針對實(shí)際工業(yè)生產(chǎn)過程中被控對象的時變和純滯后的特點(diǎn)，本文根據(jù)神經(jīng)網(wǎng)絡(luò)具有能
夠充分逼近任意復(fù)雜的非線性關(guān)系和能夠?qū)W習(xí)嚴(yán)重不確定系統(tǒng)的動態(tài)特性，具有適應(yīng)性、智能性較好的特點(diǎn)并結(jié)合灰色預(yù)測控制的超前預(yù)測的特點(diǎn)，提出了基于BP 神經(jīng)網(wǎng)絡(luò)的灰色預(yù)測控制算法。仿真結(jié)果的對比分析表明：本文的控制算法與傳統(tǒng)控制器相比，具有控制簡便、自適應(yīng)性較強(qiáng)等特點(diǎn)，適用于對純滯后和模型時變對象的控制。最后采用xPC 技術(shù)在實(shí)際設(shè)備上進(jìn)行了算法的驗(yàn)證，取得了滿意的效果。
關(guān)鍵詞：BP 神經(jīng)網(wǎng)絡(luò)，灰色預(yù)測，xPC 技術(shù)
Abstract:Aimed at the controlled object’s character of changing with time and time
delay, we based the NN’s merit of highly accord with all kinds of non-liner, learning
the uncertain system, good adaptability, excellent intelligence and join the
characteristic of forecast of the algorithm of grey prediction control , we put
forward the algorithm of grey predict control based on the theory of BP NN.The
comparing and analysing of simulation result shows：compared with traditional
controller, our algorithm become simple and convenient, stronger in adaptability，
it works well in resolving the problem of time delay and model of time varying.
Finally, we do real-time control verification by proposed algorithm in the method
of xPC technology on real device.
Keywords:BP neural network, grey prediction, xPC technology