逻辑自动机下的可预测性分析趋于保守,通常在实际系统中应用受限。该文研究基于随机自动机的故障预测问题。对于每一个正常的系统状态,应用吸收概率理论计算其转移到故障状态的概率和平均时间。根据系统事件的可观测性构造诊断器,确定系统可能处于的状态集合。基于观测序列,确定系统状态分布,通过概率加权计算系统转移到故障状态的概率和平均时间。应用HVAC(heating,ventilation and air conditioning)系统的仿真实例验证算法的有效性。结果表明:该方法能够预测不同观测序列下系统发生故障的概率和平均时间。此外,对于逻辑不可预测系统,该方法依然适用。
This paper investigates the minimal-energy driving problem for high-speed electric train, and then proposes a three-stage optimal strategy. First, a switching system model is introduced to describe the new dynamics in high-speed electric train, which considers the extended range of speed, the energy efficiency and the regenerative brake. Based on the new model, the optimal driving strategy with minimal-energy consumption is studied, and the problem is boiled down to optimal control for switching systems. Using a numerical algorithm, a three-stage driving strategy is concluded, in which the traditional quasi-coasting stage is discarded and the maximal traction and brake are not suitable anymore. Finally, a case study on CRH is illustrated.
