混沌系統(tǒng)的動(dòng)態(tài)神經(jīng)網(wǎng)絡(luò)自適應(yīng)控制
Adaptive Control of Unknown Chaotic System via Dynamic Neural Networks
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摘要: 針對混沌系統(tǒng)模型誤差,該文提出一種非線性魯棒自適應(yīng)辨識(shí)和控制新方法,目標(biāo)是通過下面兩個(gè)步驟將混沌系統(tǒng)鎮(zhèn)定到不動(dòng)點(diǎn):首先利用動(dòng)態(tài)神經(jīng)網(wǎng)絡(luò)對系統(tǒng)進(jìn)行辨識(shí),然后在辨識(shí)估計(jì)基礎(chǔ)上設(shè)計(jì)控制器將混沌狀態(tài)引導(dǎo)至期望目標(biāo)位置;并且對系統(tǒng)的穩(wěn)定性能進(jìn)行了嚴(yán)格數(shù)學(xué)分析;Duffing方程的數(shù)值仿真實(shí)驗(yàn)證明了所提出方法的有效性。Abstract: A new robust adaptive identification-based control of chaotic system with uncertain parameters in view of modeling error is proposed in this paper. The objective is to adjust the unknown chaos to a fixed point. It is fulfilled by taking following two steps: a dynamical neural network is used as system identifier, then a controller based on identification estimates is established to direct the chaos states towards desired target. Also, rigorous mathematical proof is given to analyze the stability properties of the system. Finally, the effectiveness of the proposed method is demonstrated by the Duffing equation.
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