A PID controller design method based on probabilistic robustness was presented for thermal process with varied parameters.
针对具有实参数不确定性的热工过程，基于概率鲁棒方法，提出一种PID控制器设计方法。
This paper designed a control model, which is constructed based on CMAC neural network and combined with a PID controller.
提出利用CMAC神经网络与PID的复合控制，实现非线性系统控制。
The control system model consists of control strategy, sensors and actuators sub models. The control strategy is a PID controller.
控制系统模型包括控制策略、传感器和执行器模型，本文选用的控制策略为PID调节器。
In order to achieve the best characteristics of the setting value tracking and the disturbance rejection, a PID controller optimization design method is put forward.
为使PID控制器同时具有给定值跟踪和抑制干扰两个方面的最佳特性，提出了PID控制器优化设计方法。
The computer simulation results show that the neural network supervisory control has more adaptive ability and robust than that of a PID controller, and the control precision is increased.
对压水堆功率控制过程进行了计算机仿真，仿真结果表明，与传统的PID控制相比，神经网络监督控制具有较强的自适应能力和鲁棒性，有效地提高了控制系统的精度。
According to the demands of ball mill coal pulverized system, inverse matrix decoupling theory, a traditional decoupling method, is used in the system control, and a PID controller is designed.
本文首先根据中储式球磨机制粉系统对控制精度要求不高的特点，将传统解耦方法中的逆矩阵方法应用于制粉系统解耦中，设计了基于逆矩阵静态解耦的PID控制系统。
According to the demands of ball mill coal pulverized system, inverse matrix decoupling theory, a traditional decoupling method, is used in the system control, and a PID controller is designed.
本文首先根据中储式球磨机制粉系统对控制精度要求不高的特点，将传统解耦方法中的逆矩阵方法应用于制粉系统解耦中，设计了基于逆矩阵静态解耦的PID控制系统。
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