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Research on Pressurizer Pressure Control System Based on BP Neural Network Control of Self-Adjusted PID Parameters

机译:基于BP神经网络控制的自调节PID参数的加压器压力控制系统研究

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The pressurizer is an important device in nuclear reactor system, and the traditional PID regulator is usually used to control pressure system of pressurizer in modern reactors. However, it is difficult to get precise parameters of traditional PID controller, and the PID control method is relied on the precise mathematical model badly. And the response of PID controller is often shown by the large amount of overshoot and long setting time which are not the desired results. For such a large inertia and complex time-varying control system, the tradition PID controller can not obtain the satisfy control results. A controller based on BP neural network in this paper has a simple structure, and the parameters of PID controller can be tuned on-line by the neural network self-learning characteristics. The computer simulation experiment demonstrates that the BP neural network PID controller performs very well when compared with the tradition PID regulator in minimal overshoot and more quick response.
机译:加压器是核反应堆系统中的重要装置,传统的PID调节器通常用于控制现代反应堆中加压器的压力系统。但是,难以获得传统PID控制器的精确参数,并且PID控制方法严重依赖于精确的数学模型。并且PID控制器的响应通常由大量的过冲和长凝固时间所示,这不是所需的结果。对于这种大型惯性和复杂的时变控制系统,传统PID控制器无法获得满足的控制结果。本文基于BP神经网络的控制器具有简单的结构,并且PID控制器的参数可以通过神经网络自学习特性在线调谐。计算机仿真实验表明,与传统PID调节器相比,BP神经网络PID控制器在最小的过冲和更快速的响应中相比。

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