輸出不可量測非線性系統(tǒng)的神經(jīng)模型參考自適應(yīng)控制
A neural network model reference adaptive control for the nonlinear system with unavailable outputs
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摘要: 該文針對被控對象輸出不可量測的非線性系統(tǒng)，引入一個便于在線辨識的擴展神經(jīng)網(wǎng)絡(luò)模型，提出一種基于前饋-反饋結(jié)構(gòu)的神經(jīng)網(wǎng)絡(luò)模型參考自適應(yīng)控制方法。給出了具有全局收斂性的網(wǎng)絡(luò)訓(xùn)練算法，并分析了控制系統(tǒng)的穩(wěn)定性。仿真結(jié)果表明該控制方法是有效的，而且對網(wǎng)絡(luò)初始權(quán)值的選取及被控對象特性參數(shù)的擾動都具有良好的魯棒性。Abstract: In this paper, by introducing an extended neural network model which can be easily identified on-line, a neural network model reference adaptive control method based on a feedforward-feedback structure is proposed for a class of nonlinear systems whose outputs are not measurable. A training algorithm with global convergence is offered, and the stability of the control system is analyzed. The simulation results show that this method is effective, anrl it has good robustness for both the selection of original network weights and the disturbance of plant parameters.
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李新忠,簡林柯,何鉞,非線性系統(tǒng)的模型參考神經(jīng)網(wǎng)絡(luò)控制,信息與控制,1996,25(6),367-372[2]G. Lightbody, G. W. Irwin, Direct neural model reference adaptive control, IEE Proc. Control Theory Appl., 1995, 142(1), 31-43.[3]S.J. Elliott, P. A. Nelson, Active noise control, IEEE Signal Processing Mag., 1993, 10(4), 12 35.[4]S.D. Snyder, N. Tanaka, Active control of vibration using a neural network, IEEE Trans. on Neural Networks, 1995, 6(4), 819-828.[5]M. Bouchard, B. Paillard, C. T. L. Dinh, Improved training of neural networks for the nonlinear active control of sound and vibration, IEEE Trans. on Neural Networks, 1999, 10(2), 391-401.[6]何玉彬,李新忠,神經(jīng)網(wǎng)絡(luò)控制技術(shù)及其應(yīng)用,北京,科學(xué)出版社,2000,39-48. -
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