高速动车组多模型切换主动容错预测控制

高速动车组持续高速运行,对控制系统的可靠性和抗干扰能力提出了更高要求.结合高速动车组非线性动力学特点和系统运行数据,应用减法聚类和模式分类算法建立高速动车组多模型集;为适应对象和扰动特性的变化建立高速动车组自适应模型;采用基于累计误差最小的切换策略在线选择最优控制模型,据此设计主动容错预测控制算法来实现高速动车组安全高效运行.最后,仿真实例验证了该方法的有效性.

Multi-model switching predictive control with active fault tolerance for high-speed train

In the continuous high-speed operation, severe demands on reliability and disturbance-rejection are needed by the high speed trains. According to its nonlinear dynamic characteristics and operation data, we build a set of multiple models for the high-speed train by using subtractive clustering and pattern classification algorithm. To adapt to the change of object and disturbance characteristics, we use a model switching scheme to select online, from this set of models, the optimal model with smallest model accumulative error. On the basis of this optimal model, we design the active fault tolerant predictive controller to realize the secure and efficient operations of the high-speed train. Simulation example is given to show the effectiveness of this method.