不确定性系统的非因果鲁棒学习控制

针对不确定性系统提出一种非因果鲁棒学习控制方法。该学习控制律的非因果学习部分通过标称系统的优化指标得到，鲁棒部分通过设计鲁棒加权来实现。首先，不考虑鲁棒部分的具体形式，推导出标称系统描述的学习控制律的鲁棒收敛性条件；然后，设计与系统不确定性相关的鲁棒加权，由鲁棒收敛性条件得到鲁棒加权的设计原则；最后，通过仿真实验验证了所提出方法的有效性，并分析了不同形式不确定性系统鲁棒设计的保守性。

Non-causal robust learning control for uncertain systems

A non-causal robust learning control method is presented for uncertain systems. According to the optimal index of the nominal system, the non-causal learning part of the method is developed, and the robust part is determined by designing robust weightings. Firstly, robust convergence conditions of learning control laws based on the nominal system are derived when specific forms of the robust part are not considered. Then, robust weightings which are related to uncertainties of the system are designed, and the method for designing robust weightings is drawn according to the robust convergence condition. Finally, simulations are implemented for the validating effectiveness of the method, and the conservatism of robust design of different uncertain systems is analyzed.