抗干扰控制在控制理论与应用中具有重要的理论意义和应用价值。现有半马尔科夫跳变（随机）系统/马尔科夫跳变随机系统的理论成果少有考虑多源干扰的情形。特别地，当半马尔科夫跳变随机系统受到多源干扰时，单一控制器保守性比较大，难以实现高精度控制，其瓶颈在于需要将不同通道的干扰进行解耦分离并抑制抵消；再加上跳变参数的影响，将导致闭环系统结构复杂，单一控制器保证的系统稳定性和干扰抑制性能不再成立。本项目旨在建立一套完整的多源干扰半马尔科夫跳变随机系统精细抗干扰控制器设计方法的理论体系，主要研究工作包括：首先充分考虑驻留时间和转移概率信息，建立稳定性分析及精细抗干扰控制理论；然后通过构造基于状态、基于输出、以及干扰存在不确定参数时的干扰观测器，提出面向控制性能的基于干扰观测器的精细抗干扰控制器设计新方法，进而解决控制输入受限时的精细抗干扰控制问题；最后，将部分理论成果应用到列车运行控制和无人机姿态控制中。

Anti-disturbance control is of important theoretical significance and application value in the control theory and applications. Few considerations had been put on the influence of multiple disturbances in the state-of-the-art theoretical results of semi-Markovian jump stochastic system or semi-Markovian jump system or Markovian jump stochastic system. Particularly, when the semi-Markovovian jump stochastic system is subject to multiple disturbances, traditional single anti-disturbance control methods are too conservative to achieve high control accuracy. The bottleneck is that, it is essential to decouple and separate the disturbances in different channels and meanwhile attenuate and reject the multiple disturbances. Furthermore, taking the jumping parameters into account would lead to a more complicated structure of the close-loop control system, which cannot be satisfied by a single controller in terms of system stability and disturbance attenuation performance. This project aims at establishing a systemic and complete theoretical framework for refined anti-disturbance controller design of semi-Markovian jump stochastic system under multiple disturbances. The main research work are as follows: firstly, study the stability analysis and refined anti-disturbance control theory based on the through consideration of the information of the sojourn time and transition probability; then, by constructing state-based disturbance observer (DO), output-based DO, and DO for the disturbance with uncertain parameters, we propose new control performance oriented disturbance-observer-based refined anti-disturbance control methodologies, and further solve the refined anti-disturbance control problem when control input is constrained; finally, we apply parts of the developed theoretical results into practical systems, such as train operation control and unmanned aerial vehicle attitude control.