability while reducing network structure, and the convergence of SAPSO algorithm is proved. Finally, the results of experiments show that the proposed SAPSO-RBF algorithm has good self-organizing ability and has greatly improved the network structure compactness and accuracy compared with some other existing methods.