Abstract: Cascade control is a frequently used control strategy in industrial processes. It can reduce the maximum deviation and the integral error compared with the single loop. Currently, the minimum variance method of parallel cascade control system are developed based on the assumption that all the disturbance are subject to Gaussian distribution. However, in the practical condition, some disturbances do not obey the Gaussian distribution. The minimum entropy index of performance assessment of the parallel cascade control system subjected to non-Gaussian disturbances is proposed. In the stochastic process, the information entropy has more general significance than mean or variance for any random variable. The estimated ARMA model for the parallel cascade control loop based on the minimum entropy instead of the minimum mean squares error has better performance for non-Gaussian disturbances. The result of proposed methods are demonstrated through a simulation example. 

Key words:

Parallel cascade control, performance assessment, minimum entropy, non-Gaussian

摘要： 串级控制在工业过程中是经常被采用的控制策略,与单回路相比它能够减小最大偏差和积分误差。当前的最小方差性能评估方法都是基于高斯扰动，但在实际生产中，一些扰动并不一定服从高斯分布。对于非高斯的扰动提出基于最小熵的性能评估基准。在随机过程中,信息熵相比均值或者方差对于任意的随机变量具有更为一般的意义。在非高斯噪声的并行串级控制系统中,辨识系统的ARMA模型根据最小熵而不是最小均方误差将更为合适。通过在仿真证明该指标的有效性。

关键词: 并行串级控制, 性能评估, 最小熵, 非高斯