PID、模糊和模型预测控制在实际非线性装置中的应用

K. Borgeest, P. Schneider
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引用次数: 0

摘要

针对具有m个控制变量和n≤m个修正变量的移动机床的冷却系统,研究了不同的控制策略,以期在充分冷却的情况下实现功率最小化、节能和降低风扇噪声。该植物是非线性的,无法识别。本文研究了三种不同类型的控制器,即模糊控制、PI(D)和模型预测控制(MPC)。14种不同的标准被用于评估。在许多方面,具有模糊预测的线性控制器被证明是最好的,特别是预测模型可以处理对象的非线性特性。具有未知对象的先进控制方案的一个问题是难以证明其稳定性。关键词:工程机械,去模糊化,风机控制,风机噪声,前馈,模糊控制,模糊化,Mamdani,模型预测控制,MPC,非线性系统,PI控制,PID控制
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PID, Fuzzy and Model Predictive Control Applied to a Practical Nonlinear Plant
For the cooling system of a mobile machine with m control variables and with n≤m correction variables different control strategies have been investigated in order to minimize power to save energy and to reduce fan noise with sufficient cooling. The plant is nonlinear and not identified. Three different kinds of controllers have been investigated in several variations, i.e. fuzzy control, PI(D) and model predictive control (MPC). 14 different criteria have been used for evaluation. In many respects a linear controller with fuzzy prediction proved best, in particular the prediction model can handle nonlinear properties of the plant. A problem of advanced control schemes with unidentified plants is the difficulty to prove stability. KeywoRDS Construction Machine, Defuzzification, Fan Control, Fan Noise, Feedforward, Fuzzy Control, Fuzzification, Mamdani, Model Predictive Control, MPC, Nonlinear System, PI Control, PID Control
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